A comparative analysis of models for predicting delays in air traffic networks

In this paper, we compare the performance of different approaches to predicting delays in air traffic networks. We consider three classes of models: A recently-developed aggregate model of the delay network dynamics, which we will refer to as the Markov Jump Linear System (MJLS), classical machine learning techniques like Classification and Regression Trees (CART), and three candidate Artificial Neural Network (ANN) architectures. We show that prediction performance can vary significantly depending on the choice of model/algorithm, and the type of prediction (for example, classification vs. regression). We also discuss the importance of selecting the right predictor variables, or features, in order to improve the performance of these algorithms. The models are evaluated using operational data from the National Airspace System (NAS) of the United States. The ANN is shown to be a good algorithm for the classification problem, where it attains an average accuracy of nearly 94% in predicting whether or not delays on the 100 most-delayed links will exceed 60 min, looking two hours into the future. The MJLS model, however, is better at predicting the actual delay levels on different links, and has a mean prediction error of 4.7 min for the regression problem, for a 2 hr horizon. MJLS is also better at predicting outbound delays at the 30 major airports, with a mean error of 6.8 min, for a 2 hr prediction horizon. The effect of temporal factors, and the spatial distribution of current delays, in predicting future delays are also compared. The MJLS model, which is specifically designed to capture aggregate air traffic dynamics, leverages on these factors and outperforms the ANN in predicting the future spatial distribution of delays. In this manner, a tradeoff between model simplicity and prediction accuracy is revealed. Keywords- delay prediction; network delays; machine learning; artificial neural networks; data miningNational Science Foundation (U.S.) ( Award 1239054

Intelligent Diagnosis Systems

This paper examines and compares several different approaches to design of intelligent systems for diagnosis applications. These include expert systems (or knowledge based systems), truth (or reason) maintenance systems, case based reasoning systems, and inductive approaches like decision trees, neural networks (or connectionist systems), and statistical pattern classification systems. Each of these approaches is demonstrated through the design of a system for a simple automobile fault diagnosis task. The paper also discusses the domain characteristics that influence the choice of a specific technique (or combination of techniques) for a given application

The Relation Between Reporting Quality and Financing and Investment: Evidence from Changes in Financing Capacity

We use changes in the value of a firm's real estate assets as an exogenous change in a firm's financing capacity to examine (1) the relation between reporting quality and financing and investment conditional on this change, and (2) firms’ reporting quality responses to the change in financing capacity. We find that financing and investment by firms with higher reporting quality is less affected by changes in real estate values than are financing and investment by firms with lower reporting quality. Further, firms increase reporting quality in response to decreases in financing capacity. Our findings contribute to the literature on reporting quality and investment, and on the determinants of reporting quality choices.Sloan School of ManagementWharton Schoo

Shaping Liquidity: On the Causal Effects of Voluntary Disclosure

Can managers influence the liquidity of their shares? We use plausibly exogenous variation in the supply of public information to show that firms seek to actively shape their information environments by voluntarily disclosing more information than is mandated by market regulations and that such efforts have a sizeable and beneficial effect on liquidity. Firms respond to an exogenous loss of public information by providing more timely and informative earnings guidance. Responses are greatest when firms lose local information producers and appear motivated by a desire to communicate with retail investors. Liquidity improves as a result of voluntary disclosure

Clusters and communities in air traffic delay networks

The air transportation system is a network of many interacting, capacity-constrained elements. When the demand for airport and airspace resources exceed the available capacities of these resources, delays occur. The state of the air transportation system at any time can be represented as a weighted directed graph in which the nodes correspond to airports, and the weight on each arc is the delay experienced by departures on that origin-destination pair. Over the course of any day, the state of the system progresses through a time-series, where the state at any time-step is the weighted directed graph described above. This paper presents algorithms for the clustering of air traffic delay network data from the US National Airspace System, in order to identify characteristic delay states (i.e., weighted directed graphs) as well as characteristic types-of-days (i.e., sequences of such weighted directed graphs) that are experienced by the air transportation system. The similarity of delay states during clustering are evaluated on the basis of not only the in- and out-degrees of the nodes (the total inbound and outbound delays), but also network-theoretic properties such as the eigenvector centralities, and the hub and authority scores of different nodes. Finally, the paper looks at community detection, that is, the grouping of nodes (airports) based on their similarities within a system delay state. The type of day is found to have an impact on the observed community structures.United States. National Aeronautics and Space Administration (FA8721-05-C-0002)National Science Foundation (U.S.) (1239054

Shaping Liquidity: On the Casual Effects of Voluntary Disclosure

Can managers influence the liquidity of their shares? We use plausibly exogenous variation in the supply of public information to show that firms seek to actively shape their information environments by voluntarily disclosing more information than is mandated by market regulations and that such efforts have a sizeable and beneficial effect on liquidity. Firms respond to an exogenous loss of public information by providing more timely and informative earnings guidance. Responses are greatest when firms lose local information producers and appear motivated by a desire to communicate with retail investors. Liquidity improves as a result of voluntary disclosure

Biologically inspired computational structures and processes for autonomous agents and robots

Recent years have seen a proliferation of intelligent agent applications: from robots for space exploration to software agents for information filtering and electronic commerce on the Internet. Although the scope of these agent applications have blossomed tremendously since the advent of compact, affordable computing (and the recent emergence of the World Wide Web), the design of such agents for specific applications remains a daunting engineering problem;Rather than approach the design of artificial agents from a purely engineering standpoint, this dissertation views animals as biological agents, and considers artificial analogs of biological structures and processes in the design of effective agent behaviors. In particular, it explores behaviors generated by artificial neural structures appropriately shaped by the processes of evolution and spatial learning;The first part of this dissertation deals with the evolution of artificial neural controllers for a box-pushing robot task. We show that evolution discovers high fitness structures using little domain-specific knowledge, even in feedback-impoverished environments. Through a careful analysis of the evolved designs we also show how evolution exploits the environmental constraints and properties to produce designs of superior adaptive value. By modifying the task constraints in controlled ways, we also show the ability of evolution to quickly adapt to these changes and exploit them to obtain significant performance gains. We also use evolution to design the sensory systems of the box-pushing robots, particularly the number, placement, and ranges of their sensors. We find that evolution automatically discards unnecessary sensors retaining only the ones that appear to significantly affect the performance of the robot. This optimization of design across multiple dimensions (performance, number of sensors, size of neural controller, etc.) is implicitly achieved by the evolutionary algorithm without any external pressure (e.g., penalty on the use of more sensors or neurocontroller units). When used in the design of robots with limited battery capacities , evolution produces energy-efficient robot designs that use minimal numbers of components and yet perform reasonably well. The performance as well as the complexity of robot designs increase when the robots have access to a spatial learning mechanism that allows them to learn, remember, and navigate to power sources in the environment;The second part of this dissertation develops a computational characterization of the hippocampal formation which is known to play a significant role in animal spatial learning. The model is based on neuroscientific and behavioral data, and learns place maps based on interactions of sensory and dead-reckoning information streams. Using an estimation mechanism known as Kalman filtering, the model explicitly deals with uncertainties in the two information streams, allowing the robot to effectively learn and localize even in the presence sensing and motion errors. Additionally, the model has mechanisms to handle perceptual aliasing problems (where multiple places in the environment appear sensorily identical), incrementally learn and integrate local place maps, and learn and remember multiple goal locations in the environment. We show a number of properties of this spatial learning model including computational replication of several behavioral experiments performed with rodents. Not only does this model make significant contributions to robot localization, but also offers a number of predictions and suggestions that can be validated (or refuted) through systematic neurobiological and behavioral experiments with animals

Spatial variation decomposition via sparse regression

In this paper, we briefly discuss the recent development of a novel sparse regression technique that aims to accurately decompose process variation into two different components: (1) spatially correlated variation, and (2) uncorrelated random variation. Such variation decomposition is important to identify systematic variation patterns at wafer and/or chip level for process modeling, control and diagnosis. We demonstrate that the spatially correlated variation can be accurately represented by the linear combination of a small number of “templates”. Based upon this observation, an efficient algorithm is developed to accurately separate spatially correlated variation from uncorrelated random variation. Several examples based on silicon measurement data demonstrate that the aforementioned sparse regression technique can capture systematic variation patterns with high accuracy.Interconnect Focus Center (United States. Defense Advanced Research Projects Agency and Semiconductor Research Corporation)Focus Center Research Program. Focus Center for Circuit & System SolutionsNational Science Foundation (U.S.) (Contract CCF-0915912

Spatial Learning and Localization in Animals: A Computational Model and Its Implications for Mobile Robots

The ability to acquire a representation of spatial environment and the ability to localize within it are essential for successful navigation in a-priori unknown environments. The hippocampal formation is believed to play a key role in spatial learning and navigation in animals. This paper briefly reviews the relevant neurobiological and cognitive data and their relation to computational models of spatial learning and localization used in mobile robots. It also describes a hippocampal model of spatial learning and navigation and analyzes it using Kalman filter based tools for information fusion from multiple uncertain sources. The resulting model allows a robot to learn a place-based, metric representation of space in a-priori unknown environments and to localize itself in a stochastically optimal manner. The paper also describes an algorithmic implementation of the model and results of several experiments that demonstrate its capabilities

Intelligent Diagnosis Systems

This paper examines and compares several different approaches to the design of intelligent systems for diagnosis applications. These include expert systems (or knowledge-based systems), truth (or reason) maintenance systems, case-based reasoning systems, and inductive approaches like decision trees, artificial neural networks (or connectionist systems), and statistical pattern classification systems. Each of these approaches is demonstrated through the design of a system for a simple automobile fault diagnosis task. The paper also discusses the domain characteristics and design and performance requirements that influence the choice of a specific technique (or a combination of techniques) for a given application