Author: tomeri

The Disordered Mind…, Eric R. Kandel

The Disordered Mind: What Unusual Brains Tell us about Ourselves, Eric R. Kandel, 2018.

Kandel is an eminent neuroscientist, known for his work on the low-level mechanisms of learning and memory as demonstrated in Aplysia. He’s won a host of prizes, including the Nobel for this work. Interestingly, as an undergraduate he majored in humanities, and afterwards became a psychiatrist, before migrating into neuroscience. Now in his 90’s, he is writing about larger themes, and addressing himself to more general audiences.

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The Innocence of Father Brown, G. K. Chesterton

March 2025

I only discovered G. K. Chesterton a few years ago, through his essays which are generally excellent, and some which I would call brilliant. More recently I’ve dipped into his fiction. The Man Who was Thursday was superb, both surreal and funny, and laden with the striking descriptions — of landscapes, settings, people — of which Chesterton is a master. After that, just last month, I tried a second piece of fiction, The Napoleon of Notting Hill.  I wrote a brief review of that, and, as I said, I did not care for that at all — it was clearly produced by the same author, but there the surreal became simply absurd, and the humor farce. Suspension of disbelief failed. 

Still, having liked so much of his writing, and having found so little recent fiction satisfying, I wanted to try again, and so turned to his Father Brown stories about a Priest-Detective. The friend who had initially brought GKC to my attention recommended the story, The Blue Cross, as his favorite, and sent me a link to this volume on Project Guttenberg. The Blue Cross was indeed excellent, and so I proceeded through the rest of the volume.

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The Napoleon of Notting Hill, G. K. Chesterton

A couple of years ago I read Chesterton’s The Man who was Thursday. I love it. The writing was beautiful in parts, and the story a blend of the absurd and surreal — it was funny, although I did catch on to what was happening pretty quickly. But still, it was quite delightful, and that was not dampened by the Chestertonian moral/religious overlay.

All that is to say that I picked up The Napoleon of Notting Hill with anticipation. Written circa 1903, the story was set in a future London — 1984 — where almost nothing had changed in terms of class, society or technology, with the exception that instead of having a hereditary monarchy, a monarch was selected at random. The story is about the selection of a new monarch who is deeply unserious, and for his own amusement decrees that the various neighborhoods of London should function as independent nations, with their own heraldry and uniformed guards (which the new King designed), and their own traditions and customs. All this is intended to restore some of the ‘romance’ of medieval times, and, to the King’s delight, soon results in armed battles between the neighborhoods — Notting Hill, in a surprise, becoming ascendant.

Anyway, that’s the starting point of the book, but I have to say it didn’t engage me much. Whereas ‘Thursday’ was funny and surreal, this was absurd and unbelievable. It took about 3/4 of the book (it’s short) for me to become at all engaged, and then it was more a matter of curiosity about how Chesterton would wrap it up, rather than caring about the characters or story. Towards the end Chesterton does make a case for his preference for romance and semi-feudal systems vis a vis modernity, but it was mainly interesting as reinforcing my understanding of Chesterton’s view of the world.

Too bad. …But I do intend to give a couple of his ‘Father Brown’ books — about his priest-detective, a try.

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Basaltic Volcanoes, G. Walker

January 2025

I am told this is a classic papper. Here are some notes / excerpts:

  • “Basaltic magma is derived by incongruent partial melting of mantle peridotite, favoured in tectonic settings (e.g. hotspots and rifts) where mantle rock rises adiabatically to relatively shallow levels, or in subduction-zone settings where volatiles decrease the melting temperature of mantle rock.”
  • Important magma parameters (pretty uniform for basaltic volcanos)
    • Magma density relative to lithosphere density — helps deter- mine the positions of magma chambers and intrusions;
    • Viscosity and yield strength determine the geometry and structures of lava flows and intrusions;
    • Gas content + viscosity + rheology controls the explosive violence of eruptions by determining the ease with which gases escape from magmas.
  • Parameters responsible for diversity: magma-supply rate and involvement of non- magmatic water.
  • ”Basaltic systems have a source in the mantle from which magma ascends, mainly because of its positive buoyancy but sometimes aided by tectonic forces, toward the surface. They have one or more conduits by which the magma ascends. Polygenetic volcano systems generally possess a high-level magma chamber, situated at a neutral buoyancy level, which stores magma and modulates its delivery to the volcano and to sub-volcanic intrusions. Deep storage reservoirs may also exist.”
  • Types of volcanos
    • Shield volcanos
    • Stratovolcanos
    • Central Volanos.
    • Monogenetic volcanoes. These consist of clusters of scattered and mostly small (> 2 km3) volcanoes, each generated by a single eruption. Most commonly a volcano con- sists of a cinder cone associated with outflows of aa lava, but some are lava shields of scutulum- type (e.g. Rangitoto Island, Auckland, and Xitle in M…, and many that occur near the coast or close to lakes are phreatomagmatic tuff-rings or maars.
    • Flood basalt fields consist of monogenetic volcanoes erupted from widely scattered vents, but their lava flows cover wider areas, overlap or are superposed to form parallel-stratified successions, and have much greater volumes. Giant flood-basalt fields are distributed through geological time at average intervals of 32 Ma (Rampino & Stothers 1988), and each one formed at the time of inception of a hotspot, on arrival of an ascending mantle plume at the asthenosphere/litho- sphere boundary.
  • Volcano Collapse due to instable foundations, layers of pyroclastic or hydrothermally-altered material, intrusive dykes, local updomings in central volcanoes, severe marine erosion.
  • Polygenetic vs. monogenetic. “In the polygenetic volcano systems, magma batches ascend sufficiently frequently along the same conduit that the conduit walls are maintained in a hot condition and provide magma with a thermally and mechanically very favour- able pathway toward the surface. In the monogenetic and flood basalt systems magma batches ascend at such long time inter- vals that the pathway taken by one batch has effectivelycooled by the time that the next batch is ready to ascend.”
  • Fissures / Rift systems. ‘Most basaltic eruptions occur from fissures, and virtually all basaltic volcano systems have eruptive fissures. Fissures are opened very easily by the hydraulic jacking action of magma, and are the ‘natural’ underground conveyance for low- viscosity magma (Emerman & Marrett 1990). They commonly extend for tens of kilometres and are typically concentrated into rift zones. Magma solidified in fissures forms dykes. Dykes have a high survival potential, and in deeply eroded areas may be virtually all that survives of the volcanic system.
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Vesper Flights, Helen Macdonald

January 2025

I picked up this book, probably about a year ago at the recommendation of Dan Russell. In terms of single-author collections, I’ve liked this more than anything I’ve read in years, perhaps with the exception of Loren Eisley’s essays. Regardless, Macdonald is a superb writer, and in particular her descriptions of the natural world are remarkable. I intend to seek out her other books.

I like, as well, her view of what literature ought to do:

What science does is what I would like more literature to do too: show us that we are living in an exquisitely complicated world that is not all about us. — Helen Macdonald, Vesper Flights. p. ix

Favorites

  • 2. Nothing Like a Pig
  • 9. Ants
  • 10. Symptomatic
  • 12. Winter Woods
  • 18. Deer in the Headlights
  • 35 Eulogy
  • 38. Dispatches from the Valley

The Craft of Writing — things I’ve learned here

  • Describing a Moment: “And then it happens: a short, collapsing moment.” This passage, by foregrounding the nature of the moment, and the movement from uncertainty to realization, does a superb job of highlighting and intensifying an epiphany. (In Nothing like a Pig.)
  • A beautiful resonating description: “a long, yawning burr that dopplered into memory and replayed itself in dreams.“
  • Use of incongruity for stream-of-consciousness. In Deer in the Headlights she does a great job of conveying the incongruity of two worlds — the forest and the highway — in a single sentence that juxtaposes glimpses of the nature of each. Similarly, in The Student’s Tale, the first sentence, with independent clauses connected by a series of “ands,” really conveys an immediate stream-of-consciousness experience, where the attention is hopping around, and making non-rational connections as it does so (.e.g, the grapes on the table are black, and so is the taxi out front).
  • Transforming dynamic movement into a pattern.“The hitching curves of the gulls in a vault of sky crossed with thousands of different flightlines…” (Ants). For me, this generates a pattern — a vault of sky circumscribed by imagined flightlines – that extends over time and creates a persistent space which frames other happenings…

1. Nests

This essay describes nests. She begins with her feelings about nests develop when she was a child, and encountered them in her yard. She then goes into the present, and reflects more on this than their meanings.

*2. Nothing like a Pig

This essay describes an encounter with a boar. She reflects both on the boar, and more in general on animals in particular, and how the conception of an animal differs from the reality of the animal

Then it happens: a short, collapsing moment as sixty or seventy yards away something walks fast between the trees, and then the boar. The boar. The boar.

– Vesper Flights, Helen Macdonald, p 11.

A great bit of writing. The “short, collapsing moment.” The uncertainty about distance — “sixty or seventy yards” — and what she is seeing — “something.” The revelation: “and then the boar.” And the repetition: “The boar. The boar.“

3. Inspector Calls

A very nice short piece about an encounter with autistic boy, who is visiting her flat with his parents. In particular he connects with her bird and the bird with him.

4. Field Guides

“Field guides made possible the joy of encountering a thing I already knew but had never seen.”

5. Terkels Park

An essay on the place where she grew up. A bit nostalgic, but it was unusual, and had interesting reflections, so I found it worth reading. Some very nice writing:

I could lie awake in the small hours and hear a single motorbike speeding west or east: a long, yawning burr that dopplered into memory and replayed itself in dreams.

— ibid. p 12

My eyes catch on the place where the zoetrope flicker of pines behind the fence gives way to a patch of sky with the black peak of a redwood tree against it and the cradled mathematical branches of a monkey puzzle, and my head blooms with an apprehension of lost space,

— Ibid. p 13

6. High-Rise

About watching migrating birds at night from the top of the Empire State Building. An interesting discussion of how birds migrate — the height and speeds at which they fly, and the way they navigate — and the problems that the lights and tall buildings of the city give them.

7. The Human Flock

“Overhead a long wavering chevron of beating wings is inked across the darkening sky.“

Recounting the observation of large flocks of migrating cranes, and continuing to a discussion of the dynamics of swarms and murmurations. “Turns can propagate through a cloud of birds at speeds approaching 90 miles an hour…” This segues into a concluding comment on refugees, and a plea to regard them as individuals rather than masses.

8. The Student’s Tale

An account of meeting a student who is a refugee and spending time in camps…

A great opening sentence:

There’s a window and the rattle of a taxi and grapes on the table, black ones, sweet ones, and the taxi is also black and there’s a woman inside it, a charity worker who befriended you when you were in detention, and she’s leaning to pay the driver and through the dust and bloom of the glass I see you standing on the pavement next to the open taxi door and your back is turned towards me so all I can see are your shoulders hunched in a blue denim jacket.

— The Student’s Tale, Vesper Flight, Kate Macdonald, p. 53

I think this is a marvelous stream-of-consciousness sentence, with the writers attention shifting from taxi to grapes to taxi to the woman and then to the student whose shoulders are hunched. The second person is also very effective.

*9. Ants

About the mating flights of ants, and the birds that prey upon them. Also reflects on the power of scientific understanding to enhance the beauty of things, rather than detract: “…it’s things I’ve learned from scientific books and papers that are making what I’m watching almost unbearably moving.”

A red kite joins the flock, drifting and tilting through it on paper-cut wings stamped black against the sky.

[…]

The hitching curves of the gulls in a vault of sky crossed with thousands of different flightlines, warm airspace tense with predatory intent and the tiny hopes of each rising ant.

— Helen Macdonald, Vesper Flights, p. 63

*10. Symptomatic

Discusses her experiences with migraines. The writing is beautiful and ranges from describing the onset and symptoms of her migraine, to the way in which she has come to live with them. Ends with a partial analogy to earth undergoing climate change…

I was busily signing books when a spray of sparks, an array of livid and prickling phosphenes like shorting fairy lights, spread downwards from the upper right-hand corner of my vision until I could barely see through them.
—Helen Macdonald, Vesper Flights, p. 66

11. Sex, Death, Mushrooms

On mushroom hunting: “It is raining hard, and the forest air is sweet and winey with decay.“

The air is damp and dark in here. Taut lines of spider silk are slung between their flaking trunks; I can feel them snapping across my chest. Fat garden spiders drop from my coat on to the thick carpet of pine needles below.
—Helen Macdonald, Vesper Flights, p. 80

I like feeling the snapping, and the spiders dropping from her coat to the forest floor. It animates the scene, and tells us she is moving through it.

* 12. Winter Woods

Beginning with her custom of walking in the woods every New Year’s day, she reflects on the things that are distinctive about forests in winter. From the revelation of the landscape, to the bark textures and angled branches of leafless trees, to the sometimes transitory life that becomes evident. Winter woods, she suggests, are full of potential:

So often we think of mindfulness, of existing purely in the present moment, as a spiritual goal. But winter woods teach me something else: the importance of thinking about history. They are able to show you the last five hours, the last five days, the last five centuries, all at once. They’re wood and soil and rotting leaves, the crystal fur of hoarfrost and the melting of overnight snow, but they are also places of different interpolated timeframes. In them, potentiality crackles in the winter air.
—ibid., p. 85

13. Eclipse

On viewing a solar eclipse. The phenomenology of the event, but also the deep, irrational, fundamental, emotional impact. The essay is reminiscent of Joan Didion’s essay, and in particular the way in which the fading daylight alters the colors in ways that cast the landscape in an alien light. It ends, beautifully, with a description of the light returning, and the emotions that brings.

14. In Her Orbit

A description of a trip with an astrobiologist to study extremophiles at very high altitudes in the Andes. Some beautiful descriptions of desolate and unworldly environments.

15. Hares

A description of the phenomenon of boxing hares, their place in English thought and mythology, and their decline due to environmental change.

16. Lost, But Catching Up

A very short essay description her glimpse of a hound that was trying to catch up to the pack during a fox hunt.

17. Swan Upping Nestboxes

About the English tradition of “Swan Upping,” and her experience observing the activity; all interladen with reflections on the role of tradition and its uneasy releationship to Brexit, which had recently occurred.

18. Deer in the Headlights

Discusses her changing feelings about deer, from initially wishing to known nothing about them and valuing them as a source of surprise and delight, to a desire to understand them. She says it better, though:

Deer occupy a unique place in my personal pantheon of animals. There are many creatures I know very little about, but the difference with deer is that I’ve never had any desire to find out more. They’re like a distant country I’ve never wanted to visit. I know the names of different deer species, and can identify the commonest ones by sight, but I’ve always resisted the almost negligible effort it would take to discover when they give birth, how they grow and shed their antlers, what they eat, where and how they live. Standing on the bridge I’m wondering why that is.
– Helen Macdonald, Vesper Flights, p. 141

As the title suggests, much of the essay is about deer-vehicle collisions; and also about how people react to them, in the moment, and, sometimes in cruel ways, on the internet. It is a complex essay. It doesn’t really speak to me, but there are a lot of great turns of phrase and passages.

Here is how the essay begins:

The deer drift in and out of the trees like breathing. They appear unexpectedly delicate and cold, as if chill air is pouring from them to the ground to pool into the mist that half obscures their legs and turning flanks. They aren’t tame: I can’t get closer than a hundred yards before they slip into the gloom.

– Helen Macdonald, Vesper Flights, p. 140

And here is a passage I admire for the way it highlights the incongruity of the two worlds: nature and the highway. It moves from the forest, to the road, to the forest, to the road, to her standing, embodied, on the bridge.

For a while the road doesn’t seem real. Then it does, almost violently so, and at that moment the bridge and the woods behind me do not. I can’t hold both in the same world at once. Deer and forest, mist, speed, a drift of wet leaves, white noise, scrap-metal trucks, a convoy of eighteen-wheelers, beads of water on the toes of my boots and the scald of my hands on the cold metal rail.

– Helen Macdonald, Vesper Flights, p. 141

19. The Falcon and the Tower  

She is watching birds — falcons — in an abandoned industrial plant in Dublin. The essay discusses falcons, and how they have adapted to living in cities and their infrastructures. Moves from their behavior and natural history, to the ways in which people have viewed them, to their change in habitat given the ‘advance’ of civilization. Ends with a reflection on the brevity of life, and a note of hope.

20. Vesper Flights

The essay that gives the collection a title. Begins with her finding a dead Swift and not knowing what to do with it. Segues into a description of Swifts and how they are somewhat “magical” — “the closest things to aliens on earth.” After describing their natural history, describes the phenonmenon of “vesper flights,” where they gather in the evening and fly up to 8,000 feet. She describes how this behavior was discovered, and goes through the history of this behavior being observed and understood. Interleaved with this is her accounts of how, as a small child, she sought comfort in the evening (her own private vespers) by imagining herself as embedded in layers of the earth below her and the atmosphere above her. This comes together as we learn that vesper flights, for Swifts, help them take account of where they are and the oncoming weather conditions, and as Macdonald reflects on ways in which she (we) can adopt practices that enable us to locate ourselves and think about what comes next.

21. In Spight of Prisons

A very nice, short essay about her annual practice of going to see glowworms in a quarry.

* 22. Sun Birds and Cashmere Spheres

About her efforts to observe Oriels at the single place in Britain where they can still be found. Over time, their habitat is degraded, and at last there is only one… but, at the last moment, she is able to get a glimpse of it. She has a lovely sentence where she describes the song (or a song) or the oriel: “Wo-de-wal-e, wo-de-wal-e, a phrase like the curl of the cut ends of a gilded banner furling over the page of an illuminated manuscript.“

In this essay, she excels at capturing the fragmentary, mosaical nature of perception.

…what I saw became something like looking into a Magic Eye picture. Here was a circle, and in it a thousand angles of stalk and leaf and scraps of shade at various distances, and every straight stalk or branch was alternately obscured and revealed as the wind blew. I began to feel a little seasick watching this chaos, but then, as magically as a stereogram suddenly reveals a not-very-accurate 3D dinosaur, the muddy patch just off centre resolved itself into the nest.

[…]

Finally, I saw my oriole. A bright, golden male. It was a complex joy, because I saw him only in stamped-out sections, small jigsaw pieces of a bird, but moving ones, animated mutoscope views. A flick of wings, a scrap of tail, then another glimpse – this time, just his head alone – through a screen of leaves. I was transfixed. I had not expected the joyous, extravagant way this oriole leapt into the air between feeds, the enormously decisive movements, always, and the little dots like stars that flared along the edge of his spread-wide tail.

– Helen Macdonald, Vesper Flights, p. 177 & 179

23. The Observatory

About swans, beginning with an odd experience she had with one approaching her, and sitting beside her, in a moment of grief.

24. Wicken

About a visit to a nature preserve with her young niece, and her niece’s puzzlement about why there were so many animals here — ‘did they bring them from a zoo?’ Reflections on the shift from a time when nature and animals were all around us, to the present, when they are mostly found in special preserves.

25. Storm

A short essay describing a thunderstorm, and also reflecting on storms as metaphors, in particular, in this essay, for the onset of Brexit.

26. Murmurations

Begins with getting a passport replaced at the last minute, and then moves to how birds were seen during war time, and the rise and evolution of the notion of birdwatching.

27. A Cuckoo in the House

On cuckoos, how people perceive them, and in particular a rather eccentric British intelligence agent — Maxwell Knight — who raised a cuckoo. Didn’t grab me, but others might well find it a fascinating tale.

28. The Arrow-Stork

About tracking migrating birds. Makes this interesting point:

Projects like this give us imaginative access to the lives of wild creatures, but they cannot capture the real animals’ complex, halting paths. Instead they let us watch virtual animals moving across a world of eternal daylight built of a patchwork of layered satellite and aerial imagery, a flattened, static landscape free of happenstance. There are no icy winds over high mountain passes here, no heavy rains, soaring hawks, ripening crops or recent droughts.
– Helen Macdonald, Vesper Flights, p. 217

29. Ashes

About destroying diseased trees, beginning with elms with Dutch Elm disease in her childhood, and ending with ash trees and the emerald ash borer.

30. A Handful of Corn

About feeding animals. Starts with a nice anecdote about an elderly woman who put out corn to attract badgers at night. Continues into the practice of feeding animals, and makes the interesting point that there are some animals it is socially acceptable to feed, and others — foxes, rats, pigeons — that it is not.

31. Berries

This short essay begins with her decorating a Christmas tree, and sprucing up its decorations with berries from outside, but feeling slightly guilty because berries exist as food for birds. Segues into natural history of both birds and berries.

A great bit of description: “…like a gravity stricken whirlwind, a pack of fat birds swirled down from the blank sky…“

32. Cherry Stones

About the return of hawfinches to Britain, the excitement it engenders, and the ways in which their behavior seems to be changing vis a vis what habitat they prefer. Also touches on the blurring of natural history and national identity.

33. Birds, Tabled

About the practice of capturing and keeping birds, which in England is mostly done by the working classes, and which is, it seems, looked down upon by others. She discusses the practice, how bird keepers feel about it and their birds, and the class differences and that this highlights. Interesting.

34. Hiding

An interesting piece about hides (what we in the U.S. call “blinds”). It touches both on the aims and experience of watching animals from blinds, as well as the human experience within blinds.

* 35. Eulogy

A eulogy for a friend: a description of the her friend is interleaved with a night outing to see nightjars. A beautiful piece of writing.

The essay begins with a description of the outing, setting out while it is still light, but with the darkness coming:

 As night falls, our senses stretch to meet it. A roebuck barks in the distance, small mammals rustle in the grass. The faintest tick of insects. The scratchy, resinous fragrance of heathland grows stronger, more insistent. As we pass clumps of viper’s bugloss we watch the oncoming night turn their leaves blacker, their purple petals bluer and more intense until they seem to glow. The paths become luminous trails through darkness. White moths spiral up from the ground, and a cockchafer zips past us, elytra raised, wings buzzing.

– Helen Macdonald, Vesper Flights, p. 255

After this, she makes the connection to her friend: “Soon all color will be gone. The thought is a hard one.” And then, after writing about him: “Now, watching the slow diminishment of sense and detail around me, I’m thinking of Stu and what is happening to him, thinking of his family, of what we face at the end of our lives’ long summers when the world parts from us, of how we all, one day, will walk into darkness.“

A somber essay, but ending with a note of, not hope, but acceptance. Stu says, “It’s OK. It’s OK. It’s not hard.”

It’s OK, he said. It’s not hard. Those are the words I am remembering as we walk onward, as the minutes pass, until night thickens completely and there is starlight and dust and the feel of sand underfoot. It’s so dark now I cannot see myself. But the song continues, and the air around us is full of invisible wings.
– Helen Macdonald, Vesper Flights, p. 259

36. Rescue

An account of a visit to the house of a friend who rescues and rehabilitates swifts. It begins with her friend feeding nestlings, and ends with the release of a swift, and a haunting description of the swift’s transformation as it is about to take to the sky.

37. Goats

A brief, funny story about her, her dad, and pushing goats. Wouldn’t call it an essay though.

* 38. Dispatches from the Valleys

A curious essay centered around her experiences in her first job out of college, working on a falcon conservation-breeding farm. She describes what it was like — it sounded unpleasant to me, but she clearly got to do many things she loved and valued. She describes what led her to leave the farm, and does a good job of creating tension by naming two incidents, first “the dreadful incident with the ostrich,” and then “the cattle on the hill,” and describing each played out.

The ostrich incident — euthanasia of a horribly injured bird — was straightforward, if unpleasant. The “cattle on the hill” incident is quite strange: it involves her spending hours sneaking up on them, and then jumping up and scaring them into stampeding, though she does not know why.

At the end of the essay, though, she recounts an epiphany, and, for me, it resolves not just the ‘cattle on the hill’ incident, but the whole essay:

And then I thought of the day I stalked the steers on the hill and it resolved into perfect clarity. For I had seen myself as one of those steers, one of a feral and uncared-for herd enjoying life in the middle of nowhere, not thinking about what would happen in the future, and not much worried about it, but knowing deep down that one day I was headed for the abattoir. There would be no escaping the deep sea for the shore. And my stalking and shouting was not mindless. It had been an inchoate attempt to knock them out of their contented composure. It had been a warning to make them run the hell out of there, because the valley we were all in was dark and deep and could have no good end.
– Helen Macdonald, Vesper Flights, p. 282

39. The Numinous Ordinary 

An interesting essay with some nice passages in it, but it didn’t really resonate with me.

40. What Animals Taught Me

Discusses the author’s changing conceptions of and relationships to animals. She liked caring for them, as a child, but came to recognize that was about her feeling good about herself, rather than about the animals. As she grew older, she found that an intense focus on animals was a way to make herself disappear, to allow herself into a separate world that did not contain the difficulties she was faced with. Later, with respect to falconry, she speaks about how she learned that the other party in a relationship might see it very differently — a lesson she was slow to apply to humans. The “deepest lesson animals have taught me is how easily and unconsciously we see other lives as mirrors of our own.” And “None of us sees animals clearly. They are too full of the stories we have given them.“

Towards the end of the essay, speaking of a rook, she comments that now what she enjoys is not imagining that she can feel what the rook feels, know what it knows, but that it’s slow delight in knowing that she cannot.

As it passes overhead, the rook tilts its head to regard me briefly before flying on. And with that glance I feel a prickling in my skin that runs down my spine, my sense of place shifts, and the world is enlarged. The rook and I have shared no purpose. We noticed each other, is all. When I looked at the rook and the rook looked at me, I became a feature of its world as much as it became a feature of mine. Our separate lives coincided, and all my self-absorbed anxiety vanished in that one fugitive moment, when a bird in the sky on its way somewhere else sent a glance across the divide and stitched me back into a world where both of us have equal billing.
– Helen Macdonald, Vesper Flights, p. 299

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2025 ILSG Kilauea &c Field Trip Prep Notes

Volcanic Rock Types

B (Basalt)—Use normative mineralogy to subdivide. 

O1 (Basaltic andesite) 

O2 (Andesite) 

O3 (Dacite) 

R (Rhyolite) 

T (Trachyte or Trachydacite)—Use normative mineralogy to decide. 

Ph (Phonolite) 

S1 (Trachybasalt)—*Sodic and potassic variants are Hawaiite and Potassic Trachybasalt. 

S2 (Basaltic trachyandesite)—*Sodic and potassic variants are Mugearite and Shoshonite. 

S3 (Trachyandesite—*Sodic and potassic variants are Benmoreite and Latite.

Pc (Picrobasalt) 

U1 (Basanite or Tephrite)—Use normative mineralogy to decide. 

U2 (Phonotephrite) 

U3 (Tephriphonolite) 

F (Foidite)—When possible, classify/name according to the dominant feldspathoid. Melilitites also plot in this area and can be distinguished by additional chemical criteria. 

(*)Sodic as used above means that Na2O – 2 is greater than K2O, and potassic that Na2O – 2 is less than K2O. Yet other names have been applied to rocks particularly rich in either sodium or potassium—as are ultrapotassic igneous rocks.

Lava flows from Mauna Loa

Palagonite

About Palagonite

Palagonite is an alteration product formed from basaltic glass (tachylite); concentric bands of it often surround kernels of unaltered tachylite, and are so soft that they are easily cut with a knife. In the palagonite the minerals are also decomposed and are represented only by pseudomorphs. 

Palagonite soil is a light yellow-orange dust, comprising a mixture of particles ranging down to sub-micrometer sizes, usually found mixed with larger fragments of lava. The color is indicative of the presence of iron in the +3 oxidation state, embedded in an amorphous matrix.

Palagonite tuff is a tuff composed of sideromelane fragments and coarser pieces of basaltic rock, embedded in a palagonite matrix. A composite of sideromelane aggregate in palagonite matrix is called hyaloclastite.

Formation of Palagonite

Phreatomagmatic. Palagonite can be formed from the interaction between water and basalt melt. The water flashes to steam on contact with the hot lava and the small fragments of lava react with the steam to form the light-colored palagonite tuff cones common in areas of basaltic eruptions in contact with water.

Weathering. Palagonite can also be formed by a slower weathering of lava into palagonite, resulting in a thin, yellow-orange rind on the surface of the rock. The process of conversion of lava to palagonite is called palagonitization.

Tachylite

About Tachylite (tachylyte)

Tachylite (from ταχύς, meaning “swift”) is a form of basaltic volcanic glass formed by the rapid cooling of molten basalt. It is a type of mafic igneous rock that is decomposable by acids and readily fusible. The color is a black or dark-brown, and it has a greasy-looking, resinous luster. It is often vesicular and sometime spherulitic. Small pheoncrysts of feldspar or olivine are sometimes visible. Fresh tachylite glass often contains lozenge-shaped crystals of plagioclase feldspar and small prisms of augite and olivine, but all these minerals occur mainly as microlites or as skeletal growths with sharply-pointed corners or ramifying processes.

All tachylites weather easily and become red to brown as their iron oxidizes.

Formation

Three modes of occurrence characterize this rock. In all cases they are found under conditions which imply rapid cooling, but they are much less common than acid obsidians. (Alkaline rocks have a stronger tendency to crystallize (i.e. not form glass), in part because they are more liquid and the molecules have more freedom to arrange themselves in crystalline order.)

Scoria

The fine scoria (aka cinders) thrown out by basaltic volcanoes are often spongy masses of tachylite with only a few larger crystals or phenocrysts imbedded in black glass. Basic pumices of this kind are exceedingly widespread on the bottom of the sea, either dispersed in the pelagic red clay and other deposits or forming layers coated with oxides of manganese precipitated on them from the sea water. These tachylite fragments, which are usually much decomposed by the oxidation and hydration of their ferrous compounds, have taken on a dark red color (scoria is from σκωρία, skōria, Greek for rust.); this altered basic glass is known as “palagonite.” [see above]

Lava flows

In the Hawaiian Islands volcanoes have poured out vast floods of black basalt, containing feldspar, augite, olivine, and iron ores in a black glassy base. They are highly liquid when discharged, and the rapid cooling that ensues on their emergence to the air prevents crystallization taking place completely. Many of them are spongy or vesicular, and their upper surfaces are often exceedingly rough and jagged, while at other times they assume rounded wave-like forms on solidification. Great caves are found where the crust has solidified and the liquid interior has subsequently flowed away, and stalactites and stalagmites of black tachylite adorn the roofs and floors. On section these growths show usually a central cavity enclosed by walls of dark brown glass in which skeletons and microliths of augite, olivine and feldspar lie embedded

Dikes and Sills

A third mode of occurrence of tachylite is as margins and thin offshoots of dikes or sills of basalt and diabase. They are often only a fraction of an inch in thickness, resembling a thin layer of pitch or tar on the edge of a crystalline diabase dike, but veins several inches thick are sometimes found. In these situations tachylite is rarely vesicular, but often shows pronounced fluxion banding* accentuated by the presence of rows of spherulites that are visible as dark brown rounded spots. The spherulites have a distinct radiate structure and sometimes exhibit zones of varying color. The non-spherulitic glassy portion is sometimes perlitic, and these rocks are always brittle. Common crystals are olivine, augite and feldspar, with swarms of minute dusty black grains of magnetite. At the extreme edges the glass is often perfectly free from crystalline products, but it merges rapidly into the ordinary crystalline diabase, which in a very short distance may contain no vitreous base whatever. The spherulites may form the greater part of the mass, they may be a quarter of an inch in diameter and are occasionally much larger than this.

Fluxion banding

See: https://en.wikipedia.org/wiki/Flow_banding

Flow banding is caused by friction of the viscous magma that is in contact with a solid rock interface, usually the wall rock to an intrusive chamber or the earth’s surface.

The friction and viscosity of the magma causes phenocrysts and xenoliths within the magma or lava to slow down near the interface and become trapped in a viscous layer. This forms laminar flow, which manifests as a banded, streaky appearance.

Flow banding also results from the process of fractional crystallization that occurs by convection if the crystals that are caught in the flow-banded margins are removed from the melt. This can change the composition of the melt in large intrusions, leading to differentiation.

From GPT:

Fluxion banding results from shear forces within a moving magma body. This can happen in several ways:

1. Differential Flow in Lava. As lava moves, its viscosity varies due to cooling and crystallization. The outer layers, which cool faster, may develop a plastic or solid crust, while the inner material remains fluid. This difference in viscosity causes layers of magma to stretch and deform, forming elongated bands.

2. Crystal Sorting and Alignment. “ As magma flows, mineral crystals within it may become aligned due to shear stress. This is common in silicic lavas like rhyolite and dacite, where feldspar and quartz can form parallel bands.

3. Magma Mixing and Compositional Banding. If two magmas of different compositions mix, they may not completely homogenize, leading to streaks of contrasting compositions that appear as bands.

4. Intrusive Settings. In some plutonic rocks, fluxion banding may form as a result of late-stage magmatic flow, where crystals and melts segregate due to convection or deformation.

Kilauea: Dynamics of eruptions; Magma types

  • over the last 4 decades Kilauea has been very active, erupting both from Haumaumau crater on its summit and various rifts on its east side.
  • in 1983 Kilauea  longest and most voluminous outpouring of lava from Kīlauea’s East Rift Zone in over 500 years. It resulted in the creation of the Pu‘u ‘Ō‘ō cone and extensive lava flows that covered significant areas, destroyed numerous structures, and added new land to the island. 
  • Kilauea erupted on 4 May 2018 — it was an east rift zone eruption following the collapse of the Pu’u O’o vent. The rift eruption was driven by collapse of the central (shallow) magma chambers 
  • The 2018 rift eruption had at least three different magmas: 
    • a highly evolved cool (1110°) viscous lava presumably from sources in the rift system [May 3-9]
    • a less evolved hot (1130°) more fluid lava [May 17-18…]
    • a very hot (1145°) magma lacking the cargo of low temperature crystals of the previous lavas, but with olvine with high levels of MgO indicating magma > 1250° somewhere in the feeder system

“The first two were the chemically evolved basalt of the initial fissures and the highly viscous andesite. Both are volumetrically minor sources that represent distinct pockets of old residual magma from Kīlauea’s east rift zone that evolved for more than 55 years, cooling and crystallizing at depth. The third and volumetrically more substantial source was less-evolved and hotter basalt of fissure 8. This source was similar in composition to the magma erupted at Kīlauea in the years before 2018 and was ultimately derived from the summit region. Draining and collapse of the summit by this voluminous eruption may have stirred up deeper, hotter parts of the summit magma system and sent mixed magma down the rift..”

Things I’ve learned re eruption dynamics and magmas

  • Not all lava from Hawaiian volcanoes is basaltic
  • Even that that is basaltic, changes in composition; each eruption features at least one, and often several, unique lava compositions. 
  • Magma chambers are not homogeneous; this is presumably even more true of rift systems, where greater cooling can generate mushes of crystals 
  • The 2018 Kilauea rift eruptions were driven by collapse of summit magma chambers. 
  • The 2018 Kilauea rift eruption exhibited periodicity of 2-3 days (surges that began within minutes of caldera collapses 40 K upslope) and 5-10 minutes (pulses driven by local outgassing changes )
  • The dynamics of an eruption can be mapped into several stages
  • Lateral injection of magma into a rift zone (which forms, in Hawaii, due to volcano flanks sliding into ocean) leads to initial eruption
  • Pressure in the rift system leads to its elaboration – advancing dikes may capture pockets of highly evolved magma with mushes of low temperature crystals.
  • Magma injection into rifts, if large enough, can trigger slip on caldera ring faults 
  • Ring fault slippage can add pressure to rift system and drive eruptive behavior at the rift
  • The central magma chamber appears to be vertically zoned. Initial eruptions of the rift zone (after flushing out pockets of magma that have evolved in the rifts) are composed of younger magmas from lower in the chamber; summit eruptions are fed by older, more evolved magma, higher up in the chamber. 
  • The 2018 Kilauea eruption produced lava at volumes of 100 meters3/sec
  • Stages of Hawaiian volcanoes: pre-shield (alkalic basalt & basanite); shield (thoelitic basalt derived from both shallow plumbing system and deep plumbing system adjacent to mantle); post-shield (alkalic basalt from deep plumbing system adjacent to mantle (shallow plumbing has crystalized)); post erosional/rejuvenated (alkalic basalt, basanite & nephelinite from ???)

Order and nature of basaltic mineral & crystals

Common minerals that crystallize from basaltic magma, ordered by the temperatures at which they typically form:

  1. *Olivine (Ca2(Mg,Fe)4O4): This is one of the first minerals to crystallize at the highest temperatures, typically around 1,200°C to 1,300°C. Olivine is rich in magnesium and iron and is often found in the earliest stages of crystallization in basaltic magmas. 
         *Olivine crystals are olive-green to yellow-green color. It often has a glassy or vitreous luster, and the crystals can be angular or rounded, with a granular texture when present in volcanic rocks. When olivine crystals are large enough, they often appear as transparent or translucent, sometimes with visible crystal faces, which are usually in a near-rectangular shape. 
         When olivine is exposed to oxidation, especially under conditions of high temperatures or in the presence of oxygen, it can alter to a yellowish or brownish hue, sometimes developing a reddish or rusty tint due to the formation of iron oxide minerals.
  2. *Pyroxene (e.g., augite, diopside): Pyroxenes crystallize at slightly lower temperatures, generally around 1,100°C to 1,200°C. These minerals are composed of chains of tetrahedra and are rich in iron and magnesium. 
         *Augite crystals are dark green to black, often with a shiny, almost metallic luster. It crystallizes in short prismatic crystals, which are often rectangular or blocky in shape. Augite crystals are typically larger than many other basaltic minerals and can be quite visible in coarse-grained basalts.
         Augite, being rich in iron, may undergo partial oxidation upon exposure to the atmosphere. The oxidation often causes a darkening of the color to a more brownish or reddish tint, though it rarely forms the rusty, reddish color seen in olivine. Augite may also exhibit a duller or more matte luster when oxidized.
         *Diopside is another pyroxene mineral, typically appearing as light green to pale green, although it can also be colorless or pale yellow. It forms prismatic crystals that are often transparent or translucent. Diopside crystals have a glassy or vitreous luster and typically display distinct striations or fine parallel lines on their crystal faces.
  3. *Plagioclase feldspar (labradorite, anorthite): Plagioclase forms between 1,000°C and 1,100°C in basaltic magmas. This mineral can range from calcium-rich (anorthite) to sodium-rich (albite) compositions, with the more calcium-rich varieties crystallizing at higher temperatures.
         * Plagioclase crystals vary from white to gray, and often have a glassy luster.  They are typically tabular or blocky in shape and can show distinctive twin planes (known as albite twinning). 
  4. Magnetite (Fe3O4): Magnetite crystallizes at around 1,000°C to 1,100°C and often forms alongside other iron-rich minerals. It is a common accessory mineral in basaltic magmas.
         * Crystals not typically visible in lavas 
  5. Ilmenite (FeTiO3): Ilmenite forms at slightly lower temperatures, typically around 900°C to 1,000°C. It is a titanium-iron oxide mineral and often occurs in basaltic lavas.
         * Crystals not typically visible in lavas
  6. Spinel (MgAl2O4): Spinel crystallizes at lower temperatures, usually around 900°C. It is a common accessory mineral in basaltic rocks, often forming in the lower temperature range of basaltic crystallization.
         * Crystals not typically visible in lavas

These minerals crystallize according to Bowen’s reaction series, where early-formed minerals (like olivine and pyroxene) are typically more magnesium- and iron-rich, while later-formed minerals (like plagioclase and spinel) are more silica-rich due to depletion of Mg and Fe.

Other Notes

TBD

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EP #16: Letters, Oliver Sacks

December 2024 – April 2025

This is book # 16 in the no-longer-very-aptly named Essays Project. Though perhaps, having detoured into the wilds of Shakespeare, a tour of the letters of Sacks, who is a formidable essayist, is steering us back towards the main track. Of course, letters are not essays, but their relative brevity and personal cast, as well as the wide-ranging nature of Sack’s epistles, give them a familial resemblance. 

The book is edited by Kate Edgar, Sacks’ assistant and editor of several decades; she also contributes a brief preface which offers her perspective on Sacks’ compulsive writing process. Alas for her brevity; I believe she could offer a lot of insight on Sacks. But perhaps his letters will serve. Onward!

Preface and Editor’s Introduction

Sacks loved correspondence. He felt one ought to reply to letters, immediately if possible. He corresponded with, literally, thousands of people, from school children to Nobel laureates.  Sacks took pains to preserve his letters with carbon sets, drafts, or later, photocopies, though by no means does all his correspondence survive. But that part which does runs to about 200,000 pages, or about 70 bankers’ boxes.

Continue reading EP #16: Letters, Oliver Sacks

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The Winter’s Tale

An interesting one. The first part is a tragedy; the second transforms it into a comedy. There are a lot of loose ends that are, mostly, tied up in the penultimate scene, in a series of disclosures to Autolycus, offered for unclear reasons.

I find Autolycus are curious character — a villain who morphs into a trickster. Paulina is, in my view, the hero of the story, though it is disappointing that she is married off at the end after she declares she is going to morn for her dead husband. Apparently marrying everyone off is de rigueur for a comedy.

Continue reading The Winter’s Tale

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Snow Crystals, Kenneth Libbrecht

Snow Crystals: A Case Study of Spontaneous Structure Formation, Kenneth Libbrecht, 2022

This is Libbrecht’s magnum opus, at least on snow; this goes deep into the science. …and I love that he has ordered the references by date, so you can see the history of the science leading up to Libbrecht’s work.

Notes still in progress…

Continue reading Snow Crystals, Kenneth Libbrecht

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Macbeth

See course notes for general material about Macbeth.

I continue (post Othello) not to be terribly keen on the tragedies, but liked this more than Othello.

Precis of Macbeth

Macbeth encounters three witches who prophesy that he will become Thain of Cawdor and King of Scotland, and that Banquo’s descendants will be kings as well. Shortly thereafter Duncan appoints Macbeth as Thain of Cawdor, but announces that he will appoint his own son as crown prince. Macbeth is ambitious, and toys with the idea of murdering King Duncan. However, he has reservations – Duncan is his lord, a kinsman, and a guest in his household. However, Lady Macbeth – who appears to have summoned evil spirits to give her resolve – shames Macbeth into going forward with the plot. So Macbeth murders Duncan, and pins the murders on drunken watchmen (whom Lady Macbeth has used a potion to put to sleep), and then has them killed, and blames Duncan’s sons for the murder. 

Macbeth is crowned, but becomes increasingly unstable (as does Lady Macbeth( and paranoid). He seeks out the witches, who warn him to be wary of MacDuff, but assure him that no man borne of woman can kill him, and the he will not be defeated until Birnam Wood moves to Dunsinane Hill. After this, Macbeth goes on a bit of a killing spree,  arranging the murder of his friend Banquo (to eliminate his descendants the witches said would inherit the throne – except Banquo’s son escapes) and the family of the nobleman Macduff. Plagued by ominous visions—such as Banquo’s ghost appearing at a royal banquet—Macbeth’s grip on power loosens.

Meanwhile, Macduff and Duncan’s heir, Malcolm, raise an army in England and return to overthrow the usurper. Macbeth tries to avoid fighting Malcolm, but upon Malcolm’s pronouncement that he will take Macbeth captive and parade him about, Macbeth fights, and is slain and beheaded. Order and justice is restored. 

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Why Machines Learn

Why Machines Learn: The Elegant Math Behind Modern AI, Anil Ananthaswamy, 2024

November/December…, 2024

Context and Reflection

I am reading this book as part of a small book club: The 26-minute Book Club. This sort of book is really not my cup of tea — it is about the math with which various learning algorithms are implemented. I am, at my deepest, interested in the algorithms. Math is neither a strong point nor a deep interest — it seems more like magic to me: You generate a bunch of tautologies, define some terms and rules, and then elaborate it, and all of a sudden it enables you to do things.

I had imagined I would give this a one-meeting trial, and then bow out. However, I found the group (4 other people) quite pleasant, and composed of — barring the one person I know who invited me — interesting strangers that I would not otherwise get to know. And that, especially in retirement, is of great value.

Another advantage is that working through this book forces me to think about topics I would otherwise ignore: basic math including linear algebra, vectors, a little calculus, probability, gaussian distributions, Bayes’ Theorm. Perhaps it will prove tractable enough that I will feel emboldened to take on fluid dynamics, something that I suspect would help me better understand a whole range of natural patterns, from meteorological to geological…. Or perhaps I will figure out how to distill the ‘qualitative’ aspects that I am interested from the mangle of symbols. We shall see.

C1: Desperately Seeking Patterns

  • Konrad Lorenz and duckling imprinting. Ducklings can imprint on two moving red objects, and will follow any two moving objects of the same color; or two moving objects of different colors, mutatis mutandis. How is it that an organism with a small brain and only a few seconds of exposure can learn something like this?
  • This leads into a brief introduction to Frank Rosenblatt’s invention of Perceptrons in the late 1950’s. Perceptrons are one of the first ‘brain-inspired’ algorithms that can learn patterns by inspecting labeled data.
  • Then there is a foray into notation: linear equations (relationships) with weights (aka coefficients) and symbols (variables). Also sigma notation.
  • McCulloch and Pitt, 1943, story of their meeting, and their model of a neuron — the neurode — that can implement basic logical operations.
  • The MCP Neurode. Basically, the neurode takes inputs, combines them according to a function, and outputs a 1 if they are over a threshold theta, and a 0 otherwise. If you allow inputs to be negative and lead to inhibition, as well as allow neuroses [sometimes spell-correct is pretty funny] to connect to one another, you can implement all of boolean logic. The problem, however, is that the thresholds theta must be hand-crafted.
  • Rosenblatt’s Perceptron made a splash because it could learn its weights and theta from the data. An early application was to train perceptrons to recognize hand drawn letters — and it could learn simply by ‘punishing’ it for mistakes.
  • Hebbian Learning: Neurons that fire together, wire together. Or, learning takes place by the formation of connections between firing neurons, and the loss or severing of connections between neurons that are not in sync.
  • The difference between the MCP Neurode and Perceptrons is that perceptrons input’s don’t have to be 1 or 0 — they can be continuous. And they are weighted, and they are compared to a bias.
  • The Perceptron does make one basic assumption: that there is a clear, unambiguous rule to learn — no noise in the data. If this is the case, it can be proven that a perceptron will always find a linear divide (i.e. when there is one to be found).

C2: We are All Just Numbers

  • Hamilton’s discovery of quaternions, and his inscription on Brougham bridge in Dublin. i2 = j2 = k2 = ijk = -1 Quaternions don’t concern us, but Hamilton developed concepts for manipulating them that are quite important: vectors and scalars.
  • Scalar/Vector math: computing length; summing vectors; stretching a vector by scalar multiplication;
  • Dot product: a.b = a1b1 + a2b2 (the sum of the products of the vector’s components). The dot product (AKA the scalar product) is an operation that takes two vectors and returns a single number (a scalar). It’s a way to quantify how much two vectors “align” with each other — that is, the degree to which they point in the same direction.
    • E.g. Imagine pushing a model railroad car along some tracks. If you push in the exact direction that the tracks go, all the force you apply goes into moving the car; if you push at an angle to the tracks, only a portion of the force you apply goes into moving the car. This (the proportion of force moving the car along the tracks), is what the dot product gives you.
  • Something about dot products being similar to weighted sums, which can be used to represent perceptrons??? Didn’t understand this bit. [p. 36-42]
  • A perceptron is essentially an algorithm for finding a line/plane/hyperplane that accurately divides values into appropriately labeled regions.
  • Using matrices to represent vectors. Matrix math. Multiplying matrix A with the Transpose of Matrix B
  • So the point of all this is to take Rosenblatt’s Perceptron and transform it into formal notation that linearly transforms an input to an output.
  • “Lower bounds tell us about whether something is impossible.” — Manuel Sabin
  • Minsky and Papert’s book, Perceptrons, poured cold water on the field by proving that Perceptrons could not cope with XOR. XOR could only be solved with multiple layers of Perceptrons, but nobody knew how to train anything but the top layer
  • I am not clear on why failure to cope with XOR was such cold water…
    ➔ Later: It is because XOR is a simple logical operation; the inability of Perceptrons handling it suggested that they would not work for even moderately complex problems. Some also generalized the failure to all neural networks, rather than just single layer ones.
  • Multiple layers requires back-propagation…

C3: The Bottom of the Bowl

  • McCarthy, Minsky, Shannon and Rochester organized the 1955 Dartmouth summer seminar on Artificial Intelligence. Widrow attended this seminar, but decide it would take at least 25 years to build a thinking machine,
  • Widrow worked on filtering noise out of signals: He worked on adaptive filtering, meaning a filter that could learn from its errors. Widrow worked on continuous signals; others applied his approach to filtering digital signals. Widrow and Hoff — Adaptive filtering — invented Least Mean Squares algorithm.
  • Least Mean Squares is a method for quantifying error. What Widrow wanted to do was to create an adaptive filter that would learn in response to errors — this required a method for adjusting parameters of the filter so as to minimize errors. This is referred to as The Method of Steepest Descent, discovered by the French mathematician, Cauchy.
  • Much of the rest of the chapter introduces math for ‘descending slopes.’ dx/dy moves us along a gradient… the minimum will have a slope of zero. When we have planes or hyperplanes we need to take multiple variables into account so we have partial derivatives.

“If there’s one thing to take away from this discussion, it’s this: For a multi-dimensional or high-dimensional function (meaning, a function of many variables), the gradient is given by a vector. The components of the vector are partial derivatives of that function with respect to each of the variables.
…
What we have just seen is extraordinarily powerful. If we know how to take the partial derivative of a function with respect to each of its variables, no matter how many variables or how complex the function, we can always express the gradient as a row vector or column vector.

Our analysis has also connected the dots between two important concepts: functions on the one hand and vectors on the other. Keep this in mind. These seemingly disparate fields of mathematics-vectors, matrices, linear algebra, calculus, probability and statistics, and optimization theory (we have yet to touch upon the latter two) – will all come together as we make sense of why machines learn.”

…Reading Break…

  • So with an adaptive filter, you filter the input, and look at the error in the output, and feed that error back into the filter, which adjusts itself to minimize the error.
  • So first you need to be able to have an input where you already know what the true signal is, so that you can determine the error after the filter has transformed the input. How do you get that? ➔ Later: In the application we’re talking about, this is the training phase. Once the model is trained, you assume the characteristics of the noise will not change and the model will continue to work.
    One issue is whether the noise in the signal is always of the same sort — that is, if you train an adaptive filter on a bunch of inputs whose signals you know, will that give you a good chance of having a filter that can appropriately transform an unknown signal? The book uses the example of two modems communicating over a noisy line, and it makes sense (I think) that noise would have fairly uniform characteristics, at least for the session. But that seems unlikely to hold for everything.     Can we assume that the noise, in a particular situation, is always the same ,or at least has the same statistical properties?
    Suppose the source or nature of the noise in the signal changes over time? Well, you could embed some kind of known signal into the input (I imagine, say, a musical chord), and let the filter learn to adjust the output so that the known chord comes through.     But will a filter that preserves the chord also preserve the other information in the signal? I have no idea. I’d think it would depend a lot on (1) the nature of the signal, and (2) the nature of the noise.
  • I’m confused about the part about adding delays to signal… and I’m confused about how, in real life, you know what the desired signal is.
  • ➔ Later: Still not very clear on the noise issue, but I’m guessing it depends on what you’re applying it to. If the noise is varies in an unpredictable way for a particular application, then the filter/neuron simply won’t work and will produce gibberish.
  • Anyway, let’s assume we know the desired signal (and hence the noise/error) — how do we quantify the later? We don’t want to just add it up because it can have positive or negative values which would cancel one another out — instead, the errors are squared, and you take their mean to quantify the noise: the is called the Mean Square Error. It is also the case that squaring the errors exaggerates the effects of the larger errors, which seems like a desirable thing.
  • The math shows that the formula for the error associated with an adaptive filter is quadratic, meaning that it will be concave, and that thus the minimum error will be the minimum of the function. That can be found in multiple ways, either by finding the point at which the slope of the function is zero, or using gradient descent to find it.
  • A problem is that to do this, you need more and more samples of xn and yn and dn to calculate parameters, and you need to use calculus to calculate partial derivatives, and especially in high dimensional space this becomes burdensome (or impossible).
  • The solution was that Widrow and Hoff found a (IMO kludgy) way to just estimate the error without doing a lot of work.

weightNew = weightOld + 2 • <step-size> • <error-for-a-single-point>

  • This is called the Least Mean Squares (LSM) algorithm.
    ➔ Later: What they are doing is taking a single data point(a single input-out put pair) at random and using that to estimate the gradient and adjust the weight. Each update a new pair is randomly selected, and over time the algorithm noisily decreases the error. This is called Stochastic Gradient Descent. There is an alternative to this approach called mini-batch gradient descent that uses a randomly-selected set of points (e.g. 32 of them) for each update.

C4: In All Probability

  • The Monty Hall problem. There are three doors, one of which has a valuable prize behind it, and the others which have only goats. After you’ve picked door 1, Monty opens door 2, revealing a goat. You now have a change to change your pick — should you do that?
  • The answer. The answer is “yes.” For a long time this seemed counterintuitive to me (and Paul Erdos): revealing what is behind one of the doors should not change the probability of what is behind the other doors. What was tripping me up (ironically) is that I was ignoring the psychology. The key is that Monty is not opening a door at random: he knows what is behind each door, and in particular, he will not open a door that has the prize behind it (as that would destroy the game). So when Monty opens door 2, he is sometimes providing information about both door 2 and about door 3.
  • Let’s suppose I’ve picked the first door. There are three cases:
    (1) Pxx — if I have the correct door, Monty can open either of the others.
    (2) xPx — If a goat is behind 1, and P behind 2, Monty can only open 3
    (3) xxP — If a goat is behind 1, and P is behind 3, Monty can only open 2
    In 2 of these 3 cases, switching to the remaining unopened door gets me the prize. Monty has change the prior probabilities, and so we much re-evaluate.
  • This argument will hold for any number of doors, because Monty always knows where the car is, and since he will avoid opening that door, every door he opens changes the priors — i.e. gives additional information about the unopened doors.
  • Later: If we construct a different version of the problem, where, before Monty can pick a new door, an earthquake strikes and door 2 happens to collapse, revealing the goat, there is no reason to change (or not change) your pick. The revelation of the goat behind door 2 does not give us further information about what is behind any of the other doors, since the earthquake’s ‘revelation’ was truely a random event.
  • Bayes Theorem history. Interestingly, Thomas Bayes’ essay describing his approach was only presented to the Royal Society in 1763, two years after his death, by his friend Richard Price (who later scholars believe made substantive contributions, although Price attributed it all to Bayes).

P (X-is-true | given Evidence-for-X is positive)
IS EQUAL TO
P-X-in-the-world • How-strong-the-evidence-is (e.g. the accuracy of the test)
————————————- (DIVIDED BY) —————————————————-
(P-X-in-the-world • probability of a true positive)
• (1 – P-X-in-the-world) • (1 – probability of a true positive)

OR

The empirical probability in the world * the predictive accuracy given evidence
————————————————————————————————————————–
the likelihood of the world producing that evidence
(=sum of probabilities of all ways of producing that evidence)

…Reading Break…

  • Machine Learning is inherently probabilistic because there are an infinite number of hyperplanes that can discriminate between a learned alternative, and it has settled on one of them for no particular reason. Other factors that make ML less than accurate are that the data itself may have errors, and that the amount of data drawn upon is limited. Later: And we must keep in mind that ML is only minimizing error — whether the result has enough signal to be useful is an empirical and domain-dependent issue.
  • Distinction between theoretical probability and empirical probability (e.g., theoretical probability of a fair coin coming up heads is 50%; empirical probability of a fair coin coming up heads depends on actually doing it, and it will approach but not reach the theoretical probability as one increases the number of empirical samples.
  • Aside: There is also the issue of the degree to which real-world events are actually expressions of mathematical distributions. It seems elegant to assume that, but is it really so?
  • The case of a coin flip is an example of a Bernoulli distribution. It has only two values, a and b, and can be characterized by a probability p that such that p is the probability of a, and (1–p) is the probability of b.
  • Distributions with a mean and a variance (aka standard deviation). Now consider the case where you have N>2 outcomes, each with their own probability. This is distribution can be characterized by a mean and standard deviation — the mean (aka the expected value) is the sum of the values of each outcome multiplied by their probabilities, and the standard deviation is the square root of {the sum of the squares of the (deviations of each value from the mean)} — or the sum of the absolute value of each values difference from the mean. We can talk about the distribution as a whole in terms of its probability mass function.
  • So far we’ve been talking about variables with discrete values, but we can instead talk about variables with continuous values. Here we can’t talk about the probability of a particular value because there are an infinite number of values and the probability of any single perfectly precise value is zero. So, instead, what we do is talk about the probability of a value occurring within particular bounds: this is called the probability density function. [Aside: But wouldn’t it be possible to do some calculus like move where we look at what happens as a finite interval approaches zero?]
  • The important point is that whether we have a variable with discrete or continuous values, we can use well-known and analytically well understood functions to characterize the distribution.
  • Machine Learning. Let’s being with a set of labeled data points: y is a label that has two values, and x is an instance of the data. y is categorical; x is a vector with N components. This data set can be represented as a matrix: y1, x11, x12, x13 .. x1n and so on for y2, y3, etc. Each component of x is a feature that the algorithm will use to discriminate which y x belongs to.
  • Now, if you knew the underlying distribution P(y, x), you could determine the probability of y=A given x, and the probability of y≠A given x, and use the highest probability to assign the label. If this were the case, this is what would be termed a Bayes Optimal Classifier. [Aside: I’m a little unclear on this — it seems like it’s dependent on a particular situation, and so it seems odd to give it this sort of name.]
  • But usually you don’t know the underlying distribution, and so it must be estimated. Often it is easier to make assumptions about the underlying distribution (Bernoulli? Gaussian?), but it is important to keep in mind that these are idealizations chosen to make the math easier.
  • Aside: A Gaussian distribution is defined as being symmetric with respect to a single mode (which is also the mean and median), with asymptotic tails that never reach 0.
  • There are two approaches to estimating distributions:
    One is MLE or Maximum Likelihood Estimation, involves selecting a theta (that is a distribution with particular parameters indicated by theta) that maximizes the likelihood of observing (generating?) that labeled data you already have.
    In the text, MLE is exemplified by imagining a set of data about two populations’ heights, labeled short or tall, and that each population has a gaussian distribution, and thus that all the data will be best modeled by a combination of the two distributions. ???But is that still a gaussian distribution? And what is the rational for choosing a gaussian distribution rather than some other distribution???
    The other is called MAP, for Maximum A Posteriori estimation. As best I can tell, this involves estimating the distribution based on our experience of the world and representing it as a function. Then you set the derivative to zero, and solve that equation (after checking to be sure that you’ve got the maximum rather than the minimum). If this approach does not yield a ‘closed’ solution (most of the time), you can take the negative of the function and use gradient descent to find its minimum.
  • MLE vs MAP. So MLE tries to find the maximum using the data, and MAP tries to find the maximum of a distribution you’ve guessed at. MLE works best when there’s a lot of data; MAP works best when there isn’t much data you can make a reasonable guess about the underlying distribution).
  • The Federalist Papers example: which unattributed papers were written by Madison and which by Hamilton. A first approach was to analyze the length of sentences in papers know to be written by each author, and use the mean length and the standard deviation to discriminate: unfortunately the means and SDs for each author were almost identical. Later, someone suggested using the frequency of word use, and, in particular, function words tended to reliably discriminate between the known works of the two authors: this was then used to predict which of the unattributed essays were written by which author.
  • Aside: It is not clear to me whether the achievement of Mosteller and Wallace was due to the use of Bayesian reasoning, or to the realization the word choice was a very good discriminator between the authors. ➔ LATER: Consensus among scholar from many fields indicates, according to chatGPT, that their work did indeed validate the use of Bayesian inference, as well as creating a new approach to linguistics, and indicating the ways in which computers could be used.
  • The Penguin example.
  • A trick that statisticians use is to assume that the distributions for each feature under consideration are independent — seems like a bit of a leap, but it appears to work and it makes the math easier and requires less data.
  • Naive or Idiot Bayesian classifier.

… reading break…

C5: Birds of a Feather

  • The 1854 Snow Cholera Epidemic map
  • Voroni diagrams and nearest neighbors
  • When you represent something as a set of N-dimension vectors, the vectors can be considered as points in an n-dimensional space, and you can use the NN algorithm to compute their neighbors, and devise non-linear boundaries between labeled points.
  • However, as the dimensionality of the space increases, there’s a problem in that the space, in most regions, becomes very sparsely populated…

… reading break…

C6: There’s Magic in Them Matrices

  • Principal components analysis (PCA)  involves reducing the dimensionality of a data of space in such a way that the retained dimensions capture most of the variance.
  • This assumes that dimensions that do not contain much variance are unimportant; and that the dimensions that capture a lot of variance  actually are important. This may not be true.
  • A few notes on Vectors
    • A vector with six components as a dimensionality of six. That is, it can be represented by a single point in a six dimensional space.)
    • Multiplying a vector by a square matrix of the same dimensionality, basically means changing the magnitude and orientation of that vector in the same space.
    • An eigenvector of a matrix is a non-zero vector such that, when it is multiplied by the matrix, it does NOT change its orientation, only its length. The length of the eigenvector is called the eigenvalue.
    • Basically, for any matrix of dimension N, you can find N eigenvectors that, when multiplied by the matrix, do not change orentation, but only change magnitude (that magnitude is the eigenvalue)
    • For a symmetric matrix, the eigenvectors lie along the major and minor axes (hyperaxes) of the (hyper)ellipse.
    • There is a nice visualization on pages 186-187 of what it means to find the eigenvectors
  • Centering a matrix means taking the mean for a particular dimension (feature) and subtracting that from each  individual value for that feature: that transforms each feature’s value into how much it deviates  from the mean. This is also called “mean corrected” matrix.
  • If you multiply a mean, corrected matrix by its transpose, you get a square matrix where the diagonal values showed the variance for each feature, and the off diagonal values show the covariant between pairs of features. This is called the mean-corrected ovarian matrix. 
  • Now, if you compute the eigenvectors for that matrix, the eigenvalues will allow you to see where most of the variance is and do a principal components analysis, reducing the dimensionality of the matrix.

… reading break…
Actually, a break of about six weeks
during which I was traveling and missed the group meetings.
I may go back and summarize the missed material… or I may not not.

C7: The Great Kernel Rope Trick

The first part of the chapter talks about developing an algorithm for finding the optimal hyperplane that separates two groups. The improves on what a perceptron does — it finds a hyperplane, but not necessarily (and probably not) the optimal one. The algorithm here works by maximizing the margin or border between the closest points.

The kernel trick refers to computing the dot products of vectors in higher dimensional spaces by computing the dot products of lower-dimension vectors that are mappings from the original vectors. This is significant because one way of classifying sets of data that overlap is by projecting them into higher dimensional spaces where they don’t overlap, and figuring out the optimal hyperplane there — except the kernel trick allows you to calculate the hyperplane without doing in the high-dimensional space. The term used for this technique is Support Vector Machines.

C8: With a Little Help from Physics

Hopfield Networks. Networks that have local energy minima that represent ‘memories.’ When a new input comes that produces something close to a particular memory, that pattern is likely to ‘decay’ into the stable pattern that represents the memory.

Hopfield’s work showed that the brain could be modeled as a dynamic system.

C9: The Man Who Set Back Deep Learning (Not Really) – TBD

C10: The Algorithm that Put Paid to a Persistent Myth – TBD

C11: The Eyes of a Machine

  • Hubel and Weisel’s work on the visual cortex of cats beginning with the serendipitous discovery of edge detection. They described a neural architecture in which a hierarchy of cells detected increasingly complex visual features based on multiple simple detectors feeding into more complex detectors.
  • This architecture was used in neural nets…
  • The convolution operation enabled the mathematical mimicking of detectors using the convolution matrices (aka filters).
  • Cu Lin figured out how to create networks that could learn their own filters…

C12: Terra Incognita – TBD

Epilogue

Views: 6

Four Billion Years and Counting…

Four Billion Years and Counting: Canada’s Geological Heritage. Produced by the Canadian Federation of Earth Sciences, by seven editors and dozens of authors. 2014.

November-December, 2024.

I am reading this with CJS. It is a nice overview of regional geology, and it is nice that all the examples come from Canada, and at least some of the discussion is relevant to Minnesota Geology. The book is notable for its beautifully done pictures and diagrams.

Continue reading Four Billion Years and Counting…

Views: 14

Othello

November 2024

Reading as part of the Fall 2024 Shakespeare course — see general notes for more.

Although its a famous play, and does indeed contain some striking things — particularly Iago’s manipulation of Othello, and also the use of the hankerchief as symbol of fidelity and betrayal – I was not that keen on this play. Give me some comedy, or at least a little more magic!

Precis of the play

Othello, a famous general fighting for Venice, has just married Desdemona, to the dismay of her father. Othello is black, and an outsider, and knows little of the customs or society of Venice – but he is valued due to his military prowess, especially as the Turks seem about to attack. He has chosen the polished and bookish Cassio as his lieutenant, much to the distress and anger of Iago, who has spent his life in the field and believes he has earned the postion. Iago decides to get revenge, and aims to destroy Cassio and Desdemona and, through her, Othello. 

After this, the play unfolds in a straightforward way. Iago subtly raises questions about Desdemona’s faithfulness – all the while pretending that he is reluctant to speak and is unsure of the truth of what he is saying – and in a famous scene transforms Othello’s trust of Desdemona into suspicion, suggesting that she is having an affair with Cassio. Iago is one of Shakespeare’s most famous villians – Coleridge referred to him as having “motiveless malignity.”

Othello wants visible proof, and here Desdemona’s hankerchief comes into play. It was her first gift from Othello, and it was woven by a fortune teller with magical properties. Iago secrets Desdemona’s hankerchief (which she had lost and Emilia found and given to Iago) in Cassio’s quarters. Cassio finds the hankerchief and gives it to the courtesian Bianca to copy – Othello watches this from a distance, and believes it proof of Desdemona’s infidelity. Othello orders Iago to kill Cassio, and Othello strangles Desdemona. When it is revealed that Desdemona was innocent, Othello kills himself.

Views: 0

Through the Language Glass

by Guy Deutscher

October 2024

This is an excellent book; interesting well-documented science, and some beautiful and erudite writing as well. The basic argument — that grammar determines what must be specified, rather than what can be specified, and in that manner instills certain habits of mind that effect how people see the world — seems correct, if not quite living up to the subtitle of the book: Why the World Looks Different in Other Languages.

Perhaps the most interesting and fun part of the book was to be introduced to languages that work very differently from English: The Mates language (in Peru) that requires speakers to specify whether the fact they report is based on personal observation, indirect evidence, or hearsay; and the Australian language that has no egocentric prepositions, but requires all positional information to be reported in terms of the cardinal directions, thus requiring their speakers to always be oriented.

This book was a pleasure to read. I plan to seek out other work by this writer. 

Contents

Front Matter

On whether languages reflect the characteristics of their speakers, he writes:

Many a dinner table conversation is embellished by such vignettes, for few subjects lend themselves more readily to disquisition than the character of different languages and their speakers. And yet should these lofty observations be carried away from the conviviality of the dining room to the chill of the study, they would quickly collapse like a soufflé of airy anecdote-at best amusing and meaningless, at worst bigoted and absurd.

— p. 2

The basic argument of the book is this:

The effects that have emerged from recent research, however, are far more down to earth. They are to do with the habits of mind that language can instill on the ground level of thought: on memory, attention, perception, and associations. And while these effects may be less wild than those flaunted in the past, we shall see that some of them are no less striking for all that.

I think it is correct, but that the subtitle of the book – Why the World Looks Different in Other Languages – is a bit of an exaggeration.

C1-5: <Reprise of history and status of color terms>

C1: Naming the Rainbow

This chapter reprises now-unknown work by William Gladstone (now remembered as an English prime minister) on Homer and his writings, and focuses in on particular on one chapter in Gladstone’s monumental 3,000 page work: a chapter on Homer’s use of color terms.

Gladstone’s scrutiny of the Iliad and the Odyssey revealed that there is something awry about Homer’s descriptions of color, and the conclusions Gladstone draws from his discovery are so radical and so bewildering that his contemporaries are entirely unable to digest them and largely dismiss them out of hand. But before long, Gladstone’s conundrum will launch a thousand ships of learning, have a profound effect on the development of at least three academic disciplines, and trigger a war over the control of language between nature and culture that after 150 years shows no sign of abating.

Gladstone notes that Homer uses color terms in odd ways — the famous “wine dark sea” (really “wine-looking” sea) being just one example.

Mostly Homer, as well as other Greek authors of the period, use color very little in their descriptions: mostly they use black or white; terms for colors are used infrequently and inconsistently. For example, the only other use of “wine-looking” is to describe the color of oxen.

Gladstone’s fourth point is the vast predominance of the “most crude and elemental forms of color”-black and white-over every other. He counts that Homer uses the adjective melas (black) about 170 times in the poems, and this does not even include instances of the corresponding verb “to grow black,” as when the sea is described as “black-ening beneath the ripple of the West Wind that is newly risen.” Words meaning “white” appear around 100 times. In contrast to this abun-dance, the word eruthros (red) appears thirteen times, xanthos (yellow) is hardly found ten times, ioeis (violet) six times, and other colors even less often.

C6: Crying Whorf

This chapter describes the origin, rise and fall of linguistic relativity. Sapir is depicted as respectable but making over-stated claims; Whorf comes across as a charlatan, for example, making claims to have deeply studied Hopi, when he only had access to a single informant in New York – and making broad claims that are entirely wrong (e.g. that the Hopi language does not have a future tense). 

Deutscher traces the origin of linguistic relativity to Wilhelm von Humboldt in 1799,  whose “linguistic road to Damascus led through the Pyrennes.” Deutscher encountered the Basque language, and found that it was radically different from the languages linguists tended to study. He then sought out other ‘more exotic’ languages, which he found by going to the Vatican library and studying the notes of Jesuit missionaries to South and Central America: “…Humboldt was barely scratching the surface. But the dim ray of light that shown from his materials felt dazzling nonetheless because of the utter darkness in which he and his contemporaries had languished.” p. 135 Although Humboldt’s ideas led to linguistic relativity, it should be noted that he had a much more nuanced and correct view: In principle, any language may express any idea; the real differences among languages are not what they are able to express but in “what it encourages and stimulates its speakers to do from its own inner force.” But this view was not carried forward, and instead: “The Humboldtian ideas now underwent a process of rapid fermentation, and as the spirit of the new theory grew more powerful, the rhetoric became less sober. ”

All that said, Deutscher argues it is a mistake to dismiss the idea that language has no influence over thought. But rather than taking the strong case the language constrains thought, he instead argues the habits of language may lead to habits of mind. In the case of the influence of language, and refers to the idea that Boas introduces and that Jakobson crystalized into a maxim: “Languages differ in what they must convey, and not in what they may convey.”

Phrases I like

“…has still the power to disturb our hearts.” [Sapir, referring to Homer, Ovid, etc.] p. 129

“[His] linguistic road to Damascus led through the Pyrennes.” p. 134

“…Humboldt was barely scratching the surface. But the dim ray of light that shown from his materials felt dazzling nonetheless because of the utter darkness in which he and his contemporaries had languished.” p. 135

Views: 4

Henry V

October 2024

Reading as part of the Fall 2024 Shakespeare course — see general notes for more.

Precis

Background: Henry V is son of Henry IV, who obtained the throne by usurping it from Richard II – this means that there is some feeling that neither Henry is a legitimate ruler. Before becoming King, Henry V was a wild youth, dissipated and engaged in lascivious acts. But on his father’s death, Henry becomes a serious and mature ruler. 

The play opens with a chorus praising Henry as an unmatched warrior King. But then, the next act depicts the Archbishop of Canterbery revealing his plan to avert a harsh tax on the Church by legitimizing and encouraging Henry’s plans to invade France and take its throne.

Act 2 begins with the chorus describing the desire of the young men of England to pursue honor by participating in this war. The first scene following this shows conversation – and almost a fight, between three old soldiers who are erstwhile companions of Henry – the depicts honor as the least of their concerns. The second scene of Act shows the unmasking of traitors among the Lords who support Henry. 

Act III. The war has begun. The English army, led by Henry, lays siege to the French town of Harfleur. Before the gates, Henry delivers a rousing speech (“Once more unto the breach…”) to rally his soldiers; the siege takes a heavy toll, and the town eventually surrenders.

 In Act IV, Henry has arrived at Agincourt; his army is weary and outnumbered. Henry, in disguise, walks among his soldiers at night, listening to their fears and doubts. In the morning, Henry delivers his famous St. Crispin’s Day speech, which lifts the English spirits.

In Act V, the battle of Agincourt is won by the English. Henry returns to England, where the victory is celebrated, and then to France, to negotiate the final terms of the peace. There he woos a reluctant Princess Katherine, which marriage will solidify his claim to the thrown. The play ends with a reminder that Henry will die, and things will unravel.

Structure of the Play

1. Invasion Groundwork

  • Prologue: Chorus wishes for a greater stage, and tells audience to use its imagination.
  • 1.1 Theological Justification
    Bishops of Canterbury and Ely discuss bill that will seize money from the search; they plan to avoid it by providing a theological justification for Henry V’s claim to France, and thus his invasion. They also mention how much Henry V has changed since his father’s death: “And so the Prince obscured his contemplation / Under the veil of wildness / which, no doubt, grew like the summer grass, fastest by night / Unseen yet crescive in his facility”
  • 1.2: Bishops assure H of invasion’s morality; tennis ball mock
    Henry V     invites the Bishops to give an explication of the law regarding his claims to France, and they do so, even as Henry repeatedly asks them to be honest about it. Henry also raises the possibility of Scotland invading should he go to France, but the Bishops argue that that can be defended against. Finally, after deciding that he will take control of France, by invasion if necessary, he invites in the French ambassadors, who, in a message from the Dauphin, present him with a barrel of tennis balls. Henry says he will play play a set in France, and will “strike his father’s crown into the hazard.” Exter, uncle to the King, is present and speaks a line or two. 

2. Preparations for War

Elimination of traitors; introduction of common solidiers; preparation by France

  • Chorus: The chorus describes the excitement in England about the coming war – They sell the pasture now to buy the horse – and provide notice that three nobles – Cambridge, Scroop, and Grey –have become traitors. 
  • Bardolph, Henry’s former tavern companion, prevents two solidiers – Nym and Pistol – from fighting over Hostess Quickly, Pistol’s wife, and requires them to become friends. They are interrupted by news that Falstaff is dying. 
  • Cambridge, Scroop, and Grey are brought into Henry V’s presence, not realizing that he knows they are traitors, and are asked about whether Henry should show mercy to someone who has spoken against it. They say no, and override Henry’s wishes to show clemency. He the reveals that he knows of their betrayals, and they are all condemned to death.
  • Falstaff has died. Bardolph, Nym, Pistol and Hostess Quickly morn his death. The three men prepare to depart for France, and Pistol bids Hostess Quickly goodbye. 
  • The King of France and the Dauphin plan for the defense of France against Henry – the King is cautious, the Dauphin is not, being contemptuous of Henry, and ignoring warnings about Henry’s new ethos. Exter enters as ambassador, and asks the King of France to yield to Henry, and returns insults to the Dauphin. The King says he will answer in the morning: “A night is but small breath and little pause / To answer matters of this consequence.”

3. Invasion, part 1: Success as Harlefor surrenders

Initial success: Harlefor surrenders; commoners show cowardance; 5:1 odds

  • Chorus: Describes the departure of the English navy: …
         Play with your fancies and in them behold, 
         Upon the hempen tackle, shipboys climbing.
         Hear the shrill whistle, which doth order give 
         To sounds confused. Behold the threaden sails, 
         Borne with th’ invisible and creeping wind, 
         Draw the huge bottoms through the furrowed sea 
         Breasting the lofty surge. O, do but think 
         You stand upon the rivage and behold
         A city on th’ inconstant billows dancing…
    and notes that the French King offered the hand of his daughter and some small unprofitable dukedoms – this offer is disregarded (and is reported only after the navy is described as being launched). 
  • The invasion begins: “Once more into the breach, dear friends, once more / Or close the wall up with our English dead.” Henry makes a speech as the prepare to advance.
  • The three soldiers show their cowardence in trying to withdraw from the assault – they are driven back to it by Captain Fluellen. Captain Fluellen then engages in discussions and disputations with three other Captains: Glower, Jamy, Macmorris. [Not quite sure of the point of this scene]
  • Henry gives a speech before the gates of Harlefor, saying it is their last chance, and that they will be to blame if they do not surrender and the city is ravaged:

I  will not leave the half-achieved Harfleur 
Till in her ashes she lie buried.
     The gates of mercy shall be all shut up, 
     And the fleshed soldier, rough and hard of heart 
     In liberty of bloody hand, shall range 
     With conscience wide as hell, mowing like grass
     Your fresh fair virgins and your flow’ring infant
     What is it then to me if impious war, 
     Arrayed in flames like to the prince of fiends, 
     Do with his smirched complexion all fell feats 
Unlinked to waste and desolation?

  • Katherine, Princess of France, has one of her maids teach her English. [The scene appears to be presented in French – would the audience have understood???]
  • The governor surrenders the town, and Henry spares its citizens.
    [Neither of these things happened in history.]
  • The French nobles are embarrassed by Henry’s successful invasion. But they convince themselves they will triumph, and send an ambassador to ask what ransom Henry will offer when he is captured.
  • Ancient Pistol has distinguished himself and pleads with Captain Fluellen for the life of Bardoph, who has been sentenced to death for stealing. His plea is rejected, and he departs with a curse. Captain Fluellen talks with Henry, and mentions Bardolph, whose execution Henry upholds. The French Ambassador, Mountjoy arrives to enquire about Henry’s ransom: Henry says ‘nothing but my body.’
  • The French nobles, confident of their victory on the eve of the battle, boast and banter among themselves.

4. Invasion, part 2: Triumph at Agincourt

Eve of  battle; Henry & Williams & Fluellen; Pistol demands ransom;  triumph at Agincourt

  • The Chorus draws a beautiful picture of the two armies the night before the battle, camped across from one another, awaiting the morning. The French confident, the English anxious… but with Henry moving among them to raise morale.
         Now entertain conjecture of a time
         When creeping murmur and the poring dark
         Fills the wide vessel of the universe
    From camp to camp, through the foul womb of night,
         The hum of either army stilly sounds, 
         That the fixed sentinels almost receive 
         The secret whispers of each other’s watch.
         Fire answers fire, and through their paly flames 
         Each battle sees the other’s umbered face
  • Henry walks though his camp, in disguise. He encounters Pistol, overhears a conversation between Grover and Fluellen that leaves him impressed with the Welshman’s quality, and argues with a soldier – Williams – about the King’s responsibility for the spiritual fate of his solidiers – they exchange gloves with the intention of dueling later. Last, Henry laments his father’s usurpation of Richard II’s throne. 
  • The French nobles, about to fight, lament that the English are so few and weak.
  • Henry gives a speech of encouragement again. Responding to someone wishing for more men, Henry says he does not wish for more, and furthermore that those who do not wish to figtht will be furnished with passage home. ‘I do not wish to share the honor more than I have to,’ is his sentiment.
  • The ambassador, Mountjoy, comes again to negotiate a ransom, which Henry refuses. 
  • A French soldier surrenders to Pistol, who threatens to kill him unless he provides a ransom. 
  • The French nobles recognize that they have been defeated, and, ashamed, vow that they will die in battle. 
  • Henry hears of the deaths of York and Suffolk; unsure of whether he had victory, when he hears a French call to arms he orders all French prisoners killed. 
  • Fluellen in conversation with Grover compare Henry to Alexander the Great. Montjoy arrives with the French surrender. Williams appears with the glove, which Henry does not acknowledge; but Henry give Fluellen the other glove and sends him after Williams, and then sends others after Fluellen to prevent a full fight. 
  • William encounters Fluellen, and strikes him. The other men arrive and prevent an escalation. Henry arrives and explains what happens and ‘pardons’ Williams, and has his glove filled with crowns. [I’m not quite sure of what happens after this, especially between Williams and Fluellen—Fluellen seems to do an about face and now thinks well of Williams.] The scene ends with the numbers of the dead being announced, and Henry giving credit for the victory to god.

5. Treaty signed, and marriage

Treaty signed and Princess Kate agrees to marry Henry; Fluellen gets revenge

  • Chorus: Brings Henry back to England where he and his victory are celebrated, and then back to France where the treaty recognizing Henry as sovereign will be signed. 
  • Fluellen, via use of a cudgel, forces Pistol to eat a leek to avenge his insults; Pistol decides to return to England where he will wear his cudgel wounds to pretend to be a wounded soldier. 
  • Henry and the King of France meet, and Henry delegates negotiation to his nobles while he woos Princess Katherine – she consents to marrying him, but without, it seems to me, much understanding or enthusiasm. Henry rides roughshod over her preference not to kiss before the wedding: “O Kate, nice customs curtsy to great Kings.”

A few notes

Throughout the play we see that Henry has separated himself from his old base companions: Falstaff dies (and was previously exiled); Henry allows Barloph to be hanged for stealing; the Bishops remark on how Henry has changed.

Deception: Not much. Henry goes in disguise among his troops. Henry incident with William. Henry does not tell Fluellen what is up when he sends him after William. Henry uses lots of flowery words which it is unlikely Princess Kate will understand.

??? Is Henrys order to kill the prisoners proper?

??? Does Henry really think the war is just?

??? Henry says that if they do not surrender, governor will be responsible for soldiers’ depredations.

Quotes I like

Now entertain conjecture of a time
When creeping murmur and the poring dark
Fills the wide vessel of the universe.
From camp to camp, through the foul womb of night,
The hum of either army stilly sounds, 
That the fixed sentinels almost receive 
The secret whispers of each other’s watch.
Fire answers fire, and through their paly flames 
Each battle sees the other’s umbered face;

 I will not leave the half-achieved Harfleur 
Till in her ashes she lie buried.
The gates of mercy shall be all shut up, 
And the fleshed soldier, rough and hard of heart
In liberty of bloody hand, shall range
With conscience wide as hell, mowing like grass:
Your fresh fair virgins and your flow’ring infant
What is it then to me if impious war,
Arrayed in flames like to the prince of fiends,
Do with his smirched complexion all fell feats
Enlinked to waste and desolation?

Play with your fancies and in them behold,
Upon the hempen tackle, shipboys climbing.
Hear the shrill whistle, which doth order give
To sounds confused. Behold the threaden sails,
Borne with th’ invisible and creeping wind,
Draw the huge bottoms through the furrowes
Breasting the lofty surge. O, do but think
You stand upon the rivage and behold
A city on th’ inconstant billows dancing…

Othello

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Measure for Measure

October 2024

Reading as part of the Fall 2024 Shakespeare course — see general notes for more.

Precis of Measure for Measure

The Duke of Vienna (aka Friar Lodowick) plans to travel abroad, leaving young Angelo as regent, empowered to enforce laws that the Duke has allowed to go fallow. However, the Duke really plans to remain in Vienna, disguised as a Friar, to see how Angelo carries out his duties. Angelo immediately shuts down many of the houses of prostitution, and condemns Claudio, a man who has only erred in having sex after handfasting but before the banns were read, to be executed. This seems extreme and disturbs many: Escalus, a judge; the Provost, who runs the jail; and Lucio, a friend of Claudio and ne’r do well Viennese noble. Lucio seeks out Isabella, Claudio’s sister, who is in the process of joining a convent, to persuade Angelo to be merciful, after protests by Escalus and the Provost fail. Angelo speaks with Isabella, steadfastly refusing, until she asks him to look into his heart and see if has not had similar feelings that led Claudio to his current straits.  Angelo wavers, and tells her to return tomorrow. In a soliloquy he reveals that is attracted to her virtue, and wishes to have sex with her. In a second interview he tells her he’ll free Claudio if she’ll sleep with him. She refuses, and he tells her if she does not relent he’ll torture Claudio to death.

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The Tempest

October 2024

This is not being read as part of the Shakespeare course; there is a week’s break for midterms, and, as CT and I are discussing S’s plays as I read them for the course, we are adding in the Tempest for this playless week.

That said, here is a link to the Shakespeare course notes: general notes

Precis of The Tempest

Before the play: Duke Prospero deposed, with young Miranda cast adrift, but Gonzalo secreted food, water and books as gifts. Now magician-ruler of the isle, he’s bound Ariel, enslaved Caliban, and his magically-raised storm has brought his enemies to him. They are Sebastian, his usurping brother; King Alonso, who went along, and Antonio who has learnt sibling-treachery from Sebastian. Innocents too, are also present: Prince Ferdinand, Alonso’s son, and old Gonzales, faithful one. 

     The play itself: the travelers are cast separately, each group to take a journey. Ferdinand will Miranda woo; Caliban will revolt, but rue; Alonso’s overthrow is thwarted. Prospero has a change of heart, forgives those arrayed against him, All return, but Caliban, to rule Naples and Milan. 

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Shakespeare course, Fall 2024

I’m taking an introductory Shakespeare course at the U of Minnesota this fall.

Week 1: Intro & Life of Shakespeare, 1

The course looks very promising. The professor, Katherine Schiel, is a Shakespeare scholar and in particular researchers the life of Shakespeare’s wife. The course focuses on literature (rather than TV and move adaptations), and the syllabus shows that we will cover eight of Shakespeare’s works, including the sonnets. I was also struck by how much more talkative and friendly the other students in the course are – both in engaging in in-class discussion, and in engaging with me.

These are more general notes; I also notes on each play read that can be found from the “About this site” page.

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Much Ado about Nothing

October 2024

Reading as part of the Fall 2024 Shakespeare course — see general notes for more.

Precis of Much Ado About Nothing

Don Pedro and his cohort arrive at the residence of Leonato, governor of Messina, who has a daughter Hero and a niece, Beatrice. Beatrice, a witty and assertive woman has long been in a “merry war” of words with returning soldier Signor Benedict. Don Pedro decides to play match maker and deceives them both, leading each to think the other is in love with them, and so Beatrice is matched with the marriage-shy Benedict. At the same time, her cousin, Hero, is on course to wed Count Claudio, hero of the recent war, until she is framed by the villainous Don John, brother to Don Pedro. Don John’s ruse succeeds for some, pitting Beatrice and Benedict against Count Claudio, Leonato and others, until Dogberry, a crazy constable, exposes the deception carried out by John’s henchmen, Borrachio and Conrade, and everyone is reconciled and married. 

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