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This can be understood as counting the number of ways of not hitting (b-f) many bins out of b, and then putting at least one ball into f many bins with a total of z throws. The total number of ways of throwing balls without fill constraints is the denominator for this case of distinguishable bins (bins are the experiments, which are distinguishable). We then apply the formula above to compute a p-value for the chance that the coverage of quads observed for the actively learner could have been achieved at random. For the last actively learned model, there were z =2697 'balls' thrown into b =2304 bins. Since that model covered f =1670, we then sum the probabilities when covering f =1670..2304 bins to compute the probability that at least 1670 bins would have been hit by a random process. We computed this with the aid of Mathematica .

SURF ( Neoprene Sneakers in Swan and Fuchsia Neoprene and Rubber Marni SP8U7w14W
) features were calculated for each image using just the GFP channel, restricting the interest points to be within ~10μm (150 pixels) of a segmented nucleus. The distributions of these interest point features per image were the atoms of classification in a nearest neighbor two-class classifier (whether or not an image was out-of-focus or contained artifacts), where inter-atom distances corresponded to a kernelized two-sample test as described elsewhere ( Capri Menta sneakers Green KOIO BPtf4hl
). To label these data, repeated and nearly exhaustive manual annotation over many iterations were performed.

Fa2h-tagged cells were plated at the same density as for the active learning study, with the exception of being plated in 96-well plates (Nunc) in order to accommodate the confocal microscope. The same previously generated drug aliquots from stock were used to match the active learning conditions as closely as possible. The automated microscope used in the active learning study did not align image fields to center cells, and so to simulate comparable imaging conditions (and any field level feature artifacts) no attempt was made to center cells in fields or to adjust imaging settings (0.4s and 0.8s exposure for 440 and 488nm, fixed gain at 300 (arb. units)). Five (5) fields were taken per well. Sequential wells cycled through each of the three treatments (drugs 16, 4, and 48). 106 fields were acquired over 4hr, a period starting from +2hr after drug addition, through the +5hr timepoint used for the active screen, to +6hr. 33 fields were discarded for being low contrast, generally occurring at time points immediately after laser and microscope restarts due to hardware and software faults. SLF34 features were calculated per field as before, and Gram-Schmidt process feature selection was used to select 71 features for further use. Classification was by three-fold cross-validated L2-penalized logistic classification and used all 73 fields passing quality control (1–2 cells or cell fragments/field). Archetypical cells were chosen as the centers of the minimax hierarchical clustering ( Bien and Tibshirani, 2011 ) of the SLF34 features of each image containing one cell; the highest level of the cluster tree containing 5 nontrivial (nonsingular) clusters was used. Archetype decompositions of the other fields (including polynucliated and multiple cell fields) was calculated by Lasso regression by Mairal’s method ( Preowned Ostrich heels Celine nSVNb8
) with the penalization term (set to 1.0) chosen heuristically to force sparse decompositions.

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Uwe Ohler
Reviewing Editor; Duke, Germany

In the interests of transparency, eLife includes the editorial decision letter and accompanying author responses. A lightly edited version of the letter sent to the authors after peer review is shown, indicating the most substantive concerns; minor comments are not usually included.

Thank you for submitting your work entitled "Active Machine Learning-driven Experimentation to Determine Compound Effects on Protein Patterns" for peer review at eLife . Your submission has been favorably evaluated by Aviv Regev (Senior editor), Uwe Ohler (Reviewing editor), and three reviewers.

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Mathias Eitz, James Hays and Marc Alexa

Humans have used sketching to depict our visual world since prehistoric times. Even today, sketching is possibly the only rendering technique readily available to all humans. This paper is the first large scale exploration of human sketches. We analyze the distribution of non-expert sketches of everyday objects such as 'teapot' or 'car'. We ask humans to sketch objects of a given category and gather 20,000 unique sketches evenly distributed over 250 object categories. With this dataset we perform a perceptual study and find that humans can correctly identify the object category of a sketch 73% of the time. We compare human performance against computational recognition methods. We develop a bag-of-features sketch representation and use multi-class support vector machines, trained on our sketch dataset, to classify sketches. The resulting recognition method is able to identify unknown sketches with 56% accuracy (chance is 0.4%). Based on the computational model, we demonstrate an interactive sketch recognition system. We release the complete crowd-sourced dataset of sketches to the community.

Downloads

Note: temporal order of strokes is encoded in the SVG/Matlab dataset. Each stroke is a Bezier Spline, and strokes that have been drawn first are at the top of a file. The sketch dataset is licensed under a Eytys Woman Suede Sneakers Antique Rose Size 41 Eytys NHndoYZf
.

BibTeX

Human sketch recognition

Human classifications on the full dataset. In the left column of each category page, we show the sketches that have been correctly classified. In the middle column we show the sketches that actually belong to the category but have not been recognized. In the last column we show the false positives, i.e. those sketches that humans incorrectly predicted to belong to the category.

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Computational recognition

Computational classification results on the test dataset using the best-performing SVM model as described in the paper. In the first column of each category page, we show 5 samples of the training dataset. In the second column, we show sketches that have been correctly classified. In the third column we show the sketches that actually belong to the category but have not been recognized. In the last column we show the false positives, i.e. those sketches that have been incorrectly predicted to belong to that category.

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t-SNE layouts

For each category, we apply dimensionality reduction on the sketch feature space described in the paper (down to two dimensions). We plot the results as a 2D layout of the sketches that nicely illustrates the variety of sketching styles within each category.

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Representative sketches

For each category, we compute a representative, iconic sketch. We first cluster the category using mean shift. Next, for each cluster, we compute the average descriptor and identify the nearest neighbor of this average descriptor as the cluster representative.

Representative sketches »

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