Above are papers related to the following paper: [Paper_ID] Paper #78508. 	[Title] Canonical Momenta Indicators of Financial Markets and Neocortical EEG [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. Please summarise all the given information.

Above are papers related to the following paper: [Paper_ID] Paper #1120138. 	[Title] Volatility of Volatility of Financial Markets [Abstract] We present empirical evidence for considering volatility of Eurodollar futures as a stochastic process, requiring a generalization of the standard Black-Scholes (BS) model which treats volatility as a constant. We use a previous development of a statistical mechanics of financial markets (SMFM) to model these issues. Please summarise all the given information.

Above are papers related to the following paper: [Paper_ID] Paper #9586. 	[Title] %A L. Ingber %T Adaptive simulated annealing (ASA): Lessons learned %J Control and Cybernetics Annealing [Abstract]  Please summarise all the given information.

Above are papers related to the following paper: [Paper_ID] Paper #33818. 	[Title] Statistical Mechanics of Nonlinear Nonequilibrium Financial Markets: Applications to Optimized Trading [Abstract] 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 multi-variate 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. Please summarise all the given information.

Above are papers related to the following paper: [Paper_ID] Paper #78555. 	[Title] NONLINEAR NONEQUILIBRIUM NONQUANTUM NONCHAOTIC STATISTICAL MECHANICS OF NEOCORTICAL INTERACTIONS [Abstract] The work in progress reported by Wright & Liley shows great promise, primarily because of their experimental and simulation paradigms. However, their tentative conclusion that macroscopic neocortex may be considered (approximately) a linear near-equilibrium system is premature and does not correspond to tentative conclusions drawn from other studies of neocortex. At this time, there exists an interdisciplinary multidimensional gradation on published studies of neocortex, with one primary dimension of mathematical physics represented by two extremes. At one extreme, there is much scientifically unsupported talk of chaos and quantum physics being responsible for many important macroscopic neocortical processes (involving many thousands to millions of neurons) (Wilczek, 1994). At another extreme, many non-mathematically trained neuroscientists uncritically lump all neocortical mathematical theory into one file, and consider only statistical averages of citations for opinions on the quality of that research (Nunez, 1995). In this context, it is important to appreciate that Wright and Liley (W&L) report on their scientifically sound studies on macroscopic neocortical function, based on simulation and a blend of sound theory and reproducible experiments. However, their pioneering work, given the absence of much knowledge of neocortex at this time, is open to criticism, especially with respect to their present inferences and conclusions. Their conclusion that EEG data exhibit linear near-equilibrium dynamics may very well be true, but only in the sense of focusing only on one local minima, possibly with individual-specific and physiological-state dependent Please summarise all the given information.

Above are papers related to the following paper: [Paper_ID] Paper #78557. 	[Title] Application of statistical mechanics methodol- ogy to term-structure bond-pricing models, Mathl. Comput. Modelling Application of [Abstract]  Please summarise all the given information.

Paper #78508 has the title `Canonical Momenta Indicators of Financial Markets and Neocortical EEG  
` and the 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. 
`.
Question: Which category should Paper #78508 be classified as ?
You can select one from ['Neural_Networks', 'Case_Based', 'Theory', 'Reinforcement_Learning', 'Probabilistic_Methods', 'Rule_Learning', 'Genetic_Algorithms'].


Paper #78508 has the title `Canonical Momenta Indicators of Financial Markets and Neocortical EEG  
` and the 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. 
`.
Question: Which category should Paper #78508 be classified as ?
You can select one from ['Neural_Networks', 'Case_Based', 'Theory', 'Reinforcement_Learning', 'Probabilistic_Methods', 'Rule_Learning', 'Genetic_Algorithms'].

This paper should be classified into: Neural_Networks