Field-aware Calibration: A Simple and Empirically Strong Method for Reliable Probabilistic Predictions

It is often observed that the probabilistic predictions given by a machine learning model can disagree with averaged actual outcomes on specific subsets of data, which is also known as the issue of miscalibration. It is responsible for the unreliability of practical machine learning systems. For example, in online advertising, an ad can receive a click-through rate prediction of 0.1 over some population of users where its actual click rate is 0.15. In such cases, the probabilistic predictions have to be fixed before the system can be deployed. In this paper, we first introduce a new evaluation metric named field-level calibration error that measures the bias in predictions over the sensitive input field that the decision-maker concerns. We show that existing post-hoc calibration methods have limited improvements in the new field-level metric and other non-calibration metrics such as the AUC score. To this end, we propose Neural Calibration, a simple yet powerful post-hoc calibration method that learns to calibrate by making full use of the field-aware information over the validation set. We present extensive experiments on five large-scale datasets. The results showed that Neural Calibration significantly improves against uncalibrated predictions in common metrics such as the negative log-likelihood, Brier score and AUC, as well as the proposed field-level calibration error.Comment: WWW 202

On Coalescence Analysis Using Genealogy Rooted Trees

DNA sequence data are now being used to study the ancestral history of human population. The existing methods for such coalescence inference use recursion formula to compute the data probabilities. These methods are useful in practical applications, but computationally complicated. Here we first investigate the asymptotic behavior of such inference; results indicate that, broadly, the estimated coalescent time will be consistent to a finite limit. Then we study a relatively simple computation method for this analysis and illustrate how to use it

Study on the Thermal Stress Distribution of Crystalline Silicon Solar Cells in BIPV

AbstractThe working temperature of BIPV modules is high than ground-mounted PV. Based on the theory of material mechanics and thermal stress analysis, the stress distribution of metallization interconnects system for crystalline silicon solar module in BIPV was studied for the first time. The shear stress and normal stress distribution of soldered structure for crystalline silicon solar cell under the thermal field were discussed. And the results show the stress distribution is not simply linear relationship as some results found. But there is a stress concentration at the edge, which was considered as the true reason that caused V-notch at the edge of soldered solar cell. The conclusions we got in this paper provide a theoretical basis for reliability of c-Si BIPV modules

Nanodelivery of a functional membrane receptor to manipulate cellular phenotype.

Modification of membrane receptor makeup is one of the most efficient ways to control input-output signals but is usually achieved by expressing DNA or RNA-encoded proteins or by using other genome-editing methods, which can be technically challenging and produce unwanted side effects. Here we develop and validate a nanodelivery approach to transfer in vitro synthesized, functional membrane receptors into the plasma membrane of living cells. Using β2-adrenergic receptor (β2AR), a prototypical G-protein coupled receptor, as an example, we demonstrated efficient incorporation of a full-length β2AR into a variety of mammalian cells, which imparts pharmacologic control over cellular signaling and affects cellular phenotype in an ex-vivo wound-healing model. Our approach for nanodelivery of functional membrane receptors expands the current toolkit for DNA and RNA-free manipulation of cellular function. We expect this approach to be readily applicable to the synthesis and nanodelivery of other types of GPCRs and membrane receptors, opening new doors for therapeutic development at the intersection between synthetic biology and nanomedicine