554,760 research outputs found
Statistical Analysis of Dynamic Actions
Real-world action recognition applications require the development of systems which are fast, can handle a large variety of actions without a priori knowledge of the type of actions, need a minimal number of parameters, and necessitate as short as possible learning stage. In this paper, we suggest such an approach. We regard dynamic activities as long-term temporal objects, which are characterized by spatio-temporal features at multiple temporal scales. Based on this, we design a simple statistical distance measure between video sequences which captures the similarities in their behavioral content. This measure is nonparametric and can thus handle a wide range of complex dynamic actions. Having a behavior-based distance measure between sequences, we use it for a variety of tasks, including: video indexing, temporal segmentation, and action-based video clustering. These tasks are performed without prior knowledge of the types of actions, their models, or their temporal extents
Learning from Semantic Inconsistencies as the Origin of Dynamic Capabilities in MNCs: Evidence from Pharmaceutical MNCs
This paper focuses on origins of dynamic capabilities in multinational corporations (MNCs). Building on literature in the area of organizational memory and organizational learning, we investigate factors that contribute to subsidiaries of MNCs ability to detach themselves from obsolete knowledge and practices. To construct the theoretical framework, 11 extensive interviews with marketing and sales executives from three pharmaceutical MNCs operated in Iran were conducted. We test our hypotheses using statistical quantitative analysis of data related to 459 observations from subsidiaries of 51 pharmaceutical MNCs during years 2005-2009. We examine the quality of corrective actions taken by subsidiaries of pharmaceutical MNCs subsequent to subsidiaries failing to meet expected performance objectives. Our findings confirm a moderating role for internationalization, span, and the composition of human resources on the quality of corrective actions pursued
Doctor of Philosophy
dissertationThe statistical study of anatomy is one of the primary focuses of medical image analysis. It is well-established that the appropriate mathematical settings for such analyses are Riemannian manifolds and Lie group actions. Statistically defined atlases, in which a mean anatomical image is computed from a collection of static three-dimensional (3D) scans, have become commonplace. Within the past few decades, these efforts, which constitute the field of computational anatomy, have seen great success in enabling quantitative analysis. However, most of the analysis within computational anatomy has focused on collections of static images in population studies. The recent emergence of large-scale longitudinal imaging studies and four-dimensional (4D) imaging technology presents new opportunities for studying dynamic anatomical processes such as motion, growth, and degeneration. In order to make use of this new data, it is imperative that computational anatomy be extended with methods for the statistical analysis of longitudinal and dynamic medical imaging. In this dissertation, the deformable template framework is used for the development of 4D statistical shape analysis, with applications in motion analysis for individualized medicine and the study of growth and disease progression. A new method for estimating organ motion directly from raw imaging data is introduced and tested extensively. Polynomial regression, the staple of curve regression in Euclidean spaces, is extended to the setting of Riemannian manifolds. This polynomial regression framework enables rigorous statistical analysis of longitudinal imaging data. Finally, a new diffeomorphic model of irrotational shape change is presented. This new model presents striking practical advantages over standard diffeomorphic methods, while the study of this new space promises to illuminate aspects of the structure of the diffeomorphism group
Fast-Convergent Learning-aided Control in Energy Harvesting Networks
In this paper, we present a novel learning-aided energy management scheme
() for multihop energy harvesting networks. Different from prior
works on this problem, our algorithm explicitly incorporates information
learning into system control via a step called \emph{perturbed dual learning}.
does not require any statistical information of the system
dynamics for implementation, and efficiently resolves the challenging energy
outage problem. We show that achieves the near-optimal
utility-delay tradeoff with an
energy buffers (). More interestingly,
possesses a \emph{convergence time} of , which is much faster than the time of
pure queue-based techniques or the time of approaches
that rely purely on learning the system statistics. This fast convergence
property makes more adaptive and efficient in resource
allocation in dynamic environments. The design and analysis of
demonstrate how system control algorithms can be augmented by learning and what
the benefits are. The methodology and algorithm can also be applied to similar
problems, e.g., processing networks, where nodes require nonzero amount of
contents to support their actions
The ART of IAM: The Winning Strategy for the 2006 Competition
In many dynamic open systems, agents have to interact with one another to achieve their goals. Here, agents may be self-interested, and when trusted to perform an action for others, may betray that trust by not performing the actions as required. In addition, due to the size of such systems, agents will often interact with other agents with which they have little or no past experience. This situation has led to the development of a number of trust and reputation models, which aim to facilitate an agent's decision making in the face of uncertainty regarding the behaviour of its peers. However, these multifarious models employ a variety of different representations of trust between agents, and measure performance in many different ways. This has made it hard to adequately evaluate the relative properties of different models, raising the need for a common platform on which to compare competing mechanisms. To this end, the ART Testbed Competition has been proposed, in which agents using different trust models compete against each other to provide services in an open marketplace. In this paper, we present the winning strategy for this competition in 2006, provide an analysis of the factors that led to this success, and discuss lessons learnt from the competition about issues of trust in multiagent systems in general. Our strategy, IAM, is Intelligent (using statistical models for opponent modelling), Abstemious (spending its money parsimoniously based on its trust model) and Moral (providing fair and honest feedback to those that request it)
Efficient Estimation and Control of Unknown Stochastic Differential Equations
Ito stochastic differential equations are ubiquitous models for dynamic
environments. A canonical problem in this setting is that of decision-making
policies for systems that evolve according to unknown diffusion processes. The
goals consist of design and analysis of efficient policies for both minimizing
quadratic cost functions of states and actions, as well as accurate estimation
of underlying linear dynamics. Despite recent advances in statistical decision
theory, little is known about estimation and control of diffusion processes,
which is the subject of this work. A fundamental challenge is that the policy
needs to continuously address the exploration-exploitation dilemma; estimation
accuracy is necessary for optimal decision-making, while sub-optimal actions
are required for obtaining accurate estimates.
We present an easy-to-implement reinforcement learning algorithm and
establish theoretical performance guarantees showing that it efficiently
addresses the above dilemma. In fact, the proposed algorithm learns the true
diffusion process and optimal actions fast, such that the per-unit-time
increase in cost decays with the square-root rate as time grows. Further, we
present tight results for assuring system stability and for specifying
fundamental limits of sub-optimalities caused by uncertainties. To obtain the
results, multiple novel methods are developed for analysis of matrix
perturbations, for studying comparative ratios of stochastic integrals and
spectral properties of random matrices, and the new framework of policy
differentiation is proposed
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