The National Airspace System (NAS) is a large and complex system with thousands of interrelated components: administration, control centers, airports, airlines, aircraft, passengers, etc. The complexity of the NAS creates many difficulties in management and control. One of the most pressing problems is flight delay. Delay creates high cost to airlines, complaints from passengers, and difficulties for airport operations. As demand on the system increases, the delay problem becomes more and more prominent. For this reason, it is essential for the Federal Aviation Administration to understand the causes of delay and to find ways to reduce delay. Major contributing factors to delay are congestion at the origin airport, weather, increasing demand, and air traffic management (ATM) decisions such as the Ground Delay Programs (GDP). Delay is an inherently stochastic phenomenon. Even if all known causal factors could be accounted for, macro-level national airspace system (NAS) delays could not be predicted with certainty from micro-level aircraft information. This paper presents a stochastic model that uses Bayesian Networks (BNs) to model the relationships among different components of aircraft delay and the causal factors that affect delays. A case study on delays of departure flights from Chicago O'Hare international airport (ORD) to Hartsfield-Jackson Atlanta International Airport (ATL) reveals how local and system level environmental and human-caused factors combine to affect components of delay, and how these components contribute to the final arrival delay at the destination airport.
In part, the critics of AI are driven by the knowledge that'white collar jobs' are the ones that are now under threat. Business leaders are frequently confronted by notions of job-killing automation and headlines on the variation of the theme that "Robots Will Steal Our Jobs." Elon Musk, CEO of Tesla, Silicon Valley figurehead, and champion of technology-driven innovation even goes a step further by suggesting AI is a fundamental threat to human civilisation. The robot on the assembly line is now a familiar image. AI in middle management is new.
This paper analyzes consumer choices over lunchtime restaurants using data from a sample of several thousand anonymous mobile phone users in the San Francisco Bay Area. The data is used to identify users' approximate typical morning location, as well as their choices of lunchtime restaurants. We build a model where restaurants have latent characteristics (whose distribution may depend on restaurant observables, such as star ratings, food category, and price range), each user has preferences for these latent characteristics, and these preferences are heterogeneous across users. Similarly, each item has latent characteristics that describe users' willingness to travel to the restaurant, and each user has individual-specific preferences for those latent characteristics. Thus, both users' willingness to travel and their base utility for each restaurant vary across user-restaurant pairs. We use a Bayesian approach to estimation. To make the estimation computationally feasible, we rely on variational inference to approximate the posterior distribution, as well as stochastic gradient descent as a computational approach. Our model performs better than more standard competing models such as multinomial logit and nested logit models, in part due to the personalization of the estimates. We analyze how consumers re-allocate their demand after a restaurant closes to nearby restaurants versus more distant restaurants with similar characteristics, and we compare our predictions to actual outcomes. Finally, we show how the model can be used to analyze counterfactual questions such as what type of restaurant would attract the most consumers in a given location.
Leslie Grate and Mark Herbster and Richard Hughey and David Haussler Baskin (;enter for Computer Engineering and Computer and Information Sciences University of California Santa Cruz, CA 95064 Keywords: RNA secondary structure, Gibbs sampler, Expectation Maximization, stochastic contextfree grammars, hidden Markov models, tP NA, snRNA, 16S rRNA, linguistic methods Abstract A new method of discovering the common secondary structure of a family of homologous RNA sequences using Gibbs sampling and stochastic context-free grammars is proposed. These parameters describe a statistical model of the family. After the Gibbs sampling has produced a crude statistical model for the family, this model is translated into a stochastic context-free grammar, which is then refined by an Expectation Maximization (EM) procedure produce a more complete model. A prototype implementation of the method is tested on tRNA, pieces of 16S rRNA and on U5 snRNA with good results. I. Saira Mian and Harry Noller Sinsheimer Laboratories University of California Santa Cruz, CA 95064 Introduction Tools for analyzing RNA are becoming increasingly important as in vitro evolution and selection techniques produce greater numbers of synthesized RNA families to supplement those related by phylogeny. Two principal methods have been established for predicting RNA secondary structure base pairings. The second technique employs thermodynamics to compare the free energy changes predicted for formation of possible s,'covdary structure and relies on finding the structure with the lowest free energy (Tinoco Jr., Uhlenbeck, & Levine 1971: Turner, Sugimoto, & Freier 1988; *This work was supported in part by NSF grants C,I)A-9115268 and IR1-9123692, and NIIt gratnt (.;M17129. When several related sequences are available that all share a common secondary structure, combinations of different approaches have been used to obtain improved results (Waterman 1989; Le & Zuker 1991; Han& Kim 1993; Chiu & Kolodziejczak 1991; Sankoff 1985; Winker et al. 1990; Lapedes 1992; Klinger & Brutlag 1993; Gutell et aL 1992). Recent efforts have applied Stochastic Context-Free Grammars (SCFGs) to the problems of statistical modeling, multiple alignment, discrimination and prediction of the secondary structure of RNA families (Sakakibara el al. 1994; 1993; Eddy & Durbin 1994; Searls 1993).
Santosh S. Venkatesh Electrical Engineering University of Pennsylvania Philadelphia, PA 19104 If patterns are drawn from an n-dimensional feature space according to a probability distribution that obeys a weak smoothness criterion, we show that the probability that a random input pattern is misclassified by a nearest-neighbor classifier using M random reference patterns asymptotically satisfies a PM(error) "" Poo(error) M2/n' for sufficiently large values of M. Here, Poo(error) denotes the probability of error in the infinite sample limit, and is at most twice the error of a Bayes classifier. Although the value of the coefficient a depends upon the underlying probability distributions, the exponent of M is largely distribution free.We thus obtain a concise relation between a classifier's ability to generalize from a finite reference sample and the dimensionality of the feature space, as well as an analytic validation of Bellman's well known "curse of dimensionality." 1 INTRODUCTION One of the primary tasks assigned to neural networks is pattern classification.