A paradigm of statistical mechanics of financial markets (SMFM) using nonlinear nonequilibrium algorithms, first published in L. Ingber, Mathematical Modelling, 5, 343-361 (1984), is fit to multivariate financial markets using Adaptive Simulated Annealing (ASA), a global optimization algorithm, to perform maximum likelihood fits of Lagrangians defined by path integrals of multivariate conditional probabilities. Canonical momenta are thereby derived and used as technical indicators in a recursive ASA optimization process to tune trading rules. These trading rules are then used on out-ofsample data, to demonstrate that they can profit from the SMFM model, to illustrate that these markets are likely not efficient.

High-frequency financial data are not only discretely sampled in time but the time separating successive observations is often random. We analyze the consequences of this dual feature of the data when estimating a continuous-time model. In particular, we measure the additional effects of the randomness of the sampling intervals over and beyond those due to the discreteness of the data. We also examine the effect of simply ignoring the sampling randomness. We find that in many situations the randomness of the sampling has a larger impact than the discreteness of the data.

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We examine the strong cycles in the number of initial public offerings (IPOs) and in the average initial returns realized by investors who participated in the IPOs. At the aggregate level, initial returns are predictably related to past initial returns and also to future IPO volume from 1960-1997. To understand these patterns, we use firm-level data from 1985-97 to model the initial return. Our results show that aggregate IPO cycles occur because of the time it takes to complete an IPO, the clustering of similar types of IPOs in time, and information spillovers among IPOs.

We show how the financial market valuation of firms can be used to estimate the intangible costs and benefits of computer capital and we present several new empirical results based on this model. Using eight years of data for 820 non-financial firms in the United States, we find that an increase of one dollar in the quantity of computer capital installed by a firm is associated with an increase of about ten dollars in the financial markets ’ valuation of the firm. Other forms of capital do not exhibit these high valuations. Our model suggests that adjustment costs and intangible assets may provide an explanation for the high market valuation found for computers in this study as well as the high returns found for computer capital in firm-level productivity studies. Costly investments in software, training and organizational transformations that accompany computer investments can be regarded as creating intangible assets. These intangible assets do not appear on firms ’ conventional balance sheets but they can produce both higher market valuations and apparent “excess ” returns. The empirical evidence suggests that the vast majority of the costs and benefits of computerization are embodied in otherwise unobserved intangible assets.

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In this paper we propose a methodology for utilizing Network Management Systems for the early detection of Distributed Denial of Service (DDoS) Attacks. Although there are quite a large number of events that are prior to an attack (e.g. suspicious logons, start of processes, addition of new files, sudden shifts in traffic, etc.), in this work we depend solely on information from MIB (Management Information Base) Traffic Variables collected from the systems participating in the Attack. Three types of DDoS attacks were effected on a Research Test Bed, and MIB variables were recorded. Using these datasets, we show how there are indeed MIB-based precursors of DDoS attacks This work was supported by the Air Force Research Laboratory (Rome, NY - USA) under contract F30602-00-C-0126 to Scientific Systems Company, and by Aprisma's University Fellowship Program 1999/2000. 1 that render it possible to detect them before the Target is shut down. Most importantly, we describe how the relevant MI...

This paper utilizes the Asset and Health Dynamics of the Oldest Old (AHEAD) Panel to test for the absence of causal links from socio-economic status (SES) to health innovations and mortality, and from health conditions to innovations in wealth. We conclude that there is no direct causal link from SES to mortality or to incidence of most sudden onset health conditions (accidents and some acute conditions), but there is an association of SES with incidence of gradual onset health conditions (mental conditions, and some degenerative and chronic conditions), due either to causal links or to persistent unobserved behavioral or genetic factors that have a common influence on both SES and innovations in health. We conclude that there is no direct causal link from health status to innovations in wealth.

IPTV is increasingly being deployed and offered as a commercial service to residential broadband customers. Compared with traditional ISP networks, an IPTV distribution network (i) typically adopts a hierarchical instead of mesh-like structure, (ii) imposes more stringent requirements on both reliability and performance, (iii) has different distribution protocols (which make heavy use of IP multicast) and traffic patterns, and (iv) faces more serious scalability challenges in managing millions of network elements. These unique characteristics impose tremendous challenges in the effective management of IPTV network and service. In this paper, we focus on characterizing and troubleshooting performance issues in one of the largest IPTV networks in North America. We collect a large amount of measurement data from a wide range of sources, including device usage and error logs, user activity logs, video quality alarms, and customer trouble tickets. We develop a novel diagnosis tool called Giza that is specifically tailored to the enormous scale and hierarchical structure of the IPTV network. Giza applies multi-resolution data analysis to quickly detect and localize regions in the IPTV distribution hierarchy that are experiencing serious performance problems. Giza then uses several statistical data mining techniques to troubleshoot the identified problems and diagnose their root causes. Validation against operational experiences demonstrates the effectiveness of Giza in detecting important performance issues and identifying interesting dependencies. The methodology and algorithms in Giza promise to be of great use in IPTV network operations.

Abstract—A paradigm of statistical mechanics of financial markets (SMFM) is fit to multivariate financial markets using Adaptive Simulated Annealing (ASA), a global optimization algorithm, to perform maximum likelihood fits of Lagrangians defined by path integrals of multivariate conditional probabilities. Canonical momenta are thereby derived and used as technical indicators in a recursive ASA optimization process to tune trading rules. These trading rules are then used on out-of-sample data, to demonstrate that they can profit from the SMFM model, to illustrate that these markets are likely not efficient. This methodology can be extended to other systems, e.g., electroencephalography. This approach to complex systems emphasizes the utility of blending an intuitive and powerful mathematical-physics formalism to generate indicators which are used by AI-type rule-based models of management. 1.