In Silico Flurries: Computing a world of snow. Scientific American. 23 December 2017
Art is Science in Love. Curiosity Collider. 31 January 2018
Communicating Science to Research and Industry. APSS, Sydney, Australia. 4–7 February 2018
Real human genome art, San Francisco
Nature Methods Points of View visualization column
Nature Methods Points of Significance statistics column
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.
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.
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
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
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.
Macbeth, V. i. 19.
Half a league, half a league, Half a league onward, All in the valley of Death Rode the six hundred.
Charge of the Light Brigade
Sleep—Death without dying—living, but not life.
Death is sometimes a punishment, sometimes a gift; To many it has come as a favor.
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.
Because I could not stop for Death, He kindly stopped for me, The carriage held but just ourselves And Immortality.
Life without a friend is like death without a witness.
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?
Windows NT crashed. I am the Blue Screen of Death. No one hears your screams.
A Haiku computer error message.
Three things are certain: Death, taxes and lost data. Guess which has occurred.
A Haiku computer error message.
Death is nature’s way of telling you to slow down.
Don’t be afraid of death so much as an inadequate life.
He is one of those people who would be enormously improved by death.
Those who fear death most are those who enjoy life least.
Marriage is the death of hope.
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.
The Ballad of Reading Gaol
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.
Song of Myself
I do not believe that any man fears to be dead, but only the stroke of death.
An Essay on Death
All tragedies are finish’d by a death, All comedies are ended by a marriage.
Don Juan, c.iii, st. 9
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.]
Maximes et Pensees, ch. 2
Death hath so many doors to let out life.
The Custom of the Country, II.ii
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.
Of Gray’s Odes
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
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
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.
My life is light, waiting for the death wind, Like a feather on the back of my hand.
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.
Holy Sonnets X
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
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
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.
Ballade of Unfortunate Mammals
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.
In every parting there is an image of death.
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!
The Destruction of Sennacherib
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.
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.
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.
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.
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.
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 algorithmâs 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 evolutionâeven 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.
▲ Nature Methods Points of Significance column: Machine learning: a primer.
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.