Quantum gravitational corrections to particle creation by black holes (Physics Letters B)

This is the published version of our preprint https://arxiv.org/abs/2303.00310.

Quantum gravitational corrections to particle creation by black holes 

X. Calmet, S. Hsu, M. Sebastianutti 

We calculate quantum gravitational corrections to the amplitude for the emission of a Hawking particle by a black hole. We show explicitly how the amplitudes depend on quantum corrections to the exterior metric (quantum hair). This reveals the mechanism by which information escapes the black hole. The quantum state of the black hole is reflected in the quantum state of the exterior metric, which in turn influences the emission of Hawking quanta.

In earlier work we showed that the quantum state of a black hole is reflected in the quantum state of the exterior metric (outside the horizon). This violates classical intuitions, but can be shown explicitly using long wavelength effective field theory.

We calculated examples of small corrections to the external spacetime geometry which are sensitive to the internal BH state. In this paper we show that these corrections in turn affect Hawking radiation amplitudes. 

This means that the Hawking radiation state depends on the internal BH state. At the quantum level the hole is not black! We derive the results using both Hawking's original method and the tunneling method of Parikh and Wilczek.

 

While the focus of the new paper is explicit calculations, the big picture statement is:

The quantum state of the BH is reflected in the quantum state of its external gravitational field, which forms the background where the Hawking radiation originates. Radiation amplitudes are NOT independent of interior state.

Marc Martinez: "Dream Big" and the Golden Age of Bodybuilding — Manifold #32

 

Marc Martinez is the director of Dream Big, a documentary about Gold's Gym and the golden age of bodybuilding in Venice and Santa Monica in the 1970s. 

0:00 Introduction 

1:34 Marc's background in bodybuilding 

5:25 Bodybuilding in 70s Southern California 

25:52 Setting the record straight on steroid use 

33:40 Frank Zane 

38:23 Robby Robinson 

40:20 Butler, Gaines, and Arnold 

42:35 'Dream Big' 

48:07 Pumping Iron 

59:40 Hypersexuality in bodybuilding 

1:10:44 What's next for Marc

References: 

Watch Dream Big on Amazon: https://www.amazon.com/Dream-Big-Ken-Sprague/dp/B0B8ST5LNL/ 

Dream Big documentary: https://dreambigdoc.com/ 

Dream Big trailer: https://www.youtube.com/watch?v=X22ISDn083A 

Pumping Iron: https://www.youtube.com/watch?v=6lCCk6rgn84

Biobank-scale methods and projections for sparse polygenic prediction from machine learning

New paper! 80+ pages of fun :-)

We develop a novel method for projecting AUC and Correlation as a function of data size and characterize the asymptotic limit of performance. For LASSO (compressed sensing) we show that performance metrics and predictor sparsity are in agreement with theoretical predictions from the Donoho-Tanner phase transition. 

Biobank-scale methods and projections for sparse polygenic prediction from machine learning 

https://www.medrxiv.org/content/10.1101/2023.03.06.23286870v1 

Timothy G. Raben, Louis Lello, Erik Widen, Stephen D.H. Hsu  

https://doi.org/10.1101/2023.03.06.23286870 

Abstract In this paper we characterize the performance of linear models trained via widely-used sparse machine learning algorithms. We build polygenic scores and examine performance as a function of training set size, genetic ancestral background, and training method. We show that predictor performance is most strongly dependent on size of training data, with smaller gains from algorithmic improvements. We find that LASSO generally performs as well as the best methods, judged by a variety of metrics. We also investigate performance characteristics of predictors trained on one genetic ancestry group when applied to another. Using LASSO, we develop a novel method for projecting AUC and Correlation as a function of data size (i.e., for new biobanks) and characterize the asymptotic limit of performance. Additionally, for LASSO (compressed sensing) we show that performance metrics and predictor sparsity are in agreement with theoretical predictions from the Donoho-Tanner phase transition. Specifically, a predictor trained in the Taiwan Precision Medicine Initiative for asthma can achieve an AUC of 0.63(0.02) and for height a correlation of 0.648(0.009) for a Taiwanese population. This is above the measured values of 0.61(0.01) and 0.631(0.008), respectively, for UK Biobank trained predictors applied to a European population. 

Figure: Performance in 5 ancestry groups using LASSO, Elastic Net, and PRScs with UKB and 1,000 Genomes LD matrices. Solid bands = predicted performance using All of Us and Taiwan Precision Medicine Initiative datasets.

Prof. Gilles Saint-Paul (Ecole Normale): the Yellow Vests, French Politics, and Hypergamy (Manifold #31)

 

Audio (podcast only)

Transcript 

Gilles Saint-Paul is Professeur à l'Ecole Normale Supérieure. He is a graduate of Ecole Polytechnique in Engineering and received his PhD from MIT in Economics. Gilles and Steve discuss the French elite education system, the Yellow Vest movement, French politics and populism, and Saint-Paul's paper on marriage markets and hypergamy. 

0:00 Introduction 

1:43 Gilles Saint-Paul's background and education 

6:31 French and American elite education - Les Grandes Ecoles 

14:44 The Yellow Vests 

41:46 Mating and Hypergamy 

Links: 

On the Yellow Vest Insurrection 

https://gillessaintpaul.wordpress.com/2018/12/18/on-the-yellow-vest-insurrection/ 

Genes, Legitimacy and Hypergamy: Another Look at the Economics of Marriage https://ideas.repec.org/p/ide/wpaper/9118.html