Category Independent Object Proposals Using Quantum Superposition

A vast amount of digital images and videos are continually being generated and shared across the Internet. An important step towards utilizing this ‘big data’ and deducing meaningful information from its visual contents, is to detect the presence of objects belonging to a particular class in digital images. Earlier computer vision algorithms devised for this purpose exhaustively search the entire image space for detecting objects belonging to a particular class. Object proposals aim to reduce this search space by proposing probable locations of objects in the image beforehand. This paves the way for efficiently using more computationally expensive and sophisticated detection algorithms. Conventional approaches to generating object proposals have revolved around learning a scoring function from the characteristics of objects in ground truth annotations of images. In this thesis, we propose a novel category independent proposal generation framework that is unsupervised and inspired by the psycho-visual analysis of human visual system where the search for objects gradually transitions from the most salient parts of a scene to comparatively non-salient regions. We use a state-of-the-art visual saliency estimation technique which proposes a unique relationship between spectral clustering and quantum mechanics. We improve this method by exploiting for the first time, the quantum superposition principle, to extend the search of objects beyond the salient ones. We also propose an unsupervised scoring strategy that does not incorporate any prior information about the spatial, color or textural features of objects. Experimental results have proved that our proposed methodology achieves comparable results with the contemporary state-of-the-art methods. Our unsupervised scoring strategy is shown to outperform, in some cases, the supervised frameworks employed by other methods. Moreover, it also enables us to achieve a three-fold decrease in the number of proposals while keeping the loss of recall to less than 3%. The success of our proposed methodology opens the door to a research direction where quantum mechanical principles can be utilized to enable computer vision algorithms to find objects in digital images without having any prior knowledge about them

Experimental motivation and empirical consistency in minimal no-collapse quantum mechanics

We analyze three important experimental domains (SQUIDs, molecular interferometry, and Bose-Einstein condensation) as well as quantum-biophysical studies of the neuronal apparatus to argue that (i) the universal validity of unitary dynamics and the superposition principle has been confirmed far into the mesoscopic and macroscopic realm in all experiments conducted thus far; (ii) all observed "restrictions" can be correctly and completely accounted for by taking into account environmental decoherence effects; (iii) no positive experimental evidence exists for physical state-vector collapse; (iv) the perception of single "outcomes" is likely to be explainable through decoherence effects in the neuronal apparatus. We also discuss recent progress in the understanding of the emergence of quantum probabilities and the objectification of observables. We conclude that it is not only viable, but moreover compelling to regard a minimal no-collapse quantum theory as a leading candidate for a physically motivated and empirically consistent interpretation of quantum mechanics.Comment: 24 pages, 11 figures, final published versio

Searching for New Physics Through AMO Precision Measurements

We briefly review recent experiments in atomic, molecular, and optical physics using precision measurements to search for physics beyond the Standard Model. We consider three main categories of experiments: searches for changes in fundamental constants, measurements of the anomalous magnetic moment of the electron, and searches for an electric dipole moment of the electron.Comment: Prepared for Comments on AMO Physics at Physica Script

The Depth and Breadth of John Bell's Physics

John Bell's investigations in foundations of quantum mechanics, particle physics, and quantum field theory are recalled.Comment: 46 pages, 1 figure, LaTeX; editorial corrections; email correspondence to R. Jackiw <[email protected]

Mathematical Foundations for a Compositional Distributional Model of Meaning

We propose a mathematical framework for a unification of the distributional theory of meaning in terms of vector space models, and a compositional theory for grammatical types, for which we rely on the algebra of Pregroups, introduced by Lambek. This mathematical framework enables us to compute the meaning of a well-typed sentence from the meanings of its constituents. Concretely, the type reductions of Pregroups are `lifted' to morphisms in a category, a procedure that transforms meanings of constituents into a meaning of the (well-typed) whole. Importantly, meanings of whole sentences live in a single space, independent of the grammatical structure of the sentence. Hence the inner-product can be used to compare meanings of arbitrary sentences, as it is for comparing the meanings of words in the distributional model. The mathematical structure we employ admits a purely diagrammatic calculus which exposes how the information flows between the words in a sentence in order to make up the meaning of the whole sentence. A variation of our `categorical model' which involves constraining the scalars of the vector spaces to the semiring of Booleans results in a Montague-style Boolean-valued semantics.Comment: to appea