Abstract. In this work we present an iterative multi-scale algorithm for motion estimation that follows mechanisms of motion processing in the human brain. Keeping the properties of a previously presented neural model of cortical motion integration we created a computationally fast algorithmic

precise flow fields. We combine the temporal integration with a recently proposed spatial multi scale approach [2]. The combination further improves the optic flow estimates when the image contains regions of different spatial frequencies and represents a very robust and efficient algorithm for optic flow

The principle aim of this dissertation is to investigate the philosophical application of quantum information theory to interpretational issues regarding the theory of quantum mechanics. Recently, quantum information theory has emerged as a potential source for such an interpretation. The main

film characterization using terahertz differential

The neural mechanisms of somatosensory information processing in the rodent vibrissae system are a topic of intense debate and research. Certain hypotheses emphasize the importance of stick-slip whisker motion, high-frequency resonant vibrations, and/or the ability to decode complex textures. Other

detectors and analogues of the lobula plate tangential cells to detect optic flow patterns. Ways in which olfaction could modulate this behaviour will then be investigated. A particular hypothesis to be assessed is that the fly employs path integration based on visual odometry. A robotic model

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First and foremost, I would like to thank my advisor, Prof. Jennifer E. Michaels. I am most appreciative of your guidance over the last four years, from the basics of elastic waves to both short- and long-term career advice. I have grown a tremendous amount, both profes-sionally and personally, and am deeply grateful for your mentorship, patience, and support. I would also like to thank my Ph.D. committee for their valuable insight and sugges-

Deciphering the three-dimensional (3D) structure of complex molecules is of major importance, typically accomplished with X-ray crystallography. Unfortunately, many important molecules cannot be crystallized, hence their 3D structure is unknown. Ankylography presents an alternative, relying on scattering an ultrashort X-ray pulse off a single molecule before it disintegrates, measuring the far-field intensity on a two-dimensional surface, followed by computation. However, significant information is absent due to lower dimensionality of the measurements and the inability to measure the phase. Recent Ankylography experiments attracted much interest, but it was counter-argued that Ankylography is valid only for objects containing a small number of volume pixels. Here, we propose a sparsity-based approach to reconstruct the 3D structure of molecules. Sparsity is natural for Ankylography, because molecules can be represented compactly in stoichiometric basis. Utilizing sparsity, we surpass current limits on recoverable information by orders of magnitude, paving the way for deciphering the 3D structure of macromolecules.

of both the player population and the player interactions. We evaluate several algorithms, including a novel neural network predictor, for predicting the resource demand. Using trace-based simulation, we evaluate the impact of the data center policies on the resource provisioning efficiency; we show