**phase-transition**20

An adaptability limit to climate change due to heat stress

august 2018 by nhaliday

Despite the uncertainty in future climate-change impacts, it is often assumed that humans would be able to adapt to any possible warming. Here we argue that heat stress imposes a robust upper limit to such adaptation. Peak heat stress, quantified by the wet-bulb temperature TW, is surprisingly similar across diverse climates today. TW never exceeds 31 °C. Any exceedence of 35 °C for extended periods should induce hyperthermia in humans and other mammals, as dissipation of metabolic heat becomes impossible. While this never happens now, it would begin to occur with global-mean warming of about 7 °C, calling the habitability of some regions into question. With 11–12 °C warming, such regions would spread to encompass the majority of the human population as currently distributed. Eventual warmings of 12 °C are possible from fossil fuel burning. One implication is that recent estimates of the costs of unmitigated climate change are too low unless the range of possible warming can somehow be narrowed. Heat stress also may help explain trends in the mammalian fossil record.

Trajectories of the Earth System in the Anthropocene: http://www.pnas.org/content/early/2018/07/31/1810141115

We explore the risk that self-reinforcing feedbacks could push the Earth System toward a planetary threshold that, if crossed, could prevent stabilization of the climate at intermediate temperature rises and cause continued warming on a “Hothouse Earth” pathway even as human emissions are reduced. Crossing the threshold would lead to a much higher global average temperature than any interglacial in the past 1.2 million years and to sea levels significantly higher than at any time in the Holocene. We examine the evidence that such a threshold might exist and where it might be.

study
org:nat
environment
climate-change
humanity
existence
risk
futurism
estimate
physics
thermo
prediction
temperature
nature
walls
civilization
flexibility
rigidity
embodied
multi
manifolds
plots
equilibrium
phase-transition
oscillation
comparison
complex-systems
earth
Trajectories of the Earth System in the Anthropocene: http://www.pnas.org/content/early/2018/07/31/1810141115

We explore the risk that self-reinforcing feedbacks could push the Earth System toward a planetary threshold that, if crossed, could prevent stabilization of the climate at intermediate temperature rises and cause continued warming on a “Hothouse Earth” pathway even as human emissions are reduced. Crossing the threshold would lead to a much higher global average temperature than any interglacial in the past 1.2 million years and to sea levels significantly higher than at any time in the Holocene. We examine the evidence that such a threshold might exist and where it might be.

august 2018 by nhaliday

Accurate Genomic Prediction Of Human Height | bioRxiv

september 2017 by nhaliday

Stephen Hsu's compressed sensing application paper

We construct genomic predictors for heritable and extremely complex human quantitative traits (height, heel bone density, and educational attainment) using modern methods in high dimensional statistics (i.e., machine learning). Replication tests show that these predictors capture, respectively, ~40, 20, and 9 percent of total variance for the three traits. For example, predicted heights correlate ~0.65 with actual height; actual heights of most individuals in validation samples are within a few cm of the prediction.

https://infoproc.blogspot.com/2017/09/accurate-genomic-prediction-of-human.html

http://infoproc.blogspot.com/2017/11/23andme.html

I'm in Mountain View to give a talk at 23andMe. Their latest funding round was $250M on a (reported) valuation of $1.5B. If I just add up the Crunchbase numbers it looks like almost half a billion invested at this point...

Slides: Genomic Prediction of Complex Traits

Here's how people + robots handle your spit sample to produce a SNP genotype:

https://drive.google.com/file/d/1e_zuIPJr1hgQupYAxkcbgEVxmrDHAYRj/view

study
bio
preprint
GWAS
state-of-art
embodied
genetics
genomics
compressed-sensing
high-dimension
machine-learning
missing-heritability
hsu
scitariat
education
🌞
frontier
britain
regression
data
visualization
correlation
phase-transition
multi
commentary
summary
pdf
slides
brands
skunkworks
hard-tech
presentation
talks
methodology
intricacy
bioinformatics
scaling-up
stat-power
sparsity
norms
nibble
speedometer
stats
linear-models
2017
biodet
We construct genomic predictors for heritable and extremely complex human quantitative traits (height, heel bone density, and educational attainment) using modern methods in high dimensional statistics (i.e., machine learning). Replication tests show that these predictors capture, respectively, ~40, 20, and 9 percent of total variance for the three traits. For example, predicted heights correlate ~0.65 with actual height; actual heights of most individuals in validation samples are within a few cm of the prediction.

https://infoproc.blogspot.com/2017/09/accurate-genomic-prediction-of-human.html

http://infoproc.blogspot.com/2017/11/23andme.html

I'm in Mountain View to give a talk at 23andMe. Their latest funding round was $250M on a (reported) valuation of $1.5B. If I just add up the Crunchbase numbers it looks like almost half a billion invested at this point...

Slides: Genomic Prediction of Complex Traits

Here's how people + robots handle your spit sample to produce a SNP genotype:

https://drive.google.com/file/d/1e_zuIPJr1hgQupYAxkcbgEVxmrDHAYRj/view

september 2017 by nhaliday

gravity - Gravitational collapse and free fall time (spherical, pressure-free) - Physics Stack Exchange

august 2017 by nhaliday

the parenthetical regarding Gauss's law just involves noting a shell of radius r + symmetry (so single parameter determines field along shell)

nibble
q-n-a
overflow
physics
mechanics
gravity
tidbits
time
phase-transition
symmetry
differential
identity
dynamical
august 2017 by nhaliday

Tidal locking - Wikipedia

august 2017 by nhaliday

The Moon's rotation and orbital periods are tidally locked with each other, so no matter when the Moon is observed from Earth the same hemisphere of the Moon is always seen. The far side of the Moon was not seen until 1959, when photographs of most of the far side were transmitted from the Soviet spacecraft Luna 3.[12]

never actually thought about this

nibble
wiki
reference
space
mechanics
gravity
navigation
explanation
flux-stasis
marginal
volo-avolo
spatial
direction
invariance
physics
flexibility
rigidity
time
identity
phase-transition
being-becoming
never actually thought about this

august 2017 by nhaliday

Roche limit - Wikipedia

july 2017 by nhaliday

In celestial mechanics, the Roche limit (pronounced /ʁɔʃ/) or Roche radius, is the distance within which a celestial body, held together only by its own gravity, will disintegrate due to a second celestial body's tidal forces exceeding the first body's gravitational self-attraction.[1] Inside the Roche limit, orbiting material disperses and forms rings whereas outside the limit material tends to coalesce. The term is named after Édouard Roche, who is the French astronomer who first calculated this theoretical limit in 1848.[2]

space
physics
gravity
mechanics
wiki
reference
nibble
phase-transition
proofs
tidbits
identity
marginal
july 2017 by nhaliday

chemistry - Is it possible to make an alloy that melts at low temperatures and solidifies at high temperatures? - Worldbuilding Stack Exchange

may 2017 by nhaliday

I'm sure this is outside of the range of temperatures you were interested in, but in the spirit of "truth is stranger than fiction," Helium-3 actually does this.

nibble
q-n-a
stackex
trivia
chemistry
physics
thermo
state
temperature
weird
phase-transition
may 2017 by nhaliday

co.combinatorics - Does $|A+A|$ concentrate near its mean? - MathOverflow

february 2017 by nhaliday

for p = ω(N^-1/2), |A+A| = N whp, for p = o(N^-1/2), |A+A| ~ |A|^2/2

q-n-a
overflow
nibble
math
probability
math.CO
additive-combo
tidbits
expert
concentration-of-measure
phase-transition
expert-experience
february 2017 by nhaliday

Superconcentration and Related Topics

february 2017 by nhaliday

when Var X_n = o(n) instead of Var X_n = O(n)

pdf
lecture-notes
math
probability
boolean-analysis
concentration-of-measure
limits
magnitude
concept
yoga
👳
unit
discrete
phase-transition
stat-mech
percolation
ising
p:*
quixotic
february 2017 by nhaliday

It Was You Who Made My Blue Eyes Blue | Slate Star Codex

february 2017 by nhaliday

https://terrytao.wordpress.com/2011/04/07/the-blue-eyed-islanders-puzzle-repost/

https://www.psychologicalscience.org/observer/uncommon-insights-into-common-knowledge

yvain
ssc
gowers
mathtariat
pinker
fiction
parable
gedanken
exposition
logic
rationality
epistemic
anthropology
essay
multi
puzzles
nibble
org:bleg
phase-transition
info-dynamics
ratty
https://www.psychologicalscience.org/observer/uncommon-insights-into-common-knowledge

february 2017 by nhaliday

[1511.02476] Statistical physics of inference: Thresholds and algorithms

december 2016 by arsyed

"Many questions of fundamental interest in todays science can be formulated as inference problems: Some partial, or noisy, observations are performed over a set of variables and the goal is to recover, or infer, the values of the variables based on the indirect information contained in the measurements. For such problems, the central scientific questions are: Under what conditions is the information contained in the measurements sufficient for a satisfactory inference to be possible? What are the most efficient algorithms for this task? A growing body of work has shown that often we can understand and locate these fundamental barriers by thinking of them as phase transitions in the sense of statistical physics. Moreover, it turned out that we can use the gained physical insight to develop new promising algorithms. Connection between inference and statistical physics is currently witnessing an impressive renaissance and we review here the current state-of-the-art, with a pedagogical focus on the Ising model which formulated as an inference problem we call the planted spin glass. In terms of applications we review two classes of problems: (i) inference of clusters on graphs and networks, with community detection as a special case and (ii) estimating a signal from its noisy linear measurements, with compressed sensing as a case of sparse estimation. Our goal is to provide a pedagogical review for researchers in physics and other fields interested in this fascinating topic."

papers
statistical-physics
statistics
phase-transition
machine-learning
via:rvenkat
surveys
december 2016 by arsyed

Information Processing: Search results for compressed sensing

november 2016 by nhaliday

https://www.unz.com/jthompson/the-hsu-boundary/

http://infoproc.blogspot.com/2017/09/phase-transitions-and-genomic.html

Added: Here are comments from "Donoho-Student":

Donoho-Student says:

September 14, 2017 at 8:27 pm GMT • 100 Words

The Donoho-Tanner transition describes the noise-free (h2=1) case, which has a direct analog in the geometry of polytopes.

The n = 30s result from Hsu et al. (specifically the value of the coefficient, 30, when p is the appropriate number of SNPs on an array and h2 = 0.5) is obtained via simulation using actual genome matrices, and is original to them. (There is no simple formula that gives this number.) The D-T transition had only been established in the past for certain classes of matrices, like random matrices with specific distributions. Those results cannot be immediately applied to genomes.

The estimate that s is (order of magnitude) 10k is also a key input.

I think Hsu refers to n = 1 million instead of 30 * 10k = 300k because the effective SNP heritability of IQ might be less than h2 = 0.5 — there is noise in the phenotype measurement, etc.

Donoho-Student says:

September 15, 2017 at 11:27 am GMT • 200 Words

Lasso is a common statistical method but most people who use it are not familiar with the mathematical theorems from compressed sensing. These results give performance guarantees and describe phase transition behavior, but because they are rigorous theorems they only apply to specific classes of sensor matrices, such as simple random matrices. Genomes have correlation structure, so the theorems do not directly apply to the real world case of interest, as is often true.

What the Hsu paper shows is that the exact D-T phase transition appears in the noiseless (h2 = 1) problem using genome matrices, and a smoothed version appears in the problem with realistic h2. These are new results, as is the prediction for how much data is required to cross the boundary. I don’t think most gwas people are familiar with these results. If they did understand the results they would fund/design adequately powered studies capable of solving lots of complex phenotypes, medical conditions as well as IQ, that have significant h2.

Most people who use lasso, as opposed to people who prove theorems, are not even aware of the D-T transition. Even most people who prove theorems have followed the Candes-Tao line of attack (restricted isometry property) and don’t think much about D-T. Although D eventually proved some things about the phase transition using high dimensional geometry, it was initially discovered via simulation using simple random matrices.

hsu
list
stream
genomics
genetics
concept
stats
methodology
scaling-up
scitariat
sparsity
regression
biodet
bioinformatics
norms
nibble
compressed-sensing
applications
search
ideas
multi
albion
behavioral-gen
iq
state-of-art
commentary
explanation
phase-transition
measurement
volo-avolo
regularization
levers
novelty
the-trenches
liner-notes
clarity
random-matrices
innovation
high-dimension
linear-models
http://infoproc.blogspot.com/2017/09/phase-transitions-and-genomic.html

Added: Here are comments from "Donoho-Student":

Donoho-Student says:

September 14, 2017 at 8:27 pm GMT • 100 Words

The Donoho-Tanner transition describes the noise-free (h2=1) case, which has a direct analog in the geometry of polytopes.

The n = 30s result from Hsu et al. (specifically the value of the coefficient, 30, when p is the appropriate number of SNPs on an array and h2 = 0.5) is obtained via simulation using actual genome matrices, and is original to them. (There is no simple formula that gives this number.) The D-T transition had only been established in the past for certain classes of matrices, like random matrices with specific distributions. Those results cannot be immediately applied to genomes.

The estimate that s is (order of magnitude) 10k is also a key input.

I think Hsu refers to n = 1 million instead of 30 * 10k = 300k because the effective SNP heritability of IQ might be less than h2 = 0.5 — there is noise in the phenotype measurement, etc.

Donoho-Student says:

September 15, 2017 at 11:27 am GMT • 200 Words

Lasso is a common statistical method but most people who use it are not familiar with the mathematical theorems from compressed sensing. These results give performance guarantees and describe phase transition behavior, but because they are rigorous theorems they only apply to specific classes of sensor matrices, such as simple random matrices. Genomes have correlation structure, so the theorems do not directly apply to the real world case of interest, as is often true.

What the Hsu paper shows is that the exact D-T phase transition appears in the noiseless (h2 = 1) problem using genome matrices, and a smoothed version appears in the problem with realistic h2. These are new results, as is the prediction for how much data is required to cross the boundary. I don’t think most gwas people are familiar with these results. If they did understand the results they would fund/design adequately powered studies capable of solving lots of complex phenotypes, medical conditions as well as IQ, that have significant h2.

Most people who use lasso, as opposed to people who prove theorems, are not even aware of the D-T transition. Even most people who prove theorems have followed the Candes-Tao line of attack (restricted isometry property) and don’t think much about D-T. Although D eventually proved some things about the phase transition using high dimensional geometry, it was initially discovered via simulation using simple random matrices.

november 2016 by nhaliday

CS294 MARKOV CHAIN MONTE CARLO: FOUNDATIONS & APPLICATIONS, FALL 2009

course berkeley tcs expert yoga 👳 lecture-notes topics markov monte-carlo sampling ergodic unit mixing counting approximation math.FA phase-transition stat-mech spectral graphs graph-theory random ising p:someday expert-experience quixotic

august 2016 by nhaliday

course berkeley tcs expert yoga 👳 lecture-notes topics markov monte-carlo sampling ergodic unit mixing counting approximation math.FA phase-transition stat-mech spectral graphs graph-theory random ising p:someday expert-experience quixotic

august 2016 by nhaliday

[1007.3411] The phase diagram of random Boolean networks with nested canalizing functions

july 2010 by Vaguery

Frankly, I've alway thought this, especially after some early "confusing" experiments that never got published because they were part of my first Ph.D. thesis research: "…We argue that the presence of only the frozen phase in the work of Kauffman et al. was due simply to the specific parametrization used, and is not an inherent feature of this class of functions. However, these networks are significantly more stable than the variants where all possible Boolean functions are allowed."

complexology
edge-of-chaos
models-and-modes
network-theory
Stuart-Kauffman
simulation
phase-transition
july 2010 by Vaguery

[1005.5566] Defects and multistability in eutectic solidification patterns

june 2010 by Vaguery

"We use three-dimensional phase-field simulations to investigate the dynamics of the two-phase composite patterns formed upon during solidification of eutectic alloys. Besides the spatially periodic lamellar and rod patterns that have been widely studied, we find that there is a large number of additional steady-state patterns which exhibit stable defects. The defect density can be so high that the pattern is completely disordered, and that the distinction between lamellar and rod patterns is blurred. As a consequence, the transition from lamellae to rods is not sharp, but extends over a finite range of compositions and exhibits strong hysteresis. Our findings are in good agreement with experiments."

materials-science
metallurgy
simulation
phase-transition
alloys
mixtures
solid-statie-physics
condensed-matter
june 2010 by Vaguery

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