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  • The learner repeated this process for a total of 30 rounds (this number was chosen due to budgetary constraints). Since not all experiments passed quality control, 30 rounds accounted for 2697 experiments (~29%) of the total 96x96 experiment space, which covered 1670 experiments (~72%) in the underlying (unduplicated) 48x48 experiment space. During the process of data collection, there was no human intervention as to which experiments to perform, nor did we attempt any assessment of the performance of the learner. To complete the dataset, the 634 combinations of drug and clone (ignoring duplication) that had not been chosen by the learner were collected after the 30 rounds. We also collected images for all drugs for six clones outside the set of 48 that the learner knew about.

    After data collection had been completed, we first asked whether the active learner accurately predicted phenotypes for unobserved experiments. We assessed the accuracy of the predictions of a model from a given round using data it had not yet seen: data from the 'completion' experiments as well as from experiments that were performed in subsequent rounds. Each prediction for an experiment consisted of a phenotype from the set of phenotypes observed so far, and was considered correct if a plurality of images from that experiment were closest (in the feature space) to an image from an observed experiment that had been assigned to the same phenotype. That is, for each of the images from an as yet unobserved experiment, the image was found that was closest to it out of all that the learner had already observed. The phenotype that this closest image had been assigned by the learner was given to the image from the unobserved experiment. The number of unobserved images assigned to each phenotype was then counted, and if more images were assigned to the predicted phenotype than any other individual phenotype, the prediction was considered correct (note that the phenotypes themselves may change from round to round). This definition parallels the nearest-neighbor methods used for clustering (see Materials and methods). Using this definition, the accuracy of the model learned after each round was retrospectively calculated ( Figure 2 , black line).

    Figure 2
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    The performance of the active learner (black line) generally increased superlinearly as more data were acquired. Hypothetical models (dotted gray lines) with fixed generalization accuracy have …

    We then asked whether the last actively learned model could make accurate predictions for the six clones the learner had not seen. Given just the images for the unperturbed (no-drug) condition for the six clones, we generated predictions for the remaining 47x6 experiments and assessed them using the same nearest neighbor plurality approach described above. As a baseline accuracy for these predictions, we can consider what predictions we could make without having learned a model. In this case, we have no way of knowing what phenotypes are possible (other than the unperturbed phenotypes), and must predict that no matter what drug is added the phenotype would remain unperturbed. For the six unseen clones, this would lead to an accuracy of 85%, i.e., 15% of the experiments showed a phenotype different from the unperturbed phenotype for that clone. The model from active learning did much better than this, giving an accuracy of 98% (only 5–6 out of 282 experiments were incorrectly predicted). As can be expected, the learner’s predictions were also significantly better than expected for random guessing if we are given the set of possible phenotypes ( P <0.05, Multinomial test). This high accuracy at generalizing to new clones suggests that the final model had captured quite well the effects of the drugs on the localization of any target (at least for targets somewhat similar to the original set).

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    Recognize your favorite air or space enthusiast. Add his or her name to the Museum’s Wall of Honor.

    Posted on Tue, July 3, 2018

    If you’re an observant visitor, you might have noticed that the American flag on the side of the Space Shuttle Discovery on display at our Steven F. Udvar Hazy Center appears to be backwards.

    No, it’s not a very large (literally and figuratively) NASA snafu. The “backwards” flag is actually part of the US Flag Code, which applies to spacecraft, aircraft, and even service members’ uniform insignia.

    According to United States Army regulations, “The full-color US flag cloth replica is worn so that the star field faces forward, or to the flag’s own right.” The flag must always be positioned to look like it is flying forward, so it’s really all about perspective.

    Portrait of the prime crew of the Apollo 11 lunar landing mission. From left to right they are: Commander, Neil A. Armstrong, Command Module Pilot, Michael Collins, and Lunar Module Pilot, Edwin E. Aldrin Jr. On July 20th 1969 at 4:18 PM, EDT the Lunar Module "Eagle" landed in a region of the Moon called the Mare Tranquillitatis, also known as the Sea of Tranquillity. After securing his spacecraft, Armstrong radioed back to earth: "Houston, Tranquility Base here, the Eagle has landed". At 10:56 p.m. that same evening and witnessed by a worldwide television audience, Neil Armstrong stepped off the "Eagle's landing pad onto the lunar surface and said: "That's one small step for a man, one giant leap for mankind." He became the first human to set foot upon the Moon.

    If you have the flag positioned on your left shoulder (as you can see on the Apollo astronaut’s spacesuits), then the flag appears as it would flying in the breeze on a flag pole—with the star field facing forward as the wearer moves forward. On other military uniforms, you’ll see what’s called the “reverse side flag” on the right sleeve.

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    The other thing I noticed was that Spufford knows about the Australia paradox! OK, he doesn’t call it by that name, but it’s recognizably the same thing. I’d not before realized how applicable the Australia paradox is to storytelling. The real Australia really does exist, but places in stories are stage sets. Obvious, really, but I’d never thought of it that way before.

    One other thing: Spufford says in his book that he’s written lots of book reviews (“a couple hundred thousand words of literary journalism”) but is only including a few of these because he “was never very good at declaring sufficient independence from the book at hand, and producing something that I wanted to say, in a form worth keeping.” I don’t care, I want to read them all, or at least a lot of them. Remember Orwell’s 4-volume collected essays, letters, and journalism? Of course you do. I’d love to see a big fat book with small type, crammed with just about every review that Spufford’s ever written. And it’s not like I’m the world’s biggest Spufford fan. I just like to read book reviews. Too bad that the market for books is declining (competition with free stuff online such as this blog!) so we’re not going to see such a collection. I remember running into Alfred Kazin’s son once and asking if they could put together a collection of his dad’s unpublished book reviews , but he didn’t seem at all interested in the idea.

    P.S. I wrote this post around six months and it just happened to come up today, just coincidentally right after Dan’s literature-related .

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    Posted by Dan Simpson on 28 June 2018, 7:19 pm

    Pryor unhooks the deer’s skull from the wall above his still-curled-up companion. Examines it. Not a good specimen –the back half of the lower jaw’s missing, a gap that, with the open cranial cavity, makes room enough for Pryor’s head.

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    So as we roll into the last dying embers of the flaming glory that is (North American) Pride Month (Europe you’ve still got some fun ahead), I’ve doing my best to make both good and questionable decisions.

    The good decision was to read an absolutely fabulous book by a British author named Will Eaves (on a run of absolutely stunning books ) who fairly recently released a book called Murmur. Murmur is an odd beast, I guess you could say it’s a novel about Alan Turing but that would be moderately inaccurate and probably unfair.

    Firstly, because even from one of my favourite authors, I am basically allergic to people writing about maths and mathematicians. It’s always so stodgy and wooden, as if they are writing about a world they don’t understand but also can’t convincingly fake. (Pride month analogy: I mostly avoid straight people writing queer stories for the same reason.) And Turing is a particular disaster for cultural portrayals: he intersects with too many complicated things (world war 2, cryptography, computers, pre-1967 British homosexuality) for him to ever be anything but an avatar.

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