Comment: Monitoring Networked Applications With Incremental Quantile Estimation

Our comments are in two parts. First, we make some observations regarding the methodology in Chambers et al. [arXiv:0708.0302]. Second, we briefly describe another interesting network monitoring problem that arises in the context of assessing quality of service, such as loss rates and delay distributions, in packet-switched networks.Comment: Published at http://dx.doi.org/10.1214/088342306000000600 in the Statistical Science (http://www.imstat.org/sts/) by the Institute of Mathematical Statistics (http://www.imstat.org

An Overview on Application of Machine Learning Techniques in Optical Networks

Today's telecommunication networks have become sources of enormous amounts of widely heterogeneous data. This information can be retrieved from network traffic traces, network alarms, signal quality indicators, users' behavioral data, etc. Advanced mathematical tools are required to extract meaningful information from these data and take decisions pertaining to the proper functioning of the networks from the network-generated data. Among these mathematical tools, Machine Learning (ML) is regarded as one of the most promising methodological approaches to perform network-data analysis and enable automated network self-configuration and fault management. The adoption of ML techniques in the field of optical communication networks is motivated by the unprecedented growth of network complexity faced by optical networks in the last few years. Such complexity increase is due to the introduction of a huge number of adjustable and interdependent system parameters (e.g., routing configurations, modulation format, symbol rate, coding schemes, etc.) that are enabled by the usage of coherent transmission/reception technologies, advanced digital signal processing and compensation of nonlinear effects in optical fiber propagation. In this paper we provide an overview of the application of ML to optical communications and networking. We classify and survey relevant literature dealing with the topic, and we also provide an introductory tutorial on ML for researchers and practitioners interested in this field. Although a good number of research papers have recently appeared, the application of ML to optical networks is still in its infancy: to stimulate further work in this area, we conclude the paper proposing new possible research directions

Air Quality, Infant Mortality, and the Clean Air Act of 1970

We examine the effects of total suspended particulates (TSPs) air pollution on infant health using the air quality improvements induced by the 1970 Clean Air Act Amendments (CAAA). This legislation imposed strict regulations on industrial polluters in nonattainment' counties with TSPs concentrations exceeding the federal ceiling. We use nonattainment status as an instrumental variable for TSPs changes to estimate their impact on infant mortality changes in the first year that the 1970 CAAA was in force. TSPs nonattainment status is associated with sharp reductions in both TSPs pollution and infant mortality from 1971 to 1972. The greater reductions in nonattainment counties near the federal ceiling relative to the attainment' counties narrowly below the ceiling suggest that the regulations are the cause. We estimate that a one percent decline in TSPs results in a 0.5 percent decline in the infant mortality rate. Most of these effects are driven by a reduction in deaths occurring within one month of birth, suggesting that fetal exposure is a potential biological pathway. The results imply that roughly 1,300 fewer infants died in 1972 than would have in the absence of the Clean Air Act.

The Impact of Air Pollution on Infant Mortality: Evidence from Geographic Variation in Pollution Shocks Induced by a Recession

This study uses sharp, differential air quality changes across sites attributable to geographic variation in the effects of the 1981-82 recession to estimate the relationship between infant mortality and particulates air pollution. It is shown that in the narrow period of 1980-82, there was substantial variation across counties in changes in particulates pollution, and that these differential pollution reductions appear to be orthogonal to changes in a multitude of other factors that may be related to infant mortality. Using the most detailed and comprehensive data available, we find that a 1 mg/m3 reduction in particulates results in about 4-8 fewer infant deaths per 100,000 (a 0.35-0.45 elasticity). The estimated effects are driven almost entirely by fewer deaths occurring within one month and one day of birth, suggesting that fetal exposure to pollution has adverse health consequences. The estimated effects of the pollution reductions on infant birth weight provide evidence consistent with this potential pathophysiologic mechanism. The analysis also reveals a nonlinear relationship between pollution and infant mortality at the county level. Importantly, the estimates are remarkably stable across a variety of specifications. All of these findings are masked in conventional' analyses based on less credible research designs.

A Unifying Theory of Biological Function

A new theory that naturalizes biological function is explained and compared with earlier etiological and causal role theories. Etiological theories explain functions from how they are caused over their evolutionary history. Causal role theories analyze how functional mechanisms serve the current capacities of their containing system. The new proposal unifies the key notions of both kinds of theories, but goes beyond them by explaining how functions in an organism can exist as factors with autonomous causal efficacy. The goal-directedness and normativity of functions exist in this strict sense as well. The theory depends on an internal physiological or neural process that mimics an organism’s fitness, and modulates the organism’s variability accordingly. The structure of the internal process can be subdivided into subprocesses that monitor specific functions in an organism. The theory matches well with each intuition on a previously published list of intuited ideas about biological functions, including intuitions that have posed difficulties for other theories

Mapping the American Shareholder Litigation Experience: A Survey of Empirical Studies of the Enforcement of the U.S. Securities Law

In this paper, we provide an overview of the most significant empirical research that has been conducted in recent years on the public and private enforcement of the federal securities laws. The existing studies of the U.S. enforcement system provide a rich tapestry for assessing the value of enforcement, both private and public, as well as market penalties for fraudulent financial reporting practices. The relevance of the U.S. experience is made broader by the introduction through the PSLRA in late 1995 of new procedures for the conduct of private suits and the numerous efforts to evaluate the effects of those provisions. We believe that the evidence reviewed here shows that the PSLRA\u27s provisions have largely achieved their intended purposes. For example, many more private suits are headed by an institutional lead plaintiff, such plaintiffs appear to fulfill the desired role of monitoring the suit\u27s prosecution and their presence is associated with suits yielding better settlements and lower attorneys\u27 fees awards. SEC enforcement efforts, while significant, have tended to focus on weaker targets, suggesting that the big fish get away. Equally importantly, markets impose their own discipline on companies whose managers release false financial reports and, in turn, firms discipline the managers who are responsible for false misleading reporting, perhaps because of the presence of, or potential for, private enforcement actions