The Secret Science of Synchronicity Paper

Several metaphysical/philosophical concepts are developed as tools by which we may further understand the essence, structure, and events/symbols of “Complex” Synchronicity, and how these differ from “Chain of Events” Synchronicity. The first tool is the concept of Astronomical vs Cultural time. This tool is to be the basis of distinguishing Simple from Complex Synchronicity as Complex Synchronicities are chunks of time that have several coincidences in common with each other. We will also look at the nature of the perspective of the time being quantized. The next tool is a particular case study of two movies, The Matrix and Black Swan, that may be viewed as an example of a Complex Synchronicity in the collective conscious of popular culture (as opposed to Simple Synchronicity or a single coincidence). And the final tool is the concept of “Chain of Events” synchronicity as a separate concept from Simple or Complex synchronicities. This 3rd tool is developed using a mathematical metaphor of foreshadowing (an element of storytelling) in the seemingly random pattern of prime numbers. The purpose of this paper is to distinguish and develop these concepts and to lay a foundation for the further study of the concept of Synchronicity first illuminated by Carl Jung as an acausal connecting principle between coincidences

Photon counting compressive depth mapping

We demonstrate a compressed sensing, photon counting lidar system based on the single-pixel camera. Our technique recovers both depth and intensity maps from a single under-sampled set of incoherent, linear projections of a scene of interest at ultra-low light levels around 0.5 picowatts. Only two-dimensional reconstructions are required to image a three-dimensional scene. We demonstrate intensity imaging and depth mapping at 256 x 256 pixel transverse resolution with acquisition times as short as 3 seconds. We also show novelty filtering, reconstructing only the difference between two instances of a scene. Finally, we acquire 32 x 32 pixel real-time video for three-dimensional object tracking at 14 frames-per-second.Comment: 16 pages, 8 figure

Fast Video Classification via Adaptive Cascading of Deep Models

Recent advances have enabled "oracle" classifiers that can classify across many classes and input distributions with high accuracy without retraining. However, these classifiers are relatively heavyweight, so that applying them to classify video is costly. We show that day-to-day video exhibits highly skewed class distributions over the short term, and that these distributions can be classified by much simpler models. We formulate the problem of detecting the short-term skews online and exploiting models based on it as a new sequential decision making problem dubbed the Online Bandit Problem, and present a new algorithm to solve it. When applied to recognizing faces in TV shows and movies, we realize end-to-end classification speedups of 2.4-7.8x/2.6-11.2x (on GPU/CPU) relative to a state-of-the-art convolutional neural network, at competitive accuracy.Comment: Accepted at IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 201

"'Who are you?' - Learning person specific classifiers from video"

We investigate the problem of automatically labelling faces of characters in TV or movie material with their names, using only weak supervision from automaticallyaligned subtitle and script text. Our previous work (Everingham et al. [8]) demonstrated promising results on the task, but the coverage of the method (proportion of video labelled) and generalization was limited by a restriction to frontal faces and nearest neighbour classification. In this paper we build on that method, extending the coverage greatly by the detection and recognition of characters in profile views. In addition, we make the following contributions: (i) seamless tracking, integration and recognition of profile and frontal detections, and (ii) a character specific multiple kernel classifier which is able to learn the features best able to discriminate between the characters. We report results on seven episodes of the TV series “Buffy the Vampire Slayer”, demonstrating significantly increased coverage and performance with respect to previous methods on this material

Learning to Extract Motion from Videos in Convolutional Neural Networks

This paper shows how to extract dense optical flow from videos with a convolutional neural network (CNN). The proposed model constitutes a potential building block for deeper architectures to allow using motion without resorting to an external algorithm, \eg for recognition in videos. We derive our network architecture from signal processing principles to provide desired invariances to image contrast, phase and texture. We constrain weights within the network to enforce strict rotation invariance and substantially reduce the number of parameters to learn. We demonstrate end-to-end training on only 8 sequences of the Middlebury dataset, orders of magnitude less than competing CNN-based motion estimation methods, and obtain comparable performance to classical methods on the Middlebury benchmark. Importantly, our method outputs a distributed representation of motion that allows representing multiple, transparent motions, and dynamic textures. Our contributions on network design and rotation invariance offer insights nonspecific to motion estimation

The Secret Science of Synchronicity Paper

Several metaphysical/philosophical concepts are developed as tools by which we may further understand the essence, structure, and events/symbols of “Complex” Synchronicity, and how these differ from “Chain of Events” Synchronicity. The first tool is the concept of Astronomical vs Cultural time. This tool is to be the basis of distinguishing Simple from Complex Synchronicity as Complex Synchronicities are chunks of time that have several coincidences in common with each other. We will also look at the nature of the perspective of the time being quantized. The next tool is a particular case study of two movies, The Matrix and Black Swan, that may be viewed as an example of a Complex Synchronicity in the collective conscious of popular culture (as opposed to Simple Synchronicity or a single coincidence). And the final tool is the concept of “Chain of Events” synchronicity as a separate concept from Simple or Complex synchronicities. This 3rd tool is developed using a mathematical metaphor of foreshadowing (an element of storytelling) in the seemingly random pattern of prime numbers. The purpose of this paper is to distinguish and develop these concepts and to lay a foundation for the further study of the concept of Synchronicity first illuminated by Carl Jung as an acausal connecting principle between coincidences