Compression, physical XORs, and NN clustering!

Today, Samson and Diran gave new twists and applications for their previous topics.

Lossless Dataset Compression: Samson discussed lossless compression of statistical data, capable of shrinking huge datasets (often hundreds of TB) down by orders of magnitude. The goal, instead of trying to file away the data into a smaller footprint than other compressors, is to rather allow the data to still be readily accessible while being compressed.

Encryption, Encore: Diran described a way to transmit secret messages across untrustworthy carriers. Using the previously discussed holomorphic encryption, and splitting the message into multiple planes coupled with random noise, one ensures that no single carrier possessing only some of the planes is able to compute the secret. Rather, decryption requires XORing every plane together, which can in fact be done physically with multiple layers of invisible ink, or a unique way of encoding 1s and 0s that allows the union of dark spots to simulate XORs.

Neural Network Clustering: As part of his “neural network” series, Samson introduced a simple, single-hidden-layer network capable of determining, in a spatially clustered dataset, which cluster (or subset of clusters, “0” or “1”) an arbitrary data point should be marked under. The hidden layer is a Gaussian of the vector distance between a given point and each cluster, such that nearer clusters have greater influence. Finding the optimal weights during training requires only least-squares regression, allowing many data points to be trained at once.

Have you gone mod?: Jimmy offered a counterargument to Lisa’s proposal that sequential numbers, such as user IDs of a website, would yield non-random subsets when their residue (mod a certain number) is taken.

Next meeting will be next Tuesday Nov 22nd at 3pm in the Stats Club Office M3 3109.

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