Wednesday, May 11, 2022

Quantum Hair and Black Hole Information -- Quantum Gravity and All of That seminar series (video)

 

May 5 2022 talk in the international seminar series Quantum Gravity and All of That

The talk is pitched at a slightly more expert audience than previous versions I have given. 

There are interesting comments by and discussions with G. Veneziano, V. Rubakov, Suvrat Raju and others during the seminar. 

The Zoom client on ChromeOS does not allow me to see others in the meeting when I share my slides fullscreen. So at times I was not sure whose questions I was responding to! 


Title: Quantum Hair and Black Hole Information 
Abstract: I discuss recent results concerning the quantum state of the gravitational field of a compact matter source such as a black hole. These results demonstrate the existence of quantum hair, violating the classical No Hair Theorems. I then discuss how this quantum hair affects Hawking radiation, allowing unitary evaporation of black holes. Small corrections to leading order Hawking radiation amplitudes, with weak dependence on the external graviton state, are sufficient to produce a pure final radiation state. The radiation state violates the factorization assumption made in standard formulations of the information paradox. These conclusions are consequences of long wavelength properties of quantum gravity: no special assumptions are made concerning short distance (Planckian) physics.

Thursday, May 05, 2022

Raghuveer Parthasarathy: Four Physical Principles and Biophysics -- Manifold podcast #11

 

Raghu Parthasarathy is the Alec and Kay Keith Professor of Physics at the University of Oregon. His research focuses on biophysics, exploring systems in which the complex interactions between individual components, such as biomolecules or cells, can give rise to simple and robust physical patterns. 

Raghu is the author of a recent popular science book, So Simple a Beginning: How Four Physical Principles Shape Our Living World. 


Steve and Raghu discuss: 

0:00 Introduction 

1:34 Early life, transition from Physics to Biophysics 

20:15 So Simple a Beginning: discussion of the Four Physical Principles in the title, which govern biological systems 

26:06 DNA prediction 

37:46 Machine learning / causality in science 

46:23 Scaling (the fourth physical principle) 

54:12 Who the book is for and what high schoolers are learning in their bio and physics classes 

1:05:41 Science funding, grants, running a research lab 

1:09:12 Scientific careers and radical sub-optimality of the existing system 



Resources: 


Raghuveer Parthasarathy's lab at the University of Oregon - https://pages.uoregon.edu/raghu/ 
 
Raghuveer Parthasarathy's blog the Eighteenth Elephant - https://eighteenthelephant.com/


Added from comments:
key holez • 2 days ago 
It was a fascinating episode, and I immediately went out and ordered the book! One question that came to mind: given how much of the human genome is dedicated to complex regulatory mechanisms and not proteins as such, it seems unintuitive to me that so much of heritability seems to be additive. I would have thought that in a system with lots of complicated,messy on/off switches, small genetic differences would often lead to large phenotype differences -- but if what I've heard about polygenic prediction is right, then, empirically, assuming everything is linear seems to work just fine (outside of rare variants, maybe). Is there a clear explanation for how complex feedback patterns give rise to linearity in the end? Is it just another manifestation of the central limit theorem...?
steve hsu 
This is an active area of research. It is somewhat surprising even to me how well linearity / additivity holds in human genetics. Searches for non-linear effects on complex traits have been largely unsuccessful -- i.e., in the sense that most of the variance seems to be controlled by additive effects. By now this has been investigated for large numbers of traits including major diseases, quantitive traits such as blood biomarkers, height, cognitive ability, etc. 
One possible explanation is that because humans are so similar to each other, and have passed through tight evolutionary bottlenecks, *individual differences* between humans are mainly due to small additive effects, located both in regulatory and coding regions. 
To genetically edit a human into a frog presumably requires many changes in loci with big nonlinear effects. However, it may be the case that almost all such genetic variants are *fixed* in the human population: what makes two individuals different from each other is mainly small additive effects. 
Zooming out slightly, the implications for human genetic engineering are very positive. Vast pools of additive variance means that multiplex gene editing will not be impossibly hard...
This topic is discussed further in the review article: https://arxiv.org/abs/2101.05870

Tuesday, May 03, 2022

How We Learned, Then Forgot, About Human Intelligence... And Witnessing the Live Breakdown of Academia (podcast interview with Cactus Chu)

This is a long interview I did recently with Cactus Chu, a math prodigy turned political theorist and podcaster. (Unfortunately I can't embed the podcast here.)


Timestamps: 
3:24 Interview Starts  
15:49 Cactus' Experience with High Math People 
19:49 High School Sports 
21:26 Comparison to Intelligence 
26:29 Is Lack of Understanding due to Denial or Ignorance? 
29:29 The Past and Present of Selection in Academia 
37:02 How Universities Look from the Inside 
44:19 Informal Networks Replacing Credentials 
48:37 Capture of Research Positions 
50:24 Progressivism as Demagoguery Against the Self-Made 
55:31 Innumeracy is Common 
1:06:53 Understanding Innumerate People 
1:13:53 Skill Alignment at Cactus' High School 
1:18:12 Free Speech in Academia 
1:21:00 You Shouldn't Fire Exceptional People 
1:23:03 The Anti-Excellence Progressives 
1:28:42 Rawls, Nozick, and Technology 
1:34:00 Freedom = Variance = Inequality 
1:37:58 Dating Apps 
1:41:27 Jumping Into Social Problems From a Technical Background 
1:41:50 Steve's High School Pranks 
1:46:43 996 and Cactus' High School 
1:50:26 The Vietnam War and Social Change 
1:53:07 Are Podcasts the Future? 
1:59:37 The Power of New Things 
2:02:56 The Birth of Twitter 
2:07:27 Selection Creates Quality 
2:10:21 Incentives of University Departments 
2:16:29 Woke Bureaucrats 
2:27:59 Building a New University 
2:30:42 What needs more order? 
2:31:56 What needs more chaos?

An automated (i.e., imperfect) transcript of our discussion.

Here's an excerpt from the podcast:

Sunday, May 01, 2022

Complex Trait Prediction: Methods and Protocols (Springer 2022)


My research group contributed a chapter to this new book on Complex Trait Prediction (see below). The book is somewhat unique, covering applications to humans, plants, and animals all in a single volume. 
Complex Trait Prediction: Methods and Protocols (Springer Nature) 
Editors: 
Nourollah Ahmadi and Jérôme Bartholomé 
CIRAD, UMR AGAP Institut, Montpellier, France

 

About this book 
This volume explores the conceptual framework and the practical issues related to genomic prediction of complex traits in human medicine and in animal and plant breeding. The book is organized into five parts. Part One reminds molecular genetics approaches intending to predict phenotypic variations. Part Two presents the principles of genomic prediction of complex traits, and reviews factors that affect its reliability. Part Three describes genomic prediction methods, including machine-learning approaches, accounting for different degree of biological complexity, and reviews the associated computer-packages. Part Four reports on emerging trends such as phenomic prediction and incorporation into genomic prediction models of “omics” data and crop growth models. Part Five is dedicated to lessons learned from case studies in the fields of human health and animal and plant breeding, and to methods for analysis of the economic effectiveness of genomic prediction. 
Written in the highly successful Methods in Molecular Biology series format, the book provides theoretical bases and practical guidelines for an informed decision making of practitioners and identifies pertinent routes for further methodological researches. Cutting-edge and thorough, Complex Trait Predictions: Methods and Protocols is a valuable resource for scientists and researchers who are interested in learning more about this important and developing field.
Our article (pp 421–446):
From Genotype to Phenotype: Polygenic Prediction of Complex Human Traits 
T. Raben, L. Lello, E.Widen, and S. Hsu 
Decoding the genome confers the capability to predict characteristics of the organism (phenotype) from DNA (genotype). We describe the present status and future prospects of genomic prediction of complex traits in humans. Some highly heritable complex phenotypes such as height and other quantitative traits can already be predicted with reasonable accuracy from DNA alone. For many diseases, including important common conditions such as coronary artery disease, breast cancer, type I and II diabetes, individuals with outlier polygenic scores (e.g., top few percent) have been shown to have 5 or even 10 times higher risk than average. Several psychiatric conditions such as schizophrenia and autism also fall into this category. We discuss related topics such as the genetic architecture of complex traits, sibling validation of polygenic scores, and applications to adult health, in vitro fertilization (embryo selection), and genetic engineering.
Ungated arXiv version.

Previous discussion: 




See also Big Chickens.

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