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Why is Merlin in That Hideous Strength?
Posted on January 14, 2015 by Brenton Dickieson
I am writing a paper on C.S. Lewis and the Arthurian tradition for Sørina Higgins' collection, The Inklings and King Arthur. As I trawl through the materials one common theme keeps coming back: How do we explain the sudden appearance of Merlin in That Hideous Strength (1945)?
Part of the ultimate answer is this: "Charles Williams and J.R.R. Tolkien." Williams and Tolkien, both writers struggling with Arthurian stories and both close friends of Lewis—they are the reason that Merlin appears. They influence the way that Lewis shaped his science fiction writing during WWII.
This claim won't shock readers of the Inklings or C.S. Lewis scholars; it was from Sørina that I first heard That Hideous Strength (THS) called "The Charles Williams novel by C.S. Lewis." While the first Ransom book, Out of the Silent Planet (1938), was an H.G. Wells space fantasy—what he and Tolkien called a Romance—and Perelandra (1943) became a new myth like what Milton did in Paradise Lost. THS really sets itself apart from the other books as a supernatural thriller. It is dark, eerie, peculiar, a clear precursor to Orwell's 1984 (1948), and for some reason includes the great wizard Merlin. It is certainly in the stream of Charles William's work. Read Williams' Descent Into Hell (1937) and some of his Arthuriana, then read That Hideous Strength. You'll see what I mean. My chapter in the book is going to work out that link (if I am successful!).
But Williams and Tolkien are not Lewis' only influence. One hint that is often missed is the subtitle to That Hideous Strength: "A Modern Fairy-Tale for Grown-Ups." That Lewis has to include a preface defending the subtitle makes the reader wonder why he even bothered. THS doesn't feel like a fairy tale; it's missing all the things we would expect, including evil stepmothers, wrinkled crones, knights in shining armour, breadcrumb pathways, woodcutter cottages, and, well…, it's missing fairies. There are no fairies in That Hideous Strength.
While that last statement isn't exactly true, as we'll see, the average reader who ignored the subtitle would feel about the same as I did: This Merlin thing doesn't fit. I think, though, that Lewis' little paratextual clue, "A Modern Fairy-Tale for Grown-Ups," sets the reader up in a key way to expect the kind of book that Lewis has written. In this case, the subtitle takes us back—as so many other things in Lewis' writing do—to George MacDonald.
While George MacDonald is a relatively famous father of the faërie tradition and a well-loved children's author in the day before children were a market, MacDonald was also tremendously influential to C.S. Lewis. As I explain in my article, "Be Careful What You Read," it was George MacDonald's first prose book, Phantastes (1858), that Lewis encountered by accident one day at the train station. It erupted into his mental life, "baptizing his imagination" and preparing the way for his life as both author and Christian.
George MacDonald wrote a whole host of books that explored all aspects of faërie. But here's the link to That Hideous Strength. Depending on your edition, you may not have noticed the subtitle to Phantastes. It's easy to miss, especially for those of us using a digital copy. But the subtitle is significant. The full title of this 19th century classic is Phantastes: A Faerie Romance for Men and Women. Sounds a lot like Lewis' subtitle:
Phantastes: A Faerie Romance for Men and Women
That Hideous Strength: A Modern Fairy-Tale for Grown-Ups
The echo is pretty clear.
What is the link, then, that Lewis was trying to make? He connects the Ransom world of THS with Tolkien's and Williams' fictional worlds with overt references. But he connects THS to George MacDonald's Fairy Land through this subtle paratextual hint, the subtitle that rhymes with MacDonald's own subtitle.
When I first read Phantastes, I was extremely confused by the character of Sir Percivale, the Arthurian knight and seeker of the Holy Grail wandering through George MacDonald's Fairy Land. What I had missed early in Phantastes was George MacDonald's preparation for the Arthurian thread in his faërie garment. Early in the book, in a cabin on the threshold at the edge of the fairy wood, the protagonist Anodos reads from a great book. Here is what he reads:
"Here it chanced, that upon their quest, Sir Galahad and Sir Percivale rencountered in the depths of a great forest. Now, Sir Galahad was dight all in harness of silver, clear and shining; the which is a delight to look upon, but full hasty to tarnish, and withouten the labour of a ready squire, uneath to be kept fair and clean. And yet withouten squire or page, Sir Galahad's armour shone like the moon. And he rode a great white mare, whose bases and other housings were black, but all besprent with fair lilys of silver sheen. Whereas Sir Percivale bestrode a red horse, with a tawny mane and tail; whose trappings were all to-smirched with mud and mire; and his armour was wondrous rosty to behold, ne could he by any art furbish it again; so that as the sun in his going down shone twixt the bare trunks of the trees, full upon the knights twain, the one did seem all shining with light, and the other all to glow with ruddy fire…."
It is a fairly significant clue; if Anodos had kept reading, he would have heard his own tale. Even this I missed when I first read Phantastes, though once you work it out, it has a way of sticking. Given that Lewis is echoing MacDonald, is there something similar at play in That Hideous Strength? I went back to THS to see if I had missed the same kind of hint.
As it turns out, it begins almost immediately.
There is an Arthurian conversation between Jane, the protagonist and wife of Mark, and Dr. Dimble, her professor and a kind of mentor figure. Jane is a scholar of poetry and so knows England's literary heritage well. Dimble starts exploring whether the Arthurian world sits in the pagan, Druidic side of Britain, or the Roman, Christian side. In Dimble's view, Arthur draws them both together. But Merlin is an ambivalent character:
"Yes . . . [Merlin]'s the really interesting figure. Did the whole thing fail because he died so soon? Has it ever struck you what an odd creation Merlin is? He's not evil: yet he's a magician. He is obviously a druid: yet he knows all about the Grail. He's 'the devil's son', but then Layamon goes out of his way to tell you that the kind of being who fathered Merlin needn't have been bad after all. You remember: 'There dwell in the sky many kinds of wights. Some of them are good, and some work evil.'"
Here Dr. Dimble draws together all the elements of the story: the Christian history, magic, the Grail legend, good and evil as sides, and even faërie (sky-wights). The conversation swiftly moves from this reflective speech to the question of Bragdon Wood, the property behind their Bracton College that has been purchased by a nefarious conspiracy group for development. Merlin still sleeps there, Dr. Dimble reminds them. Who knows what will happens when his grave is dug up?
If this conversation in ch. 1, section V is not enough to prime readers for the Arthurian incursion, or if they miss the names in the book—Arthur Denniston, Fairy Hardcastle, Mr. Fisher King, the Pendragon—they should have been prepared for Merlin in ch. 1, section III. What makes this section remarkable is the third word: "I". In Out of the Silent Planet (OSP) and Perelandra, as well as in the Dark Tower fragment of a Ransom story, Lewis is a first person narrator. The first person voice grows throughout OSP to climax in a letter between "Lewis" and Ransom; in Perelandra it does the opposite, beginning with Lewis, who disappears as Ransom tells his tale. It is commonly acknowledged that the first person narrator disappears in That Hideous Strength. Through most of the book, there are no personal notes from the narrator. There is one important exception, however: ch. 1, section III. Here is how it begins:
"The only time I was a guest at Bracton [College] I persuaded my host to let … me into the Wood and leave me there alone for an hour. He apologised for locking me in….
"Very few people were allowed into Bragdon Wood. The gate was by Inigo Jones [17th century architect] and was the only entry: a high wall enclosed the Wood….
Lewis—presumably Lewis the character in the other Ransom books—is the storyteller here, like Anodos in Phantastes. Anodos transitions from his bedroom to Fairy Land almost seamlessly. You should read the whole of ch. 2, but here is an example of that seamless transition:
My dressing-table was an old-fashioned piece of furniture of black oak, with drawers all down the front. These were elaborately carved in foliage, of which ivy formed the chief part. The nearer end of this table remained just as it had been, but on the further end a singular change had commenced. I happened to fix my eye on a little cluster of ivy-leaves. The first of these was evidently the work of the carver; the next looked curious; the third was unmistakable ivy; and just beyond it a tendril of clematis had twined itself about the gilt handle of one of the drawers.
This happens in THS as well. Lewis also has a slow, incremental transition. The transition, though, is through an Oxford-style quad (you can see these in the Golden Compass film) into Bracton Wood, where Merlin rests. Here is a bit of that slide across the threshold:
"… the sense of gradual penetration into a holy of holies was very strong. First you went through the Newton quadrangle which is dry and gravelly; florid, but beautiful, Georgian buildings look down upon it. Next you must enter a cool tunnel-like passage, nearly dark at midday unless either the door into Hall should be open on your right or the buttery hatch on your left, giving you a glimpse of indoor daylight falling on panels, and a whiff of the smell of fresh bread. When you emerged from this tunnel you would find yourself in the medieval college: in the cloister of the much smaller quadrangle called Republic. The grass here looks very green after the aridity of Newton and the very stone of the buttresses that rise from it gives the impression of being soft and alive. Chapel is not far off: the hoarse, heavy noise of the works of a great and old clock comes to you from somewhere overhead.
You went along this cloister, past slabs and urns and busts that commemorate dead Bractonians, and then down shallow steps into the full daylight of the quadrangle called Lady Alice. The buildings to your left and right were seventeenth-century work: humble, almost domestic in character, with dormer windows, mossy and grey-tiled. You were in a sweet, Protestant world. You found yourself, perhaps, thinking of Bunyan or of Walton's Lives. There were no buildings straight ahead on the fourth side of Lady Alice: only a row of elms and a wall; and here first one became aware of the sound of running water and the cooing of wood pigeons. The street was so far off by now that there were no other noises.
In the wall there was a door. It led you into a covered gallery pierced with narrow windows on either side. Looking out through these you discovered that you were crossing a bridge and the dark brown dimpled Wynd was flowing under you. Now you were very near your goal. A wicket at the far end of the bridge brought you out on the Fellows' bowling-green, and across that you saw the high wall of the Wood and through the Inigo Jones gate you caught a glimpse of sunlit green and deep shadows."
Anodos' motion is like Lewis', though Anodos is walking into the faërie forest land where humans are in some danger, and Lewis is moving back through time in an enchanted forest where most of the fay have fled (or otherwise disappeared). Anodos travels on the tracks of space and perspective, Lewis on the tracks of space and time. Both make their way from the "real" world of bedrooms and kitchen tables to the world of fairy land.
Lewis travels a half mile into the wood, but the pilgrimmage feels much longer. The walled-in nature of the Wood gave it a "peculiar quality," but his real object was the Well at the centre of the Wood. Merlin's Well, where legend supported by some archaeology and bulky tradition said Merlin lay until that day. As Lewis thinks about the history of Merlin's Well, the story of Bracton Wood, and how the Bragdon College fellows were debating with Kings and Queens, he falls asleep, only to be "wakened by my friend hallowing to me from a long way off."
Who is this Merlin?
In Lewis' faërie lecture that he used to give at Oxford and that became the chapter "The Longævi" in The Discarded Image, he defines Merlin as almost in the category of "High Fairies":
The Fairy Damsels are ' met in forest wide'. Met is the important word. The encounter is not accidental. They have come to find us, and their intentions are usually (not always) amorous. They are the fées of French romance, the fays of our own, the fate of the Italians. Launfal's mistress, the lady who carried off Thomas the Rymer, the fairies in Orfeo, Bercilak in Gawain (who is called ' an alvish man' at line 681), are of this kind. Morgan le Fay in Malory has been humanised; her Italian equivalent Fata Morgana is a full Fairy. Merlin—only half human by blood and never shown practising magic as an art—almost belongs to this order. They are usually of at least fully human stature. The exception is Oberon in Huon of Bordeaux who is dwarfish, but in virtue of his beauty, gravity, and almost numinous character, must be classified among (let us call them) the High Fairies (130).
While That Hideous Strength is a very un-fairylike book at first blush, Lewis does not accidentally or carelessly place it within the MacDonald stream. For MacDonald, the inclusion of the Arthurian legend within the speculative Fairy Land is not a significant stretch. The Arthurian legend has always sat on the threshold of faërie with hybrid characters like Merlin and Morgana le Fey (fey=fairy). It is a hybrid world, as Dr. Dimble explains, combining the Druidic and the Christian, the Magic and the Moral. Some texts blend the two, so that Arthurian knights contend not just with giants and dragons, but with the ambivalent world of faërie (such as the "Sir Orfeo" poem translated by J.R.R. Tolkien, or the Welsh tradition). As Anodos wanders through Fairy Land—like Christian wandered through the wide world in Pilgrim's Progress before him—Sir Percivale is available in the imaginarium (the imagination bank) as a character to encounter.
C.S. Lewis' fairy tale is more complex in that he intends a contemporary re-imagining of the fairy tale setting. Here is his rationale in the preface to That Hideous Strength:
If you ask why—intending to write about magicians, devils, pantomime animals, and planetary angels, I nevertheless begin with such humdrum scenes and persons—reply that I am following the traditional fairy-tale. We do not always notice its method because the cottages, castles, woodcutters, and petty kings with which a fairy-tale opens have become to us as remote as the witches and ogres to which it proceeds. But they were not remote at all to the men who made and first enjoyed the stories.
The contemporary re-imagining of faërie will be familiar to those of us for whom urban fantasy is common fare (Holly Black's work, Buffy the Vampire Slayer, Once Upon a Time, etc.). But Lewis only had Edith Nesbitt's Bastable Children tramping around London and Charles Williams' supernatural thrillers for models. Lewis exchanges woodcutter cottages and impenetrable castles for college dining halls and institutional bureaucracies. So we can see how the frameworks of faërie would differ from MacDonald's in the key symbols of the faërie world.
But the storyline is quite different too. MacDonald's Anodos is a precursor to new adult fantasy, a young man coming into his heritage who finds that he is discovering self in the midst of self-denying adventure. Although the protagonists Mark and Jane of THS are also new adults on a journey of self-discovery and self-denial, the context is not adventure but apocalypse. Anodos will, like Bunyan's Christian or Homer's Odysseus, face temptations, trials, and his own final test of strength. Mark and Jane's context is totalitarian conspiracy—the threat of a Brave New World. Their own angst, their personal limitations, and their lack of direction is washed over by geopolitical forces that consider them merely cogs in the inevitable machine of progress.
Unlike Anodos, neither Mark nor Jane ever draw a sword—or even the pacifistic version of a sword. They merely remain steadfast in the onslaught before them. Yet they are not weaponless. A dim echo of the Round Table has been re-formed with the re-emergence of the Pendragon, and Merlin stands in the balance between anthropocide, a nation collapsed into the collective mind of a nihilistic totalitarian übermensch, and the restoration of true Britain.
The "why" of Merlin has complex answers, including the influence of Charles Williams and J.R.R. Tolkien, as well as the Arthur reborn myth in England and the apocalyptic mists of WWII. If Tolkien supplied energy for imaginative world-building possibilities, and Williams extended Lewis' choice in shaping the atmosphere of the novel, MacDonald provided the generic framework. Tolkien and Williams extend Lewis' mythopoeic vocabulary; MacDonald provides the grammar.
So, why does Merlin appear in That Hideous Strength? One aspect of the full answer is the simple reply, "Because he can." When Lewis adopted a fairy tale form, Merlin became one of the available characters. It may have worked the other way around for Lewis. When he discovered that Ransom was truly Arthur reborn and the Fisher King, he only had three options for generic framework: Epic Prose (as in Tolkien's "New Hobbit," which became Lord of the Rings), Epic Poetry (as in Williams' Arthuriad), or Fairy Tale. Having tried and failed at both the other two forms, Lewis chose fairy tale. Viewed from this angle, Lewis' move from WWII-era fiction to Narnia is not that great a leap.
This entry was posted in Reflections and tagged Arthuriana, Buffy the Vampire Slayer, C.S. Lewis, Charles Williams, children's literature, faerie, fairytales, fantasy, George MacDonald, J.R.R. Tolkien, King Arthur, Phantastes, Ransom Trilogy, That Hideous Strength. Bookmark the permalink.
44 Responses to Why is Merlin in That Hideous Strength?
Catherine Mullaney says:
I could not help thinking about MacDonald's Lilith when you wrote about Phantastes. "Anodos transitions from his bedroom to Fairy Land almost seamlessly." The influence writers have upon one another is powerful. I just listened to the unabridged audiobook, Uncharted by Angela Hunt and I can see the influence of Lewis' The Great Divorce in this book written in the 21st Century. I enjoyed making my way through most of your post and I laughed out loud at the conclusion you came to in answering your own quote. I have to ask, "Really, is that all it is?"
Brenton Dickieson says:
Yes, Lillith… I'm still struggling with that book. I think I need a second read to collect it fully into my imagination.
I am doing a series on The Great Divorce–published most Mondays (echoing the same dates as the original). You may be interested in talking about how Hunt takes up the Great Divorce and doing a guest blog (which you could repost to your own blog as well).
I would be delighted to do a guest blog on Hunt's take on the Great Divorce.
Cool, send me a note to junkola [at] gmail [dot] com and we'll chat!
Two thinking-aloud thoughts you jolt out of me with this very interesting exploration – how what I tend to characterize as Williams's 'figurae rerum' such as the Graal, the Stone, and in this context perhaps most notably his reimagining in The Greater Trumps of Tarot cards (insofar as they are more than just one sub-species of playing cards) to 'depend' on an original pack and corresponding table – how they seem to meet their 'consummation' in contemporary and very particular, variously 'intimate' circumstances. Merlin is more like the Stone in doing this in a way including spectacularly evident public impact, but he seems like this. They are there as dangers, and, if things go 'as they should', are there to 'save the day' – but (cf. The Great Divorce and The Weight of Glory) to be, much more urgently, possibly instrumental in saving particular souls/concrete human persons – in Merlin's case, including himself! And who (rather than the more usual Williamsian 'what') else is significantly like this? The oath-breakers on the Paths of the Dead in Tolkien, I'd say. (I wonder how much we can tell about the dates of stages of writing The Lord of the Rings and That Hideous Strength?)
Sørina Higgins says:
Reblogged this on The Oddest Inkling and commented:
Here is a BRILLIANT post by Brenton Dickieson about Merlin in That Hideous Strength. Enjoy!
stephencwinter says:
I agree with Sorina that this is BRILLIANT! I have always loved That Hideous Strength and regard it as being prophetic in the sense that it tells us, in the modern west, our story. I had forgotten the journey to the wood through the various quads. That alone made me want to read the book again.
We need to find Logres in our time for the denizens of the NICE grow in power daily. Could we find Merlin? Is there anyone among us who could speak to him?
Do you know Masefield's Arthurian poetry? Consider both his "Midsummer Night" and "Simkin, Tomkin and Jack" by way of comparison…
Many thanks, David. I know some of Masefield's poetry, the most famous ones I fear. I just pulled The Book of Beauty that he edited off my shelves to take a look & note that he did not include a single poem of his own there. I also read his children's stories to my own children but I don't know his Arthurian poetry. Did you suggest him in response to my question asking who among us could speak to Merlin?
Yes. Avoiding 'spoilers' as much as possible, in the volume Midsummer Night and other Tales in Verse (1928) in addition to poems retelling the stories of King Arthur there are two poems which we might describe as about the 'return of Arthur' (and his court, in the case of "Midsummer Night", while "On the Coming of Arthur", later in the book, refers only to him by name) – where this is vividly looked to as a future event. "Simkin, Tomkin and Jack", from Minnie Maylow's Story and Other Tales and Scenes (1931), tells what happens when those three brothers excavate one who "was Arthur's comrade" and attempt to resuscitate him which includes present, and also looks to future, events. You mention "his children's stories" and I should have mentioned the Arthurian element in The Midnight Folk (1927) – a book we know Lewis relished, as it happens (so George Sayer told me, anyway)! – which, as I recall, in its own, different way also includes present, and looks to future, events.
In these various imaginings of Masefield's, we might perhaps be able to find Merlin and (attempt to) speak to him. In That Hideous Strength, my impression is that he is imagined as having definitively returned – and departed again, and that that possibility concerned him uniquely (as far as anyone knows), rather than also including King, Court, or any other "comrades".
(If you have an accommodating library within reach, either in terms of its collection, or 'interlibrary loan' service, you might also be interested in having a look at my discussion of Masefield and 'Avalon' in the – spoiler-filled! – "Introduction" to my 1994 edition of his work in Boydell & Brewer's 'Arthurian Poets' series.)
Thank you so much for replying to my comment at such length & with such detail. After reading your earlier comment I searched for Midsummer Night and found it on a website called Celtic Twilight (celtic-twilight.com) and I have begun to read it with some relish. They include a link to your 1994 edition of Masefield's work on the site.
What struck me about the poem was, firstly, what a fine story teller Masefield was; and, secondly, the robustness of his metre. I can only describe it by using what feels like an old-fashioned word and that is "manly". I can see what would have drawn Lewis to his work. The wonderful image of the Dawn Treader in the painting at the beginning of that story comes to mind. Similarly in Midsummer Night the characters seem to want to burst from the page.
If Merlin also steps from the page in this manner then I can see why you recommend Masefield's work. He would stand in such contrast to the artificially lit world of the NICE and their contemporary followers.
Yes, he is so lively (and varied) a storyteller-in-verse! (In prose, too, come to that!)
Not to mislead you, though – I was thinking in terms of 'once-and-future' King (and knights, court, etc.) analogy – the 'destined' or possible 'return' – in the first place, in my comparison above, rather than his treatment(s) of Merlin in particular (though your mentioning that, makes it clear what attention it warrants – I had better get rereading, myself!).
(Another interesting aspect of Masefield's Arthurian poetical works is the 'matter' of pagan and Christian, which in its own way is such a feature of Lewis's novel, but I won't try to address it here and now…)
On the 'verse-side', it is interesting to compare your remarks about the metrical robustness and manliness of Masefield's use of 'traditional' poetry with Lewis's observations about Williams in the two reviews reprinted in the most recent Lewis collection… (have a look: it will be well worth your while!).
Masefield and the Inklings is either a subject awaiting separate attention, or one where, sadly, I have missed what had been written already!
As I said to Stephen, I've started reading Image and Imagination: Essays and Reviews, ed. Walter Hooper (CUP, 2013), which includes both a review of Taliessin through Logres published in April 1939 when Lewis had known Williams for a couple years and before the OUP was evacuated to Oxford, and another from March 1946 when OUP republished it – after Lewis had published That Hideous Strength (THS) and Williams had died. They are not only interesting to compare with each other, and other things Lewis wrote about Williams, but for their attention to Williams's treatment of Arthur's realm and the Empire 'in period' and of Merlin – and possible implicit comparisons and contrasts with what he decided to do in THS.
Reblogged this on Khanya.
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Cameron B. Clark says:
This paper overlooks the connection between Merlin and MAGIC, and how magic relates to and/or contrasts with science and scientism. In his preface to the novel, Lewis states that "it has behind it a serious 'point' which I have tried to make in my Abolition of Man" which compares magic to science. Also, in the novel itself Lewis discusses magic in several places and distinguishes Merlin's magical art (which in one place he calls "magia" in contrast with "goeteia") from the ceremonial occultists. Merlin as a magician is a major reason why he is in the novel.
One must understand why Merlin's magic (magia) is different. His magic is rooted in who or what he is, not in what he does. This makes him a type of Christ, like Aslan was in Narnia, whose miracles are rooted in who he is. Merlin's magic, like Christ's miracles, are complimentary to nature, not contrary to it.
Thanks for this. Why don't you write a response? It is certainly something I have overlooked. I have been imagining Lewis' idea of magic in Abolition of Man as "the magician's bargain"–the "process whereby man surrenders
object after object, and finally himself, to Nature in return for power." I think the magician and the scientist holds hands in that sense, rather than contrast.
I'd love to see how that turns in your mind in a way it is static in mine. I may have missed something. Post it on your own blog or on mine and the conversation is in play!
What is certainly true is that these books are linked, and I've nowhere written up those links.
Thanks Brenton for your response. I probably should pursue a published literary outlet since I haven't seen the character of Merlin as a magician in That Hideous Strength given the attention that he deserves, but sharing it with you suffices for me since I enjoyed your above paper on Merlin in the novel. You are correct that The Abolition of Man limits the discussion of magic to the bad sort to make related points about applied science. The novel, on the other hand, presents Merlin as a good sort in contrast with the ceremonial occultists. Just as good and bad magic had a place in Narnia, the also have a place here.
An obvious hurdle on using Merlin as a type of Christ is the legend that Merlin's father was a demon. Lewis hits that legend early in the novel through his character Dr. Dimple on pages 29 – 30 (Scribner Classics): "Has it ever struck you what an odd creation Merlin is? He's not evil; yet he's a magician. He is obviously a druid; yet he knows all about the Grail. He's 'the devil's son'; but then Layamon goes out of his way to tell you that the kind of being who fathered Merlin needn't have been after all. You remember, 'There dwell in the sky many kinds of wights. Some of them are good, and some work evil." Dr. Dimple continues: "I often wonder whether Merlin doesn't represent the last trace of something the later tradition has quite forgotten about – something that became impossible when the only people in touch with the supernatural were either white or black, either priests or sorcerers." It goes without saying that Christ's birth was supernatural via the Holy Spirit which, if Lewis wanted to use Merlin as a type of Christ in the novel, certainly explains why he would address Merlin's source of magic via his non-human father.
David C. Downing in his book Into the Region of Awe: Mysticism in C.S. Lewis (2005) touches on Lewis's use of good and bad magic in the Narnia books and even says Lewis "considered the kind of 'fairy' magic we find in Arthurian romances as basically innocent in contrast to the insidious Renaissance sorcery of spells, charms and secret symbols. Fairy magic is merely a storytelling devise, while Faustian magic is an actual attempt to wield occult powers in the real world" (page 152). Downing discusses the deep/deeper magic in Narnia as good magic and even quotes from The Abolition of Man to give an example of the evil Renaissance magic, but he fails to see the good magic of Merlin in That Hideous Strength and how it can metaphorically relate to Christ and his miracles. A discussion of Merlin's magic as it compares to Christ's miracles would also have to explore what Lewis says about miracles in his book on miracles which, by the way, also discusses magic on page 179.
Excuse the typos above (the = they; devise = device). Also the quote from Dr. Dimple: "…the kind of being who fathered Merlin needn't have been (insert 'bad') after all."
Oops: Dr. Dimple = Dr. Dimble
Actually, I prefer Dr. Dimple!
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Suzannah says:
Wonderful analysis! Thank you!
I'm really looking forward to reading "The Inklings and King Arthur" – sounds like a treat.
Thanks Suzannah! The Inklings and Arthur book is really going to be a definitive collection. It will be long–most won't read it cover to cover. There are some great essays in it.
Just ran into something relevant to Lewis's Avalon development in chapters 13 and 17, and (so?) in its way, to his Merlin development, too: Brantly Millegan's post about Enoch and Elijah, in which he links St. Thomas Aquinas, Summa Theologica, Part III, Q 49, article 5, reply to objection 2: "Elias was taken up into the atmospheric heaven, but not in to the empyrean heaven, which is the abode of the saints: and likewise Enoch was translated into the earthly paradise, where he is believed to live with Elias until the coming of Antichrist." And one of the commenters, tj.nelson, quotes St. Gregory Palamas to similar effect (alas, without reference details): "I found the following reflection upon what St. Gregory Palamas thought about the assumption of Elijah:
"He writes: 'But neither did he exceed the bounds of the earth's atmosphere; the ascension of each of them was a raising from the earth without being taken from the surroundings of the earth…'."
I haven't looked up the Latin of the first and wouldn't know where to begin searching for the Greek of the second, but Lewis might well have know the first (though the second seems unlikely), and one can imagine him consciously playing with the "atmospheric heaven" to designate "the Field of Arbol" (and similarly with respect to the latter, should he know it, or other Greek exegeses like it).
https://churchpop.com/2016/08/14/mary-wasnt-the-only-one-two-other-saints-who-were-assumed-into-heaven/
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I'm not looking for a matched set with DME & Keys and stuff.
I'm just looking for a 531 preferably, but from what I understand, 535 should work as well. I want to see if I can transfer the data from mine to a working one.
No offense to those on here who do the repairs, I just want to give it a shot to fix it myself. I still may end up having to contact you with hat in hand.
So if you have one that's not a matched set and is otherwise of no use to you, PM me.


text:
Detectives ID Suspect Vehicle in Fatal Stabbing of San Jose Father of Two
Frankie Santos Jr. was killed in front of his own home, and the suspect remains at large
By Ian Cull	• Published October 11, 2018 • Updated on October 11, 2018 at 11:39 pm
Santa Clara County sheriff's detectives have identified a vehicle they hope will lead to a suspect in the fatal stabbing of a San Jose father of two in front of his own home in September.
Frankie Santos Jr. was stabbed to death in front of his house on Sept. 2, but there's been no update in the case until now. A surveillance photo from that night shows the at-large suspect's truck, a 2004-05 Nissan Titan, investigators said.
It's a sliver of hope for his mother, widow and two children.
SF to Open 3 Large COVID-19 Vaccination Sites, Unveil Vaccine Notification System
California Lottery 3 hours ago
Lotto Fever: Beware of Scammers as Mega Millions, Powerball Jackpots Soar
Santos's wife said she's had to move out of her home because she doesn't feel safe there. She's been raising money for a reward that soon will top $10,000 for information as new details emerge in the case.
"They want to see their dad again," Sky Santos said about her children. "When we get there, my younger one says, 'Is Daddy here?' He still thinks he's there."
Last Saturday, family and friends were passing out the updated flier near the Santos home when two men in a truck started threatening them.
"Some of my husband's friends tried to follow the truck to get a license plate, and they shot a gun. The police went on a high speed chase with them and didn't find them until the next day and apprehended two juveniles from the vehicle."
They're not believed to be the murder suspects.
The slaying has nearby neighborhoods on edge and prompted a meeting with authorities at the Bascom Community Center Thursday night.
"So they can voice their concerns," Santa Clara County Supervisor Ken Yeager said. "And if we can offer answers and solutions, we hope to do that."
The Santos family is asking people one last time to check their surveillance cameras, hoping to catch the suspect.
"It's life changing," Sky Santos said. "We no longer can stay in our home that have all of our memories, and whatever we have left of my husband and their father. I've lost everything."
Deputies said there have been increased patrols in the Bascom Avenue and San Carlos Street areas over the past five weeks.
San JoseSheriffSanta Clara CountyIan CullNISSAN

redpajama_set_name: RedPajamaCommonCrawl


The Kit is Used in Mobile Shops with monitoring service. Shutter Contact is used for Main Shutter, if you have more shutters You Can buy Hybrid Shutter contact in app. One Motion Sensor is placed in working Hall. Remote can be used to activate and de-activate the panel and panic button can be used in emergency.
PC9045 Control Panel - 1No, WS4904P - 1Nos, WS4939 - 1No, SD15W - 1No, SC05 -1No, WS4945 - 1No.


Here it is folks, my first official Q+A episode. The timing just felt right. So I reached out to you, my wonderful listeners, for questions in this weeks Q+A episode. This was such a fun way to connect with you guys and it's so amazing to hear what you are interested in hearing more about.
To keep it more conversational [and because I thought it would be fun] I decided to recruit someone very close to me, to join in on this episode. You guys get to meet the lovely Mr. Liveng Proof – aka my amazing husband Michael. I really appreciated his energy in this episode because you guys asked some pretty serious questions, and he was the perfect sounding board for me to answer these questions. He also added some solid anecdotes.
Q: Any suggestions for navigating sharing a history of an ED with a new potential partner (hopefully without freaking them out/ scaring them off)?
Q: Now that I'm more in tune with what constitutes "diet culture" without trying to start a crusade how does one fight against the industry and try to make things better for themselves and those around them?
Q: How did you overcome your exercise addiction? The steps you took to get there?
Q: What is one way that you have changed as a person in the past year?
Q: What do you use under your eyes or for you skin to look so awake and radiant?


Margaret Salazar-Porzio is a Curator in the Division of Home and Community Life with interests and expertise in 20th century visual and material culture of the Western United States, Pacific Rim, and Mexico; Race, Citizenship and National Identity in U.S. urban history; U.S. Latina/o History and Culture; Cultural Studies and 20th Century Cultural History in the U.S.; and K-20 Education.
Comparative Race Relations in the 20th-Century U.S.
Visual and Material Culture of Latinas/os in the U.S.
Dr. Salazar-Porzio will be working with colleagues to document Latino(a) history and culture, and serve as co-curator of the Many Voices One Nation exhibition that will open in 2017.
Before joining the SI National Museum of American History, Salazar-Porzio served as an Associate Research Scholar at the Center for Institutional and Social Change at Columbia University Law School (2011-2013), and held a Smithsonian Institution Latina/o Studies Postdoctoral Fellowship (2010-2011). In addition to receiving research fellowships from the Guggenheim (2006), Rockefeller (2003-2004), and National Science Foundations (2004), Salazar-Porzio was also a primary education teacher in Los Angeles, California, where she received a local Teacher of the Year award (2004).


You're ready for way more responsibilities, and you need to make sure your boss knows it.
1. Share your career goals.
It's important to have career goals for yourself, but don't keep those goals floating around in your head—communicate them to your boss. Sit down with her and discuss your professional career goals with specific sub-tasks that can help concretize them. If your manager is a good boss, she'll help you grow by directing opportunities your way to keep you on track. That means assigning you to special projects, so you'll build new skills, too.
It's important for you to be able to cooperate and contribute towards the greater good of your team. This shows that you're not only efficient, but that you're also a team player and easy to get along with. Your boss will notice and appreciate this. Remember, leaders think of their team first and prioritize the growth of each member; your boss needs to see that in you before you rise a level higher.
3. Volunteer for more responsibilities.
If you're balancing your current tasks well, consider taking on more. Never limit yourself to what's in your job description. If there's something that needs tending to (like making other people's work easier) and you're qualified for the task, volunteer. Your initiative helps you stand out and makes your boss think more highly of you. Just make sure you handle your responsibilities first so you don't come off as an attention seeker or people pleaser.
4. Never complain. Avoid being negative.
Whining can add a lot of negative energy to the room. If you speak negatively, it's likely that your coworkers will think that you're toxic to work with, or you'll influence them to be negative too—this is not good for the team and the company, so it's not something a good leader would do. When something bothers you, take a deep breath, clear your mind, and do your best to get the work done. If you need to vent, turn to your friend or your mom for a good ranting session.
Asking questions isn't a bad thing since it shows your bosses and your colleagues that you're genuinely open to learning and gaining new skills. If you're doing this already, the next step is to keep the answers in mind and show you're learning.
To learn more about the Raise Your Hand campaign, follow Kojie.san on Facebook.


text:
CHENNAI, TAMILNADU, TOP STORIES
Bodies of the four fishermen who went missing after colliding with a Sri Lankan Navy vessel have been recovered.
Sivakarthikeyan in a biggie?
Sivakarthikeyan eagerly awaits the release of Doctor and Ayalaan. Doctor directed by Nelson has already wrapped up with the shooting…
Chiranjeevi, Mohan Raja start their next
Chiranjeevi's 153th film was launched at Super Good Office in Hyderabad. Konidela Productions, Super Good Films, and N V R…
Destination Nainital for him
Yuvraj Singh is currently spending quality time with his kids in Nainital. They went for some time together in the…
Dulquer, Balki team up for a thriller
Dulquer Salmaan and director R Balki will be teaming up for a Bollywood project. Touted to be an edge of the…
'Kaliyugam is a futuristic film'
Futuristic films are a rarity in Tamil cinema. Only a very few films on the genre have hit the screens…
Bodies of the four fishermen who went missing after colliding with a Sri Lankan Navy…
Rahul Gandhi to kickstart TN poll campaign on 23 January
Congress leader Rahul Gandhi will hit the campaign trail on 23 January in Tamilnadu where…
Micro-finance helps woman entrepreneurs
Faridha Bee who belongs to a small locality of Manavalanagar in Thiruvallur district, had lost…
I-T raids Jesus Calls group
Income Tax department today conducted raids at 28 locations in Chennai, Coimbatore and other places…
Tamilnadu has 6,26,74,446 voters
Tamilnadu has a total electorate of 6.26 crore voters as per the final revised electoral…
Indian boat sinks after colliding with Lankan Navy Vessel
An Indian fishing boat allegedly trespassing the International Maritime Boundary Line sank in Sri Lankan…


This is a really easy and fun DIY that can be done with a sewing machine or hand-sewn. The idea is to take a plain cami (like these below, which are $8 at Target) and make it special. You can use all kinds of tanks and ribbons to create completely different looks.
Pick out some ribbon that you fancy. I picked the rainbow ribbon to go with the navy cami and I'm planning on using the ivory lace on the white cami. You might also consider buttons, bows, rosettes, or other tidbits.
Fold about an inch of ribbon under so it won't fray and use your sewing machine to secure it to the back of the cami, lined up with the strap.
From here on out, I stitched everything by hand. After I tried on the tank and made sure the straps were adjusted the way I wanted them, I secured the ribbon to the strap every couple of inches with a stitch or two. It looked like this when I finished securing the ribbon to the straps.
And you're done. Mine isn't perfect. I wish I'd thought ahead so that the ribbon matches up on the left side but it's barely noticeable. Unless someone, like me, points it out. I'll let you know how the white cami turns out too.
Cute! Your finished tank reminds me of one I had when I was a child in the '80s. Everytime I see a pic of me wearing it I want one now!
That is so cute! And easy enough for even me to do! Thanks, Ellen.
Thanks y'all. I wanna make more. Brookie, I can't remember where the necklace is from! I think it was a gift but I'm blanking.


This session is so lovely, I almost don't want to write anything, since the images speak so loudly for themselves. Suffice to say, I was invited back to the hospital to photograph sweet Claire's Fresh 48 Session when she was less than 24 hours old. I am so honored to have been asked to capture these supremely beautiful moments.


The first of these three blessed places in Gobind Sadan is Darbar Sahib, home of Guru Granth Sahib, the extraordinary Sikh scripture which includes sacred hymns of Sikh Gurus, Hindu saints, and Muslim saints. Here Maharaj is doing Chaur Sahib over Guru Granth Sahib in the veranda outside Darbar Sahib as Bhai Kirpal Singh ("Major Sahib") takes the hukam (divine order) from Guru Granth Sahib for Joey and Shruti's wedding.
International Interfaith Assembly guests pay their respects inside Darbar Sahib, where Akhand Path (non-stop reading of Guru Granth Sahib) goes on continually.
Another of the central holy places is the havan (sacred fire) which has been burning since 1968 with continuous recitation of Jaap Sahib. It was the first structure built in Gobind Sadan and is highly charged with divine Power. Delegates from the Interfaith Assembly join the prayers in havan.
The third sacred place is Baba Siri Chand's havan, where Maharaj used to meditate. Sitting inside is his old assistant, Baba Gurbux Singh. The havan is decorated with flowers for the birthday of Baba Siri Chand, a great ascetic who was elder son of the First Sikh Guru Nanak and helped to propagate Guru Nanak's mission. Baba Siri Chand was one of Maharaj's beloved Gurus since his youth, and he sponsored many research efforts to clarify the true history of Baba Siri Chand.
Maharaj Ji used to meet the sangat in various places in Gobind Sadan. One was the "Green Room" opposite the langar, which is still maintained as a sacred place for his darshan, with his original rocking chair. A beautiful natural mural was on the wall behind his chair.
Another place where we were sometimes fortunate to have Maharaj's darshan was in Gurcharan's room. Gurcharan, manager of Gobind Sadan, was often sitting there, too, doing seva of pressing his clothes and those of Guru Granth Sahib. Laughing with Maharaj in this picture is Balwant Singh, his driver and agricultural assistant.
Maharaj Ji also gave audience in the front room of the small house where he lived, which we called "Dubai kothi" because of the family from Dubai who previously lived in it. Here he is sitting with Harvinder Singh (a close assistant whom he called by the pet name "Melang"), Acharya Sri Rama Shastri, and Dr. Karan Singh, a renowned Indian statesman from a royal family .
Maharaj met larger groups in the office. He sat on a low cushion and others sat on white sheets over the carpet. Before he sat anywhere to give audience, he bowed and prayed before the place he was to sit, apparently asking God or his Guru to do the speaking there.
Maharaj used his long and beautiful hands very expressively when he spoke. Even when one couldn't understand his language—sweet rural Punjabi—his talks were visually touching and the microphone attached to my tape recorder was capturing his precious words for the world.
Portrait of Maharaj Ji in the outer audience room of his Dubai Kothi. He lived in three small rooms there. The outer room had large glass windows opening onto a garden from which devotees could have his darshan.


BOARDMAN, Ohio — Blueprint Healthcare Real Estate Advisors has brokered the sale of Commons at Greenbriar, a 50-unit assisted living community located in the Youngstown suburb of Boardman.
Built in 1996 adjacent to a skilled nursing facility owned by the seller, Commons at Greenbriar was designed to offer lower acuity, assisted living services in a residential setting and serve as a referral source for the neighboring nursing facility. Occupancy struggled to keep up with the local average as new competitors entered the market, resulting in steadily declining revenues per unit and operational cash flow. As a result, the sale was marketed as a value-add opportunity.
A joint venture between a publicly traded REIT and a national skilled nursing operator sold the property for an undisclosed price.
EDM Management Inc., a property management and consulting services firm headquartered locally in Boardman, was selected as buyer for its local expertise and acquisition track record. The buyer plans to invest in a series of capital improvements and a potential expansion to competitively position the asset among the area's newer competition.
Ben Firestone, Michael Segal and Connor Doherty of Blueprint were the lead advisors on the transaction.


text:
\section{Introduction}
Major academic and industry efforts are currently in progress to realize scalable quantum hardware and develop powerful quantum software. Quantum computers are expected to have significant applications in solving intractable problems in areas including optimization, chemistry, security, and machine learning. On the other hand, machine learning theories may also help us better solve the problems in quantum computation~\cite{Biamonte2017c,Dunjko2018,Ciliberto2018}.
One of the most fundamental topics in quantum computation and quantum information is to learn an unknown quantum state. Sufficient knowledge of a quantum state is in particular vital and indispensable in verification and identification of experimental outcomes on near-term quantum devices.

Suppose we have a quantum device or a physical process that could produce a quantum state. By using the device repeatedly, we can prepare many copies of the state and can then measure each copy. The goal of quantum state learning is to learn an approximate description of the state based on various measurement outcomes. To obtain a full characterization of an unknown quantum state, the most well-known approach is to do quantum state tomography~\cite{Nielsen2010}, which is of great practical and theoretical importance. To be specific, the goal of state tomography is to reconstruct the full density matrix that approximates the target unknown state $\rho$ within $\varepsilon$ in trace distance. In general, a $n$-qubit quantum state is described by roughly $2^{2n}$ real parameters, and a full tomography of these parameters is quite costly. Fully reconstruction of an unknown state in the worst-case costs exponential copies of the state~\cite{ODonnell2016,Haah2017b}.
The cost for tomography of an arbitrary $50$-qubit state is already prohibitively expensive.

However, some side information with respect to measurements is sufficient in some applications.
Therefore, \cite{Aaronson2007} considered how well a quantum state $\rho$ can be learned from the results of measurements. The goal of this setting is to construct a quantum state $\sigma$ that has roughly the same expectation value as the target state $\rho$ for a collection of two-outcome measurements. Recently, \cite{Aaronson2018b} considered the problem of shadow tomography, where the learner aims to output  $\tr(E_j\rho)$ up to an additive error. Under this setting, \cite{Aaronson2018b} showed that the required number of copies of the unknown state is nearly linear to the number of qubits and poly-logarithmic in terms of the number of the measurements.

One drawback of the above learning methods is the assumption that the sample measurements are drawn independently from some probability distribution, which is unrealistic in practice and fails to work for adversarial environments. Such an assumption does not always hold since the environment in experiments may change over time. It is thus desirable to design quantum learning algorithms that work in a more general model with sequential or even adversarial data.

The online learning theory~\cite{Shalev-Shwartz2011a,Hazan2016} represents a powerful tool to handle adversarial sequential data, which has a profound impact on machine learning.
Inspired by the success of online learning, developing their quantum counterparts is a natural and essential topic in the emerging field of quantum machine learning.
Recently, \cite{Aaronson2018a} proposed the model of online quantum states learning to overcome the above drawback of the i.i.d. assumption and deal with sequential data. In the online learning model, the data of measurements and losses are provided sequentially, and the referee is able to choose the measurement operator even adversarially. The leaner predicts a series of quantum states interactively in the learning process. The goal is to minimize the regret, which is the difference in the total loss between the learning algorithm and the best hypothesis quantum state in hindsight.

In this paper, we focus on the problem of online quantum state learning and consider more practical and adaptive cases. First, existing online learning algorithms are entirely classical, so we here consider a quantum algorithm that can be efficiently implemented on near-term quantum devices. Besides, most existing algorithms are proven to be worst-case optimal, but they would not perform well when dealing with a practical problem that is probably not the worst case or even relatively easy. Here we consider several common quantum computing assumptions:
\begin{itemize}
    \item First, the existing algorithms assume that the rank of the measurement operator is arbitrary and unknown, while in practice, the rank might be low and known in advance.

    \item Second, the existing algorithms fail in adapting to the case when the loss of the best quantum state in hindsight is small. In quantum, this happens when each round we are allowed to measure a large batch of states copies each gives a binary outcome, and therefore the average we observed will close to the true "Yes" probability. This includes the special  "realizable"  case where the feedback is always the real "Yes" probability, which means the loss of best quantum states in hindsight is always zero.
\end{itemize}

In this work, we propose several algorithms to address those problems.
\begin{itemize}
    \item We propose the Regulerized Follow-the-Leader (RFTL) algorithm with Tallis-2 Entropy that achieves the regret in term of measurement rank $M$ as $\operatorname{O} (\sqrt{MT})$. Moreover, we propose a variational quantum algorithm that can achieve the same result on the near-term quantum device. (Section~\ref{sec: tsallis2})
    \item When the loss is $L_2$-norm, we provide a parameter-free learning rate adjusting strategy, which helps both RFTL with von Neumann entropy and Matrix Exponentiated Gradient (MEG) updating methods to achieve a so-called "small loss" bound $\Tilde{\operatorname{O}}\left(\sqrt{n L^*} + n \right)$, where $L^*$ is the loss of the best quantum state in hindsight and is formally defined in Eq.~\eqref{eq: def of small loss}. Notice that \cite{Aaronson2018a} provides a worst-case optimal algorithm based on RFTL with von-Neumann entropy, but whether we can obtain a "small loss" bound using the same algorithm remains open. In addition, \cite{Tsuda2005} shows that MEG can achieve such a "small loss" bound only when $L^*$ is known in advance, and the adversary is oblivious. Therefore, our result is strictly better than these two. (Section~\ref{sec: small_loss})
\end{itemize}

\section{Preliminaries and Problem Description}
\label{sec:setup}
\paragraph{Quantum states}
In quantum computing, a quantum bit (or qubit) could be in a superposition of $0$ and $1$, which is different with the classical bit in a deterministic state of either $0$ or $1$. Generally, any quantum data can be represented by quantum states over some space $\cX$ (e.g., $\cX = \CC^d$).
The pure states and mixed states can be unitedly described by a mathematical tool called density matrix (or density operator), which is a positive semidefinite matrix (i.e., $\rho \ge 0$) with trace one (i.e., $\tr\rho =1$). The set of $d$-dimensional quantum states is denoted by $\cS_d$. A quantum state is pure if $\rank(\rho)=1$; otherwise it is a mixed state. Particularly, a pure state can be represented by a unit vector in the sense that $\rho = \proj{\psi}$ with $\ket \psi \in \CC^d$ and  $\bra\psi = \ket \psi^{\dagger}$, where $\dagger$ refers to conjugate transpose.
Quantum states on a composed system of $\cX$ and $\cY$ can be described by density operators over the tensor (or Kronecker) product space $\cX\otimes \cY$.
For a bipartite state $\rho_{XY}$, its marginal state $\rho_X$ on system $\cX$  can characterized via the partial trace function $\tr_{Y}(\cdot)$, i.e., $\rho_X = \tr_{Y}(\rho_{XY})$.

\paragraph{Two-outcome measurements}
Two-outcome measurement Quantum measurement is
a means to extract classical (observable) information from
a quantum state. When applying a two-outcome measurement to a quantum state, it succeeds with a specific probability. Mathematically, a two-outcome measurement could be described
by a Hermitian matrix $E$ with eigenvalues in $[0,1]$. When applying $E$ to a quantum state $\rho$, it outputs a successful result ``Yes'' with probability $\tr E\rho$ and a rejected
result ``No'' with probability $1-\tr E\rho$.

\paragraph{Frobenius norm and its dual}
Given two complex vector spaces $\mathcal{X}=\mathbb{C}^{d}$ and $\mathcal{Y}=\mathbb{C}^{m}$,
we denote the space of all linear operators of the form $A:\cX\to\cY$ as $L(\cX, \cY)$. The notation $L(\cX)$ is shorthand for $L(\cX, \cX)$. An inner product on $L(\cX, \cY)$ is defined as $\langle A, B\rangle=\operatorname{Tr}\left(A^{\dagger} B\right)$, where $A^\dagger$ is the conjugate transpose of $A$.

For $\mathcal{X}=\mathbb{C}^{d},\mathcal{Y}=\mathbb{C}^{m}$, and any operator $A\in L(\cX,\cY)$, the Frobenius norm is defined as
\begin{align}
\|A\|_2 = \sqrt{\tr(A^{\dagger}A)}.
\end{align}
This norm corresponds precisely to the $2$-norm of the vector of singular values of $A$. In particular, for a Hermitian operator $A$ ($A = A^{\dagger}$), its the Frobenius norm simplifies to
$\|A\|_2 = \sqrt {\tr A^2}$. Moreover, the dual norm of the Frobenius norm is itself, i.e.,
$\|A\|_2^* = \|A\|_2$.

\paragraph{Tsallis entropy} For a quantum state $\rho$, the quantum Tsallis entropy is defined as follows
\begin{align}
S_{q}(\rho):=\frac{1}{1-q}\left(\operatorname{Tr}\left(\rho^{q}\right)-1\right), q \in(0,1) \cup(1, \infty).
\end{align}
In particular, the Tsallis-$2$ entropy is closely connected to the Frobenius norm in the sense that
\begin{align}
S_{2}(\rho) = 1 - \tr \rho^2 = 1 - \|\rho\|_2^2.
\end{align}

\paragraph{Online learning and problem set-up}
Online quantum state learning is a sequential prediction process with interactions between a player and a referee or adversary over $T$ rounds. Suppose there is an underlying unknown state $\rho$ to be learned. At the round $t$, the player predicts
a quantum state $\omega_t$. At the same time, the referee or adversary adaptively
chooses and reveals a two-outcome measurement $E_t$ and an outcome feedback $b_t$, based on the learner's strategy and the past history. Here $b_t$ could be the exact value or an approximate value of $\ell_t(\tr E_t \rho)$ with random noise, but is allowed to be arbitrary in general. At the end of this round, the player
suffers a loss
\begin{align}
\ell_t(\tr E_t\omega_t),
\end{align}
where $\ell_t$ is a loss function. As a convention in online learning, we assume the loss function $\ell_{t}$ is convex and $L$-Lipschitz. Important examples of loss functions are $L_1$ loss defined as $\ell_{t}(z):=\left|z-b_{t}\right|$, and $L_2$ loss defined as $\ell_{t}(z):=(z-b_{t})^2$.

The regret is the difference in the total loss between the
learning algorithm and the best hypothesis state in hindsight. Notice that this best hypothesis state in hindsight might be close but different from $\rho$ when feedback $b_t$ is noisy.
Formally this is captured by the notion of regret $Reg_T$, defined as
\begin{align}
\operatorname{Reg}_T := \sum_{t=1}^T \ell_t(\tr E_t\omega_t)- \min_{\omega\in\cS_n}\sum_{t=1}^T \ell_t(\tr E_t\omega).
\end{align}
 In this paper, we also consider the setting in which there are constraints on the measurement operators. For example, in the limited-rank setting,  the sequence of two-outcome measurements $E_t$ can be arbitrary but with limited rank $M$, i.e., $\rank(E_t)\le M$, $\forall t$. The limited-rank measurements are extensively considered and studied in the literature~\cite{Bruss2001,Jozsa2003,Eldar2002}.

In addition, we also consider the regret in terms of the loss of best hypothesis state in hindsight $L^*_T$ defined as
\begin{align}
    L^*_T = \min_{\omega\in\cS_n}\sum_{t=1}^T \ell_t(\tr E_t\omega), \label{eq: def of small loss}
\end{align}
instead of the regret in terms of $T$.
In online learning, we usually call such regret form "small loss" bound. Some previous works about this small loss include~\cite{de_rooij_follow_2013} and ~\cite{Tsuda2005}.

\section{Learning with Tsallis-2 regularizer}
\label{sec: tsallis2}
\subsection{RFTL algorithm with Tsallis-2 regularizer and results}
We first follow the template of the Regularized Follow-the-Leader (RFTL) algorithm (see, e.g.,\cite{Hazan2016,Shalev-Shwartz2007,Abernethy2009}). In particular, we choose the Tsallis-2 entropy as the regularization term (or regularizer),
while previous approach~\cite{Aaronson2018a} utilizes the von Neumann entropy.

\begin{algorithm}[H]
\caption{RFTL algorithm with Tsallis-2 regularizer}
\begin{algorithmic}[1] \label{alg:rftl}
\STATE Input: number of rounds $T$, $\eta<\frac{1}{2}$, dimension $d$.
\STATE Set $\omega_1 = I/d$
\FOR{$t = 1, \ldots, T$}
\STATE Predict $\omega_{t}$. Consider the loss function $\ell_t$ and compute $\nabla_t = \ell_t'(\tr E_t\omega_t)E_t$.
\STATE Update $\omega_{t+1}$ via solving the following optimization
\begin{align}\label{eq: Tsallis-2 update rule}
\omega_{t+1}=\underset{\omega \in \cS_d}{\arg \min }\left\{\eta \sum_{s=1}^{t} \tr\left(\nabla_{s} \omega\right)-S_2(\omega)\right\}
\end{align}
\ENDFOR
\end{algorithmic}
\end{algorithm}

\begin{proposition}
\label{prop: sdp}
The updated state $\omega_t$ in Eq.~\eqref{eq: Tsallis-2 update rule} can be solved via seimidefinite programming.
\end{proposition}
Semidefinite programs~\cite{Vandenberghe1996} can be solved efficiently by polynomial-time algorithms~\cite{Khachiyan1980,AHK05,AK07,AHK12} and have many applications in combinatorial optimization  and quantum information (e.g., \cite{Jain2011a,Watrous2009,Wang2018}). Therefore the updated state $\omega_t$ in Eq.~\eqref{eq: Tsallis-2 update rule} can be computed efficiently in polynomial time in the dimension of the state. The CVX software~\cite{Grant2008} allows us to compute SDPs in practice.

We remark that Algorithm 1 is more practical on near-term quantum computers than previous online learning methods~\cite{Aaronson2018a,Yang2020} in the sense that the predicted state $\omega_t$ in Eq.~\eqref{eq: Tsallis-2 update rule} can be prepared using parameterized quantum circuits. We discuss the details in Section~\ref{sec:near term}.

Furthermore, we show that Algorithm \ref{alg:rftl} achieves an $\operatorname{O}(\sqrt{T})$ regret bound, which is stated formally in Theorem \ref{thm:regret bound}. A detailed proof is provided in Appendix \ref{app: tallis2}.
\begin{theorem}\label{thm:regret bound}
Suppose that the maximum Frobenius norm of $\{E_t\}_{t=1}^T$ is $\lambda$, i.e., $\lambda= \max_t \|E_t\|_2$. By setting $\eta=\frac{1}{L\lambda\sqrt T}$ the regret of Algorithm $1$ is bounded by
\begin{align}\label{:eq:formal bound}
\operatorname{Reg}_T\le 2L\lambda\sqrt{T}.
\end{align}
where $T$ is the number of rounds and $L$ is the Lipschitz constant of the loss function.
\end{theorem}

This theoretical bound on the regret provides powerful and flexible estimations on the performance of Algorithm 1. On one hand, it achieves an $O(\sqrt{T})$ regret bound, and thus the average regret per round is approaching to $0$ when $T$ goes to infinity. On the other hand, it could assess the performance of regret for measurement operators with specific constraints or conditions. This is well-motivated due to two reasons. First, practically, the ability to perform measurements is limited, and there are usually technical limitations with the near-term quantum hardware. Second, theoretically, there are particular classes of measurements of great interest, such as projective or von Neumann measurements. In the following Corollary, we apply our estimation of regret to the case of limited-rank measurements and then generalize the estimation for general measurements.
\begin{corollary}
\label{coro: tallis2}
Suppose that the rank of each measurement operators is bounded by $M$. Then by setting $\eta = \frac{1}{L\sqrt{TM}}$, the regret of Algorithm 1 satisfies that
\begin{align}\label{eq:rank bound}
\operatorname{Reg}_T\le 2L\sqrt{MT}.
\end{align}
For general measurement operators, we have
 \begin{align}\label{eq:dimension bound}
\operatorname{Reg}_T \le 2L\sqrt{dT}.
\end{align}
\end{corollary}

\begin{remark}
\label{remark: relation with OGD}
We remark that the Online Gradient Descent (OGD) algorithm proposed in \cite{Yang2020} could also achieve the same regret bound as the RFTL method with Tsallis-2 regularizer by more careful analysis. But their algorithm is less efficient than ours, and they haven't discussed its implementation on quantum computing devices.
\end{remark}

\subsection{Variational quantum algorithm for state prediction}\label{sec:near term}
We further introduce a variational quantum algorithm to generate the state in Eq.~\eqref{eq: Tsallis-2 update rule}. In particular, the Tsallis-2 regularizer can be estimated efficiently via the well-known Swap Test~\cite{Buhrman2001,Gottesman2001a} or the recently developed Destructive Swap Test~\cite{Subasi2019}, which allows us to compute the state overlap between two quantum states $\rho$ and $\sigma$. Compared to the general Swap test, the Destructive Swap Test is more practical on near-term devices such as IBM's and Rigetti's quantum computers~\cite{Cross2017b,Smith2016}, since it is ancilla-free and costs less circuit depth and the number of the gates. More details could be found in~\cite{Subasi2019}.
Therefore, we introduce the following variational quantum algorithm for state prediction, which could be implemented on near-term quantum devices.

\begin{algorithm}[H]
\caption{Variational quantum algorithm for state prediction}
\begin{algorithmic}[1]
\STATE Input: choose the ansatz of unitary $U(\bm\theta)$, tolerance $\varepsilon$, and initial parameters of $\bm\theta$;

\STATE Apply  $U(\bm\theta)$ to three copies of the initial state $\ket{00}_{AR}$, and obtain three copies of the marginal states $\rho_{A_1} = \rho_{A_2} = \rho_{A_3} = \tr_R U(\bm\theta)\proj{00}_{AR}U(\bm\theta)^\dagger$;

\STATE Compute  $\tr (H\rho_{A_1})$  and update cost function $L_1 =  \tr (H\rho_{A_1} )$, with $H = \eta \sum_{s=1}^{t-1} \tr\left(\nabla_{s} \rho_{A_1}\right)$;

\STATE Compute the state overlap $\tr (\rho_{A_2}\rho_{A_3})$ and update the cost function $L_2 =\tr(\rho_{A_2}\rho_{A_{3}})-1$;

\STATE Perform optimization of $\cL(\bm\theta) = L_1+L_2$ and update parameters of $\bm\theta$;
\STATE Repeat 2-5 until the cost function $\cF_2(\bm\theta)$ converges with tolerance $\varepsilon$;
\STATE Output the state $\rho^{out} = \tr_R U(\bm\theta)\proj{00}_{AR}U(\bm\theta)^\dagger$;
\end{algorithmic}
\end{algorithm}

\section{Learning for small loss Bounds }
\label{sec: small_loss}

Recall that $L_T^* = \min_{\omega \in \cS_d} \sum_{t=1}^T \ell_t(\tr E_t \omega)$. The existing results in~\cite{Aaronson2018a} and our previous results deal with the worst-case data where the adversary is fully adaptive to the learner's strategy, which means that the $L_T^*$ can be as large as $T$ if $b_t$ is chosen arbitrarily. In reality, however, the feedback $b_t$ usually comes from the empirical mean of outcomes from single or multiple measurements we implement at each round. In particular, if we are allowed to have a relatively large amount of state copies to measure at each round, then the difference between $b_t$ and $\operatorname{Tr}(E_t\rho)$ will be small, which means $L_T^*$ is small.  So the motivation is to design a more adaptive algorithm that can achieve small regret in such an easier case.

In this section, we obtain a "small loss" bound for $L_2$ norm, which means that the regret bound depends on $L_T^*$ instead of on $T$, so that the regret is small when $L_T^*$ is small.

\subsection{Small loss bound algorithm and results}

\begin{algorithm}[H]
\caption{Doubling Trick for MEG and RFTL-vonNeumann}
\label{algo: small loss}
\begin{algorithmic}[1] \label{alg:main}
\STATE Input: number of rounds $T$, $\eta<\frac{1}{2}$, dimension $d$.
\STATE Set $T_1 = 1, t=1$.
\FOR[$\beta$ indexes a block]{ $\beta = 1,2,\ldots$}
    \STATE Set $\eta_\beta = \min\{ \sqrt{\frac{n\ln 2}{2^\beta + 1}}, \frac{1}{2}\}$ \label{line: update learnint rate}
    \WHILE{ $t \leq T$ }
        \IF{$t \neq T_\beta$}

            \STATE \textbf{OPTION I:} predict \quad $\omega_t \leftarrow \textsc{MEGUPDATE}\left(\omega_{t-1},\nabla_{t-1},\eta_\beta\right)$

            \STATE \textbf{OPTION II:} predict \quad $\omega_t \leftarrow \textsc{RFTLUPDATE}\left(\{ \nabla_s\}_{s \in [T_\beta,t-1]},\eta_\beta\right)$
        \ELSE
            \STATE  predict $\omega_t  \leftarrow 2^{-n} \mathbbm{I}$
        \ENDIF
        \STATE Compute $\nabla_t = 2\left(\operatorname{Tr}(E_{t}\omega_{t})-b_{t}\right)E_t$
        \STATE Update the cumulative loss inside current block $L_\beta = \sum_{s = T_\beta}^t\ell_s(\tr E_{s}\omega_s)$
        \IF{ $L_\beta \geq 2^\beta$} \label{line: test}
            \STATE $T_{\beta+1} \leftarrow t+1$, $t \leftarrow t+1$ \COMMENT{$T_\beta$ represent the start time of block $\beta$}
            \STATE \textbf{break}
        \ENDIF
        \STATE $t\leftarrow t+1$
    \ENDWHILE
\ENDFOR
\end{algorithmic}
\end{algorithm}

The authors of \cite{Tsuda2005} show that Matrix Exponentiated Gradient (MEG) updates can achieve the optimal small loss bound  $\operatorname{O}\left(\sqrt{nL_T^*} + n \right)$ with a pre-selected learning rate $\eta = \operatorname{O}(\sqrt{\frac{n}{L_T^*}})$. Unfortunately, in practice, $L_T^*$ is usually unknown and can even be chosen adaptively by the adversary during the learning process.

Here we show a parameter-free small loss algorithm for both MEG and RFTL-vonNeumann by using a doubling trick to adjust the learning rate. The main idea is to use the cumulative loss $\sum_{s=1}^t \ell_s(\omega_s)$ as a proxy for the target hindsight loss $L_T^*$. But directly choosing $\eta_t = \frac{n}{\sum_{s=1}^t \ell_s(\tr E_{s}\omega_s)}$ will cause unstable updates of $\omega_t$. Therefore, we divide the time horizon into several blocks. Each time the cumulative loss within block $\beta$ exceeds $2^\beta$, the algorithm restarts into a new block as shown in line~\ref{line: test} and updates the learning rate as shown in line~\ref{line: update learnint rate}, so that $\eta_t$ always stays between $\left[\sqrt{\frac{n\ln2}{\sum_{s=1}^t \ell_s(\tr E_{s}\omega_s)+1}},\sqrt{\frac{2n\ln2}{\sum_{s=1}^t \ell_s(\tr E_{s}\omega_s)+1}}\right]$, except the first several blocks.

\begin{algorithm}[H]
\caption{MEGUPDATE($\omega_{t-1},\nabla_{t-1},\eta_t$)}
\begin{algorithmic}[1] \label{alg:update}

    \STATE $G_{t} \leftarrow \log \omega_{t-1} - \eta_t\nabla_{t-1} $
    \STATE $\omega_t \leftarrow \frac{\exp(G_{t})}{\operatorname{Tr} (\exp(G_t))}$
    \STATE \textbf{Return} $\omega_t$
\end{algorithmic}
\end{algorithm}

\begin{algorithm}[H]
\caption{RFTLUPDATE$\left(\{\nabla_s\}_{s \in [T_\beta,t-1]},\eta_t\right)$}
\begin{algorithmic}[1] \label{alg:update}

    \STATE $\omega_t \leftarrow \argmin_{\omega \in \cS_d} \left\{ \eta_t \sum_{s=T_\beta}^{t-1} \left(\nabla_s \omega\right) + \operatorname{Tr}(\omega \log \omega) \right\}$
    \STATE \textbf{Return} $\omega_t$
\end{algorithmic}
\end{algorithm}

\begin{theorem}
\label{thm: small loss}
Let $E_1,E_2,\ldots$ be a sequence of adaptively chosen two-outcome measurements on an $n$-qubit state presented to learner. The sequence of losses is defined as $\ell_t(z) = \left(z-b_t\right)^2$, where $b_t$ can also be chosen adaptively and will be presented be learner. Then algorithm~\ref{algo: small loss} with either update method guarantees a near-optimal regret
\begin{align*}
    \operatorname{Reg}_T \le \operatorname{O}\left(\sqrt{n L_T^*\log T}+n\log T \right)
\end{align*}.
\end{theorem}

\begin{corollary}
\label{coro: small loss}
When $L_T^* = 0$, which is the realizable case, the regret becomes $\operatorname{O}(n\log T)$. This almost matches the lower bound $\operatorname{\Omega}(n)$ stated in \cite{Aaronson2018a}.
\end{corollary}

\subsection{Regret Analysis}
The first key step is to get the following lemma, which is inspired by \cite{Tsuda2005}, \cite{Youssry2019}.

\begin{lemma}
\label{lem: small loss}
Given the loss function $\ell_t(z) = \left( z - b_t\right)^2$ with measurement operator $-\mathbbm{I} \leq E_t \leq \mathbbm{I}$. If $\omega_t$ is the result of $\textsc{MEGUPDATE}$ or $\textsc{RFTLUPDATE}$ with learning rate $\eta \in (0,\frac{1}{2})$, then for any state $\rho$,
\begin{align*}
    \eta \ell_{t-1}(\tr E_{t-1} \omega_{t-1}) - \frac{\eta}{1-2\eta} \ell_{t-1}(\tr E_{t-1} \rho)\leq B(\rho\|\omega_{t-1}) - B(\rho\|\omega_{t})
\end{align*}
where $B(X\|Y) = \operatorname{Tr}\left(X\ln X - X \ln Y \right)$, which is the Bregman divergence of von Neumann entropy.

\end{lemma}

\begin{remark}
This lemma indicates that MEG and RFTL-vonNeumann is inherently similar. Note that MEG is modified from online mirror descent (OMD) with von Neumann Entropy and RFTL is well known to have a strong connection to OMD (see, e.g., \cite{Hazan2016,Shalev-Shwartz2007,Abernethy2009}). So this lemma matches our intuition behind this connection.
\end{remark}

Then for any block $\beta$ except the last one, by rearranging the equation in Lemma~\ref{lem: small loss} and summing up all the losses within block $\beta$, we have
\begin{align*}

    \sum_{t = T_{\beta}}^{T_{\beta+1}-1} \ell_{t}(\tr E_{t}\omega_{t}) - \ell_{t}(\tr E_{t} \rho)
    \leq \frac{1}{\eta_\beta}\max_\rho \left\{B(\rho\|2^{-n} \mathbbm{I}) \right\} + 2\eta_\beta \sum_{t=T_\beta}^{T_{\beta+1}-1} \ell_t(\tr E_{t} \omega_t)
\end{align*}

Our learning rate adjusting strategy guarantees that this is always upper bounded by $\operatorname{O}\left( \sqrt{\sum_{t=T_\beta}^{T_{\beta+1}-1} \ell_t(\tr E_{t}\omega_t)}\right)$. So by summing up the losses over all blocks gives us the result. We postpone a complete version of proof to Appendix~\ref{app: small loss}.

\section{Numerical experiments}

In this section, we evaluate the RFTL-Tallis-2 and Doubling Trick algorithms with a series of numerical experiments.  For the first algorithm, we compare the performances of RFTL-Tallis-2 under the full rank measurement and 1-rank measurement, to the performances of RFTL-vonNeumann under those two measurements; For the second algorithm, we compare the performances of RFTL-vonNeumann and MEG equipped with doubling trick (TD) learning rate adjustment strategy to those unequipped.

\paragraph{Feedbacks}  Suppose there is an underlying unknown quantum state $\rho$, pure or mixed, to be learned. We first consider the noiseless realizable setting for both algorithms. In each round, the learner will observe $b_t = \tr(E_t\rho)$, which is the exact probability of getting "Yes" measurement outcome. In addition, we also consider the noisy non-realizable settings for the small loss experiment. To be specific, we assume that a certain state copy is available for testing at each round, and each test will give us a Bernoulli feedback with "Yes" probability $\tr(E_t\rho)$, with some added small noise. Here we choose the parameters $100$ state copies and $0.05*N(0,0.1)$ Gaussian noise.

\paragraph{Adversary's data generation strategy} For the adaptive adverserial setting, generating a worst-case data sequence to fool an algorithm is challenging. Here we first use the same method in \cite{Yang2020} to generate $E_t = \argmax_E \left( \tr(E(\omega_t - \rho)) \right)^2$ at each round. Moreover, we observe that rank-1 measurement can only maximize the loss in one direction. So if we randomly generate a mixed state $\rho$, then the losses in rank-1 measurement settings are likely to be much smaller than ones in full rank measurement settings, which is not easy for numerical comparison. Therefore we set $\rho$ to be pure so that the spectral norm of $\omega_t - \rho$ becomes larger than that in the mixed state case, and therefore the losses are of the same order of magnitude as those in full rank measurement settings.

\paragraph{Other parameters} We choose the $L_2$ norm loss. We take the number of qubits $n = 4$, so the dimension of the density matrices is $16 \times 16$. For each experiment, we run for 100 trials with randomly generated target states $\rho$ and report the average curves.

\subsection{Experiment Results}

The first experimental results are illustrated in Figure~\ref{fig: experiment} (a). It is easy to see that when the rank of measurement is $1$, Tallis-2 Entropy regularizer takes the advantage over Von Neumann regularizer. On the other hand, when the measurement is full rank, these two algorithms perform almost the same. Notice that theoretically, Von Neumann regularizer should take advantage over Tallis-2 Entropy regularizer in the full rank when $n$ is large. But due to the device limitation, we leave the experiment on high dimensional states for future study.

The second experimental results are illustrated in Figure~\ref{fig: experiment} (b)(c). The results clearly show that the doubling trick makes both RFTL-vonNeumann and MEG more adaptive to the small hindsight loss. Besides, we also observe that the MEG and RFTL-vonNeumann have very close performances, which matches our intuition in Lemma~\ref{lem: small loss}.

\begin{figure}[H]
	\centering
	\begin{adjustwidth}{0.1cm}{0.1cm}
		\begin{tikzpicture}
 		\node at (-5.0,3.0) {\includegraphics[width = 4.55cm]{rankLimit}};
		\node at (-5,0.6) {\small (a)  vonNeumann vs. Tallis-2};

		\node at (-0.1,3.0) {\includegraphics[width = 4.55cm]{smallLoss_noisefree}};
		\node at (0.05,0.6) {\small (b) small loss algorithm with $L^* =0$};

		\node at (4.8,3.0) {\includegraphics[width = 4.55cm]{smallLoss_noisy}};
		\node at (5.3,0.6) {\small (c) small loss algorithm with $L^*$ is small};

		\end{tikzpicture}
	\end{adjustwidth}
\caption{The experimental results for  online learning of $4$-qubit states with average regret as the metric.}
\label{fig: experiment}
\end{figure}

\section{Summary}
In summary, we have developed more practical and adaptive algorithms for online state learning. We have proposed an RFTL algorithm with Tallis-2 entropy that can guarantee to predict the quantum states with regret bounded by $\operatorname{O}(\sqrt{MT})$ for the first $T$ measurements with maximum rank $M$. This regret bound of our algorithm has advantages over previous methods in the setting with low-rank measurements since it only depends on the rank of the measurements rather than the number of qubits. In particular, we have shown a variational hybrid quantum-classical algorithm for state prediction in the RFTL algorithm, which could be run on near-term quantum computers. We have also proposed a parameter-free algorithm that can achieve a regret depending on the loss of best states in hindsight.

For future work, it will be essential to develop tight generalizations of our results to measurements with multiple outcomes. Another interesting direction is to find lower and upper bounds in different practical and adaptive regimes, which could help us better understand the fundamental limits of online state learning.

\textbf{Acknowledgements}: This work was done when YC was a research intern at Baidu Research.

\newpage

\bibliographystyle{alpha}

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This fact sheet describes the nutritional value and safe storage of dried potatoes, a commodity food. It also offers food preparation ideas.
Anding, Jenna (2008). Cooking with Dried Potatoes. Available electronically from http : / /hdl .handle .net /1969 .1 /87040.


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text:
Eunostos (griechisch ) steht für

 Eunostos (Gottheit), antike griechische Gottheit der Getreidemühlen
 Eunostos (Heros), in der griechischen Mythologie Heros in Tanagra
 Eunostos (Soloi), König von Soloi auf Zypern
 Eunostos (Bruder des Pythagoras), nach Porphyrios ein Bruder des Pythagoras

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↑ Shukla, V. (2015). "'Survival of the fittest is not Darwin's phrase'". The Tribune (August 23).
↑ शर्मा, ओपी (२०१६). "पुंदरियों ने जब गोरखा सेना को लूटा". जनसत्ता(हिमसत्ता) (अगस्त, २).
↑ अज्ञात (2014). "चुनाव का बहिष्कार करेंगे बमटा के ग्रामीण". अमर उजाला (अप्रैल, 25).
↑ फ्रीडमैन, थ एल (२०१५). "प्रकृति सियासत नहीं करती". अमर उजाला (अगस्त, २०).


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A review to a will: Issue of last French king in battle royale for Bond chateau
ALICE WRIGHT
Chateau de Chantilly, which famously featured in 1985 Bond movie A View To A Kill, is at the centre of an extraordinary row between a claimant of the French throne and the owner who wants to turn it into a luxury hotel.
Jean d'Orleans, the Count of Paris, says the Institute of France who now owns his ancestral home has flouted his ancestor's wish that it should not alter the estate.
He argues therefore that the bequest should be cancelled under the original conditions and the chateau returned to his aristocratic family, along with its 17,000 acres of grounds, its extraordinary collection of artworks and its Great Stables.
The original chateau was destroyed during the French Revolution but rebuilt in the 19th century by Henri d'Orleans, Duke of Aumale – the son of Louis Philippe I, who ruled as France's last king between 1830 and 1848.
Chateau de Chantilly famously featured in 1985 Bond movie A View To A Kill
In 1886, the duke donated his estate to the Institute of France – an umbrella body for the country's most eminent intellectual councils that functions in a similar manner to the National Trust in the UK.
The duke made strict conditions to the donation, however. He requested that the architecture of the chateau and its outbuildings would never be modified or the objects in them moved, including the great collection of paintings by the likes of Raphael, Van Dyck, Ingres and Delacroix.
Failure to respect these wishes would lead to the revocation of the bequest and the return of the estate to the duke's family.
His descendant, the Count of Paris, who is one of at least two people with a claim to the throne of France – if it were ever to become a monarchy again – is now taking on the challenge of restoring his family estate.
The Count of Paris, who is one of at least two people with a claim to the throne of France, is now taking on the challenge of restoring his family estate
He believes the institute has breached the conditions by approving plans to create a luxury, £650-a-night hotel complete with a spa and restaurant in the Pavillon d'Enghien, an annex of the chateau.
In another twist to the castle's tale, the hotel scheme is currently on hold after anti-fraud prosecutors opened an investigation into a potential rigged tender process.
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Home/John Wayne/The truth about the relationship of two legendary gunmen: John Wayne and James Arness.
The truth about the relationship of two legendary gunmen: John Wayne and James Arness.
Thanks to a wonderful friendship and a recommendation, John Wayne helped James Arness land the role of Marshal Matt Dillon on "Gunsmoke."Before getting to the "Gunsmoke" news, let's take a quick look back upon the friendship between "The Duke" and Arness.Arness co-starred with Wayne in four different pictures, "Big Jim McLain" in 1952, "Hondo" and "Island in the Sky" in 1953, and "The Sea Chase" in 1955. All of this is according to an article from ME.tv.
James Arness Can Thank John Wayne For Iconic TV Role
In 1955, "Gunsmoke" was making a move from radio into TV. This was during what some people term as TV's "Golden Age" because so many shows aired in black-and-white during those times.The show itself already had an audience from its long-running radio presence. Actor William Conrad, best-known as private detective Bill Cannon from CBS' series "Cannon," was the radio voice of Dillon. He was up for the TV role along with, wait for it, Wayne.
But Wayne apparently didn't want to go away from his growing movie career. Therefore, he suggested the show's producers take a look at his friend Arness for the lead role.History tells us the rest of the story. Arness became Matt Dillon, portraying the tough law-keeping marshal for 20 seasons on CBS. "Gunsmoke" happened to be one of those network TV shows starting out in black-and-white before turning to color episodes once they became more prevalent.
John Wayne Hypes Up First 'Gunsmoke' Episode On CBS
The friendship between Wayne and Arness also takes another twist. Both men shared the same birthday. Wayne was born on May 26, 1907, while Arness was born on May 26, 1923.Just how tight were these two Western stars? Well, Wayne, who made some TV appearances, took time out of his film schedule to formally introduce Arness and "Gunsmoke" to CBS viewers in 1955.It's truly rare when something like this takes place. I mean, it's John Wayne we're talking about here…not just any other actor. This is a special moment where Wayne gives his buddy James Arness a nice boost to new TV viewers for "Gunsmoke."
Let's jump into the time machine and take a look at "The Duke" introducing Arness before the first "Gunsmoke" episode on CBS.
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