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Poetry is just the evidence of life. If your life is burning well, poetry is just the ashLeonard Cohenburn somethingmore quotes

quotes: fun


In Silico Flurries: Computing a world of snow. Scientific American. 23 December 2017


art + literature

daily quotation server archives

In the late 90’s I started (a good decade for starts) a daily quotation server project at www.quoteserver.ca. The domain is now defunct—some pages are partially viewable at the Way Back Machine.

Below is the list of quotes I had collected by the end of the life of the project. Most are about love—duh—and a few are jolly jests from funny trenches. You know, that place where mustard gas makes your eyes water.

The quotes weren’t scraped from quote archives—each is meaningful and hand-picked.

the quote archive

And now for full list of 1,600 other things worth reading. Such as everything Dorothy Parker has written and ... yes, even the Pinky and Brain quotes, which are a special kind of special.

Quote collections about love, heart, desire, life, death, god, mind, science.

Feeling lucky? Read 10 random quotes. Well, will you, punk?

Quotes about mind

21
Shall any gazer see with mortal eyes,
Or any seeker know by mortal mind?
Veil after veil will lift — but there must be
Veil upon veil behind.
Sir Edwin Arnold
The Light of Asia, VIII
36
The mind has a thousand eyes,
And the heart but one;
Yet the light of a whole life dies,
When love is done.
Francis Bourdillon
Light
166
I don’t mind sleeping on an empty stomach provided it isn’t my own.
Philip J. Simborg
286
All life is a struggle in the dark ... This dread and
darkness of the mind cannot be dispelled by the
sunbeams, the shining shafts of day, but only by
an understanding of the outward form and inner
workings of nature. And now to business,
I will explain ...
Lucretius
On the Nature of the Universe
345
Serious error.
All shortcuts have disappeared.
Screen. Mind. Both are blank.
A Haiku computer error message.
390
Friendship is almost always the union of a part of one mind with a part of another; people are friends in spots.
George Santayana
414
And, while with silent, lifting mind I’ve trod
The high untrespassed sanctity of space,
Put out my hand, and touched the face of God.
John Gillespie Magee, Jr.
437
A mind is like a parachute; it only works when it is open.
Sir James Dewar
444
Tact is the ability to tell a man he has an open mind when he has a hole in his head.
450
Get your mind out of the gutter—it’s blocking my view.
488
In the beginner’s mind there are many possibilities, but in the expert’s mind there are a few.
Shunryu Suzuki
612
There is a lady sweet and kind,
Was never a face so pleased my mind;
I did but see her passing by,
And yet I love her till I die.
617
With women the heart argues, not the mind.
Matthew Arnold
Merope, 1.341
674
In every cry of every Man,
In every Infants cry of fear,
In every voice: in every ban,
The mind-forg’d manacles I hear.
William Blake
London
696
Measure your mind’s height by the shade it casts.
Robert Browning
Paracelsus, pt. iii, i.363
708
I do not mind lying but I hate inaccuracy.
Samuel Butler
Note Books
743
That out of sight is out of mind
Is true of most we leave behind.
Arthur Hugh Clough
Songs in Absence, That Out of Sight
866
Beauty in things exists in the mind which contemplates them.
David Hume
Of Tragedy
871
Some experience of popular lecturing had convinced me that the necessity of making things plain to uninstructed people was one of the very best means of clearing up the obscure conrners in one’s own mind.
T.H. Huxley
Man’s Place in Nature
965
A fanatic is one who can’t change his mind and won’t change the subject.
Winston Churchill
983
Dans les champs de l’observation le hasard ne favorise que les esprits prepares.
[Where observation is concerned, chance favours only the prepared mind.]
Louis Pasteur
1137
Years steal
Fire from the mind as vigor from the limb,
And life’s enchanted cup but sparkles near the brim.
Lord Byron
Childe Harold’s Pilgrimage
1182
This have I known always: Love is no more
Than the wide blossom which the wind assails,
Than the great tide that treads the shifting shore,
Strewing fresh wreckage gathered in the gales:
Pity me that the heart is slow to learn
What the swift mind beholds at every turn.
Edna St. Vincent Millay
Sonnets, xxix
1195
I know a man that’s a braver man
And twenty men as kind,
And what are you, that you should be
The one man in my mind?
Edna St. Vincent Millay
The Philosopher
1317
Her mind lives tidily, apart
From cold and noise and pain,
And bolts the door against her heart,
Out wailing in the rain.
Dorothy Parker
Interior
1508
Nothing contributes so much to tranquilizing the mind as a steady purpose—a point on which the soul may fix its intellectual eye.
Mary Wollstonecraft Shelley
1547
So the old tunes float in my mind,
And go from me leaving no trace behind,
Like fragrance borne on the hush of the wind.
but in the instant the airs remain
I know the laughter and the pain
Of times that will not come again.
Sara Teasdale
Old Tunes
1584
Remember me and beare in mind
A truthful friend is hard to find
The path of sorrow and that alone
Leads to a place where sorrow is unknown.
Anna Bowman
Autograph Albums and Bible of Ella Beaver Calhoun
1634
Something made of nothing, tasting very sweet,
A most delicious compound, with ingredients complete;
But if, as on occasion, the heart and mind are sour,
It has no great significance, and loses half its power.
Mary. E. Buell
The Kiss
VIEW ALL

news + thoughts

Statistics vs Machine Learning

Tue 03-04-2018
We conclude our series on Machine Learning with a comparison of two approaches: classical statistical inference and machine learning. The boundary between them is subject to debate, but important generalizations can be made.

Inference creates a mathematical model of the datageneration process to formalize understanding or test a hypothesis about how the system behaves. Prediction aims at forecasting unobserved outcomes or future behavior. Typically we want to do both and know how biological processes work and what will happen next. Inference and ML are complementary in pointing us to biologically meaningful conclusions.

Martin Krzywinski @MKrzywinski mkweb.bcgsc.ca
Nature Methods Points of Significance column: Statistics vs machine learning. (read)

Statistics asks us to choose a model that incorporates our knowledge of the system, and ML requires us to choose a predictive algorithm by relying on its empirical capabilities. Justification for an inference model typically rests on whether we feel it adequately captures the essence of the system. The choice of pattern-learning algorithms often depends on measures of past performance in similar scenarios.

Bzdok, D., Krzywinski, M. & Altman, N. (2018) Points of Significance: Statistics vs machine learning. Nature Methods 15:233–234.

Background reading

Bzdok, D., Krzywinski, M. & Altman, N. (2017) Points of Significance: Machine learning: a primer. Nature Methods 14:1119–1120.

Bzdok, D., Krzywinski, M. & Altman, N. (2017) Points of Significance: Machine learning: supervised methods. Nature Methods 15:5–6.

...more about the Points of Significance column

Happy 2018 `\pi` Day—Boonies, burbs and boutiques of `\pi`

Wed 14-03-2018

Celebrate `\pi` Day (March 14th) and go to brand new places. Together with Jake Lever, this year we shrink the world and play with road maps.

Streets are seamlessly streets from across the world. Finally, a halva shop on the same block!

Martin Krzywinski @MKrzywinski mkweb.bcgsc.ca
A great 10 km run loop between Istanbul, Copenhagen, San Francisco and Dublin. Stop off for halva, smørrebrød, espresso and a Guinness on the way. (details)

Intriguing and personal patterns of urban development for each city appear in the Boonies, Burbs and Boutiques series.

Martin Krzywinski @MKrzywinski mkweb.bcgsc.ca
In the Boonies, Burbs and Boutiques of `\pi` we draw progressively denser patches using the digit sequence 159 to inform density. (details)

No color—just lines. Lines from Marrakesh, Prague, Istanbul, Nice and other destinations for the mind and the heart.

Martin Krzywinski @MKrzywinski mkweb.bcgsc.ca
Roads from cities rearranged according to the digits of `\pi`. (details)

The art is featured in the Pi City on the Scientific American SA Visual blog.

Check out art from previous years: 2013 `\pi` Day and 2014 `\pi` Day, 2015 `\pi` Day, 2016 `\pi` Day and 2017 `\pi` Day.

Machine learning: supervised methods (SVM & kNN)

Thu 18-01-2018
Supervised learning algorithms extract general principles from observed examples guided by a specific prediction objective.

We examine two very common supervised machine learning methods: linear support vector machines (SVM) and k-nearest neighbors (kNN).

SVM is often less computationally demanding than kNN and is easier to interpret, but it can identify only a limited set of patterns. On the other hand, kNN can find very complex patterns, but its output is more challenging to interpret.

Martin Krzywinski @MKrzywinski mkweb.bcgsc.ca
Nature Methods Points of Significance column: Machine learning: supervised methods (SVM & kNN). (read)

We illustrate SVM using a data set in which points fall into two categories, which are separated in SVM by a straight line "margin". SVM can be tuned using a parameter that influences the width and location of the margin, permitting points to fall within the margin or on the wrong side of the margin. We then show how kNN relaxes explicit boundary definitions, such as the straight line in SVM, and how kNN too can be tuned to create more robust classification.

Bzdok, D., Krzywinski, M. & Altman, N. (2018) Points of Significance: Machine learning: a primer. Nature Methods 15:5–6.

Background reading

Bzdok, D., Krzywinski, M. & Altman, N. (2017) Points of Significance: Machine learning: a primer. Nature Methods 14:1119–1120.

...more about the Points of Significance column

Human Versus Machine

Tue 16-01-2018
Balancing subjective design with objective optimization.

In a Nature graphics blog article, I present my process behind designing the stark black-and-white Nature 10 cover.

Nature 10, 18 December 2017

Machine learning: a primer

Thu 18-01-2018
Machine learning extracts patterns from data without explicit instructions.

In this primer, we focus on essential ML principles— a modeling strategy to let the data speak for themselves, to the extent possible.

The benefits of ML arise from its use of a large number of tuning parameters or weights, which control the algorithm’s complexity and are estimated from the data using numerical optimization. Often ML algorithms are motivated by heuristics such as models of interacting neurons or natural evolution—even if the underlying mechanism of the biological system being studied is substantially different. The utility of ML algorithms is typically assessed empirically by how well extracted patterns generalize to new observations.

Martin Krzywinski @MKrzywinski mkweb.bcgsc.ca
Nature Methods Points of Significance column: Machine learning: a primer. (read)

We present a data scenario in which we fit to a model with 5 predictors using polynomials and show what to expect from ML when noise and sample size vary. We also demonstrate the consequences of excluding an important predictor or including a spurious one.

Bzdok, D., Krzywinski, M. & Altman, N. (2017) Points of Significance: Machine learning: a primer. Nature Methods 14:1119–1120.

...more about the Points of Significance column