Posts Tagged ‘maybel2e’

Why Polling on The 2020 Presidential Election Missed the Mark – The New York Times

November 14, 2020


Senator Susan Collins did not lead in a single publicly released poll during the final four months of her re-election campaign in Maine. But Ms. Collins, a Republican, won the election comfortably.

Senator Thom Tillis, a North Carolina Republican, trailed in almost every poll conducted in his race. He won, too.

And most polls underestimated President Trump’s strength, in Iowa, Florida, Michigan, Texas, Wisconsin and elsewhere. Instead of winning a landslide, as the polls suggested, Joseph R. Biden Jr. beat Mr. Trump by less than two percentage points in the states that decided the election.

This year’s misleading polls had real-world effects, for both political parties. The Trump campaign pulled back from campaigning in Michigan and Wisconsin, reducing visits and advertising, and lost both only narrowly. In Arizona, a Republican strategist who worked on Senator Martha McSally’s re-election campaign said that public polling showing her far behind “probably cost us $4 or $5 million” in donations. Ms. McSally lost to Mark Kelly by less than three percentage points.

A separate set of changes may involve how the media present polling and whether publications spend as much money on it in the future. “The media that sponsor polls should demand better results because their reputations are on the line,” James A. Baker III, the former secretary of state, wrote in The Wall Street Journal this week.


By David Leonhardt
Nov. 12, 2020

Good Grief, the Pollsters Got It Wrong – WSJ

November 14, 2020

‘I Don’t Have a Happy Ending’: A Pollster on What Went Wrong – The New York Times

November 14, 2020

By Lisa Lerer

Nov. 11, 2020

Hi. Welcome to On Politics, your guide to the day in national politics. I’m Lisa Lerer, your host.
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Why Revel Suspended Moped Service in N.Y.C. – The New York Times

August 21, 2020


August 6, 2020

Why a Data Breach at a Genealogy Site Has Privacy Experts Worried

August 2, 2020

Opinion | You Are Now Remotely Controlled – The New York Times

January 25, 2020

Deep learning and process understanding for data-driven Earth system science | Nature

March 4, 2019
Perspective | Published: 13 February 2019
Deep learning and process understanding for data-driven Earth system science Markus Reichstein, Gustau Camps-Valls, Bjorn Stevens, Martin Jung, Joachim Denzler, Nuno Carvalhais & Prabhat
Nature volume 566, pages195–204 (2019)

Figure 3 presents a system-modelling view that seeks to integrate machine learning into a system model. As an alternative perspective, system knowledge can be integrated into a machine learning frame- work. This may include design of the network architecture36,79, physical constraints in the cost function for optimization58, or expansion of the training dataset for undersampled domains (that is, physically based data augmentation)80.

Surrogate modelling or emulation
See Fig. 3 (circle 5). Emulation of the full (or specific parts of) a physical model can be useful for computational efficiency and tractability rea- sons. Machine learning emulators, once trained, can achieve simulations orders of magnitude faster than the original physical model without sacrificing much accuracy. This allows for fast sensitivity analysis, model parameter calibration, and derivation of confidence intervals for the estimates.

(2) Replacing a ‘physical’ sub-model with a machine learning model
See Fig. 3 (circle 2). If formulations of a submodel are of semi-empirical nature, where the functional form has little theoretical basis (for example, biological processes), this submodel can be replaced by a machine learning model if a sufficient number of observations are available. This leads to a hybrid model, which combines the strengths of physical modelling (theoretical foundations, interpretable compartments) and machine learning (data-adaptiveness).

Integration with physical modelling
Historically, physical modelling and machine learning have often been treated as two different fields with very different scientific paradigms (theory-driven versus data-driven). Yet, in fact these approaches are complementary, with physical approaches in principle being directly interpretable and offering the potential of extrapolation beyond observed conditions, whereas data-driven approaches are highly flexible in adapting to data and are amenable to finding unexpected patterns (surprises).

A success story in the geosciences is weather
prediction, which has greatly improved through the integration of better theory, increased computational power, and established observational systems, which allow for the assimilation of large amounts of data into the modelling system2
. Nevertheless, we can accurately predict the evolution
of the weather on a timescale of days, not months.

# REFs that I liked
ref 80

ref 57
Karpatne, A. et al. Theory-guided data science: a new paradigm for scientific discovery from data. IEEE Trans. Knowl. Data Eng. 29, 2318–2331 (2017).

# some key BULLETS

• Complementarity of physical & ML approaches
–“Physical approaches in principle being directly interpretable and offering the potential of extrapolation beyond observed conditions, whereas data-driven approaches are highly flexible in adapting to data”

• Hybrid #1: Physical knowledge can be integrated into ML framework –Network architecture
–Physical constraints in the cost function
–Expansion of the training dataset for undersampled domains (ie physically based data augmentation)

• Hybrid #2: ML into physical – eg Emulation of specific parts of a physical for computational efficiency

Artificial intelligence alone won’t solve the complexity of Earth sciences

March 4, 2019

More New Yorkers Opting for Life in the Bike Lane – The New York Times

August 2, 2017

More NYers Opting for…the Bike Lane – 450k trips/day v. 170k in ’05 But there’s #bikelash from walkers & drivers