Towards Addressing Key Visual Processing Challenges in Social Media Computing

abstract: Visual processing in social media platforms is a key step in gathering and understanding information in the era of Internet and big data. Online data is rich in content, but its processing faces many challenges including: varying scales for objects of interest, unreliable and/or missing labels, the inadequacy of single modal data and difficulty in analyzing high dimensional data. Towards facilitating the processing and understanding of online data, this dissertation primarily focuses on three challenges that I feel are of great practical importance: handling scale differences in computer vision tasks, such as facial component detection and face retrieval, developing efficient classifiers using partially labeled data and noisy data, and employing multi-modal models and feature selection to improve multi-view data analysis. For the first challenge, I propose a scale-insensitive algorithm to expedite and accurately detect facial landmarks. For the second challenge, I propose two algorithms that can be used to learn from partially labeled data and noisy data respectively. For the third challenge, I propose a new framework that incorporates feature selection modules into LDA models.Dissertation/ThesisDoctoral Dissertation Computer Science 201

Structural optimization in steel structures, algorithms and applications

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STOCHASTIC MOBILITY MODELS IN SPACE AND TIME

An interesting fact in nature is that if we observe agents (neurons, particles, animals, humans) behaving, or more precisely moving, inside their environment, we can recognize - tough at different space or time scales - very specific patterns. The existence of those patterns is quite obvious, since not all things in nature behave totally at random, especially if we take into account thinking species like human beings. If a first phenomenon which has been deeply modeled is the gas particle motion as the template of a totally random motion, other phenomena, like foraging patterns of animals such as albatrosses, and specific instances of human mobility wear some randomness away in favor of deterministic components. Thus, while the particle motion may be satisfactorily described with a Wiener Process (also called Brownian motion), the others are better described by other kinds of stochastic processes called Levy Flights. Minding at these phenomena in a unifying way, in terms of motion of agents \u2013 either inanimate like the gas particles, or animated like the albatrosses \u2013 the point is that the latter are driven by specific interests, possibly converging into a common task, to be accomplished. The whole thesis work turns around the concept of agent intentionality at different scales, whose model may be used as key ingredient in the statistical description of complex behaviors. The two main contributions in this direction are: 1. the development of a \u201cwait and chase\u201d model of human mobility having the same two-phase pattern as animal foraging but with a greater propensity of local stays in place and therefore a less dispersed general behavior; 2. the introduction of a mobility paradigm for the neurons of a multilayer neural network and a methodology to train these new kind of networks to develop a collective behavior. The lead idea is that neurons move toward the most informative mates to better learn how to fulfill their part in the overall functionality of the network. With these specific implementations we have pursued the general goal of attributing both a cognitive and a physical meaning to the intentionality so as to be able in a near future to speak of intentionality as an additional potential in the dynamics of the masses (both at the micro and a the macro-scale), and of communication as another network in the force field. This could be intended as a step ahead in the track opened by the past century physicists with the coupling of thermodynamic and Shannon entropies in the direction of unifying cognitive and physical laws