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Mad about you, orchestrally.Hooverphonicfeel the vibe, feel the terror, feel the painmore quotes

quotes: exciting


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 death

34
Little fly, thy summer’s play
My careless hand hath brushed away.
Am not I a fly like thee,
Or art not thou a man like me.
For I dance, and drink, and sing
Till some blind hand doth brush my wing.
If that is life, and strength, and breath
And the word of thought is death
Then am I a happy fly?
If I live, or if I die.
William Blake
45
Sleep is a death, O make me try
By sleeping, what it is to die,
And as gently lay my head
On my grave, as now my bed.
Sir Thomas Browne
Religio Medici, part II
48
I love thee with the love I seemed to lose
With my lost saints,—I love thee with the breath
Smiles, tears, of all my life!—and, if God choose,
I shall but love thee better after death.
Elizabeth Barrett Browning
Sonnets from the Portuguese
132
To-morrow, and to-morrow, and to-morrow,
Creeps in this petty pace from day to day
To the last syllable of recorded time,
And all our yesterdays have lighted fools
The way to dusty death.
Out, out, brief candle!
Life’s but a walking shadow, a poor player
That struts and frets his hour upon the stage
And then is heard no more: it is a tale
Told by an idiot, full of sound and fury,
Signifying nothing.
William Shakespeare
Macbeth, V. i. 19.
140
Half a league, half a league,
Half a league onward,
All in the valley of Death
Rode the six hundred.
Alfred Tennyson
Charge of the Light Brigade
154
Sleep—Death without dying—living, but not life.
Edwin Arnold
156
Death is sometimes a punishment, sometimes a gift;
To many it has come as a favor.
Seneca
157
The prince who kept the world in awe,
The judge whose dictate fix’d the law;
The rich, the poor, the great, the small,
Are levelled; death confounds ’em all.
Gay
158
Because I could not stop for Death,
He kindly stopped for me,
The carriage held but just ourselves
And Immortality.
Emily Dickinson
266
Life without a friend is like death without a witness.
299
Death comes with a crawl, or comes with a pounce,
And whether he’s slow or spry,
It isn’t the fact that you’re dead that counts,
But only, how did you die?
Edmund Vance Cooke
How Did You Die?
337
Windows NT crashed.
I am the Blue Screen of Death.
No one hears your screams.
A Haiku computer error message.
340
Three things are certain:
Death, taxes and lost data.
Guess which has occurred.
A Haiku computer error message.
417
Death is nature’s way of telling you to slow down.
431
Don’t be afraid of death so much as an inadequate life.
Bertolt Brecht
466
He is one of those people who would be enormously improved by death.
H.H. Munro
475
Those who fear death most are those who enjoy life least.
502
Marriage is the death of hope.
Woody Allen
524
At six o’clock we cleaned our cells,
At seven all was still,
But the sough and swing of a mighty wing
The prison seemed to fill,
For the Lord of Death with icy breath
Had entered in to kill.
Oscar Wilde
The Ballad of Reading Gaol
595
The smallest sprout shows there is really no death.
And if ever there was it led forward life, and does not
wait at the end to arrest it.
Walt Whitman
Song of Myself
645
I do not believe that any man fears to be dead, but
only the stroke of death.
Francis Bacon
An Essay on Death
712
All tragedies are finish’d by a death,
All comedies are ended by a marriage.
Lord Byron
Don Juan, c.iii, st. 9
729
Vivre est un maladie dont le sommeil nous soulage
toutes les 16 heures. C’est un pallatif. La mort
est le remede.
[Living is an illness to which sleep provides
relief every sixteen hours. It’s a palliative.
The remedy is death.]
Nicolas-Sebastien Chamfort
Maximes et Pensees, ch. 2
812
Death hath so many doors to let out life.
John Fletcher
The Custom of the Country, II.ii
885
Then, with no throbs of fiery pain,
No cold gradations of decay,
Death broke at once the vital chain,
And freed his soul the nearest way.
Samuel Johnson
Of Gray’s Odes
963
O death! I know it—’tis my famulus—
Thus turns to naught my fairest bliss!
That visions in abundance such as this
Must be disturbed by that dry prowler thus!
Johann Wolfgang von Goethe
Faust
1092
For the crown of our life as it closes
Is darkness, the fruit there of dust;
No thorns go as deep as the rose’s,
And love is more cruel than lust.
Time turns the old days to derision,
Our loves into corpses or wives;
And marriage and death and division
Make barren our lives.
Algernon Charles Swinburne
Dolores
1130
Though they go mad they shall be sane.
Though they sink through the sea, they shall rise again.
Though lovers be lost, love shall not,
And death shall have no dominion.
Dylan Thomas
1138
My life is light, waiting for the death wind,
Like a feather on the back of my hand.
T.S. Eliot
1141
Death be not proud, though some have called thee
Mighty and dreadful, for, thou art not so,
For, those, whom thou think’st, thou dost overthrow,
Die not, poor death, nor yet canst thou kill me.
John Donne
Holy Sonnets X
1142
Don’t strew me with roses after I’m dead.
When Death claims the light of my brow
No flowers of life will cheer me: instead
You may give me my roses now!
Thomas F. Healey
1194
Suffer me to take your hand.
Suffer me to cherish you
Till the dawn is in the sky.
Whether I be false or true,
Death comes in a day or two.
Edna St. Vincent Millay
Mariposa
1311
Show me a love was done and through,
Tell me a kiss escaped its debt!
Son, to your death you’ll pay your due—
Women and elephants never forget.
Dorothy Parker
Ballade of Unfortunate Mammals
1319
Oh, it is sure as it is sad
That any lad is every lad,
And what’s a girl, to dare impore
Her dear be hers forevermore?
Though he be tried and he be bold,
And swearing death should he be cold,
He’ll run the path the others went....
But you, my sweet, are different.
Dorothy Parker
Incurable
1446
In every parting there is an image of death.
George Eliot
1472
For the Angel of Death spread his wings on the blast,
And breathed in the face of the foe as he pass’d;
And the eyes of the sleepers wax’d deadly and chill,
And their hearts but once heaved, and for ever grew still!
Lord Byron
The Destruction of Sennacherib
1476
To die, to sleep—
To sleep, perchance to dream, ay there’s the rub,
For in that sleep of death what dreams may come
When we have shuffled off this mortal coil,
Must give us pause; there’s the respect
That makes calamity of so long life.
William Shakespeare
Hamlet
1485
The harder the conflict, the more glorious the triumph. What we obtain too cheap, we esteem too lightly; it is dearness only that gives everything its value. I love the man that can smile in trouble, that can gather strength from distress and grow brave by reflection. ’Tis the business of little minds to shrink; but he whose heart is firm, and whose conscience approves his conduct, will pursue his principles unto death.
Thomas Paine
1596
I do not want to believe that death is the gateway to another life. For me, it is a closed door. I do not say it is a step we must all take, but that it is a horrible and dirty adventure.
Albert Camus
1672
Life is short. But death is very, very long.
Henning Mankell
Quicksand
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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