High failure rates and low average returns suggest that too many people may be entering markets. Thus, anticipating how one will perform in the market is a fundamental component of the decision to start a business. Using a large sample obtained from population surveys conducted in 18 countries, we study what variables are significantly associated with the decision to start a business. We find strong evidence that subjective, and often biased, perceptions have a crucial impact on new business creation across all countries in our sample. The strongest cross-national covariate of an individual's entrepreneurial propensity is shown to be whether the person believes to have the sufficient skills, knowledge and ability to start a business. In addition, we find a significant negative correlation between this reported level of entrepreneurial confidence and the approximated survival chances of nascent entrepreneurs across countries. Our results suggest that some countries exhibit relatively high rates of start-up activity because their inhabitants are more (over)confident than in other countries.

LD Score Regression Distinguishes Confounding from Polygenicity in Genome-Wide Association Studies

We conducted a genome-wide association study (GWAS) on income among individuals of European descent and leveraged the results to investigate the socio-economic health gradient (N=668,288). We found 162 genomic loci associated with a common genetic factor underlying various income measures, all with small effect sizes. Our GWAS-derived polygenic index captures 1 - 4% of income variance, with only one-fourth attributed to direct genetic effects. A phenome-wide association study using this polygenic index showed reduced risks for a broad spectrum of diseases, including hypertension, obesity, type 2 diabetes, coronary atherosclerosis, depression, asthma, and back pain. The income factor showed a substantial genetic correlation (0.92, s.e. = .006) with educational attainment (EA). Accounting for EA's genetic overlap with income revealed that the remaining genetic signal for higher income related to better mental health but reduced physical health benefits and increased participation in risky behaviours such as drinking and smoking.

Associations between common genetic variants and income provide insights about the socioeconomic health gradient

Not Exploring Lupus Clouds

The BOLT-LMM software package computes statistics for association between phenotype and genotypes using a linear mixed model (LMM) [1]. By default, BOLT-LMM assumes a Bayesian mixture-of-normals prior for the random effect attributed to SNPs other than the one being tested. This model generalizes the standard "infinitesimal" mixed model used by existing mixed model association methods (e.g., EMMAX [2], FaST-LMM [3-6], GEMMA [7], GRAMMAR-Gamma [8], GCTA-LOCO [9]), providing an opportunity for increased power to detect associations while controlling false positives. Additionally, BOLT-LMM applies algorithmic advances to compute association statistics much faster than existing methods, both when using the Bayesian mixture model and when specialized to standard mixed model association.

BOLT-LMM v1.0 User Manual

Semantic word spaces have been very useful but cannot express the meaning of longer phrases in a principled way. Further progress towards understanding compositionality in tasks such as sentiment detection requires richer supervised training and evaluation resources and more powerful models of composition. To remedy this, we introduce a Sentiment Treebank. It includes fine-grained sentiment labels for 215,154 phrases in the parse trees of 11,855 sentences and presents new challenges for sentiment compositionality. To address them, we introduce the Recursive Neural Tensor Network. When trained on the new treebank, this model outperforms all previous methods on several metrics. It pushes the state of the art in single sentence positive/negative classification from 80% up to 85.4%. The accuracy of predicting fine-grained sentiment labels for all phrases reaches 80.7%, an improvement of 9.7% over bag-of-features baselines. Lastly, it is the only model that can accurately capture the effects of negation and its scope at various tree levels for both positive and negative phrases.

Recursive Deep Models for Semantic Compositionality
Over a Sentiment Treebank

First published dummy pdfs

High failure rates and low average returns suggest that too many people may be entering markets. Thus, anticipating how one will perform in the market is a fundamental component of the decision to start a business. Using a large sample obtained from population surveys conducted in 18 countries, we study what variables are significantly associated with the decision to start a business. We find strong evidence that subjective, and often biased, perceptions have a crucial impact on new business creation across all countries in our sample. The strongest cross-national covariate of an individual's entrepreneurial propensity is shown to be whether the person believes to have the sufficient skills, knowledge and ability to start a business. In addition, we find a significant negative correlation between this reported level of entrepreneurial confidence and the approximated survival chances of nascent entrepreneurs across countries. Our results suggest that some countries exhibit relatively high rates of start-up activity because their inhabitants are more (over)confident than in other countries."