Search results for "NEURAL NETWORKS"
showing 10 items of 599 documents
Random walk approach to the analytic solution of random systems with multiplicative noise—The Anderson localization problem
2006
We discuss here in detail a new analytical random walk approach to calculating the phase-diagram for spatially extended systems with multiplicative noise. We use the Anderson localization problem as an example. The transition from delocalized to localized states is treated as a generalized diffusion with a noise-induced first-order phase transition. The generalized diffusion manifests itself in the divergence of averages of wavefunctions (correlators). This divergence is controlled by the Lyapunov exponent $\gamma$, which is the inverse of the localization length, $\xi=1/\gamma$. The appearance of the generalized diffusion arises due to the instability of a fundamental mode corresponding to…
Anderson localization problem: An exact solution for 2-D anisotropic systems
2007
Our previous results [J.Phys.: Condens. Matter 14 (2002) 13777] dealing with the analytical solution of the two-dimensional (2-D) Anderson localization problem due to disorder is generalized for anisotropic systems (two different hopping matrix elements in transverse directions). We discuss the mathematical nature of the metal-insulator phase transition which occurs in the 2-D case, in contrast to the 1-D case, where such a phase transition does not occur. In anisotropic systems two localization lengths arise instead of one length only.
Kardar–Parisi–Zhang scaling in kinetic roughening of fire fronts
1999
Abstract We show that the roughening of fire fronts in slow combustion of paper [7] follows the scaling predictions of the Kardar–Parisi–Zhang equation with thermal noise. By improved experimental accuracy it is now possible to observe the short-time and short-range correlations of the interfaces. These do not adhere to any standard picture, and in particular, do not seem to be related to any of the existing models of front propagation in the presence of quenched disorder.
Value-at-Risk and Tsallis statistics: risk analysis of the aerospace sector
2004
In this study, we analyze the aerospace stocks prices in order to characterize the sector behavior. The data analyzed cover the period from January 1987 to April 1999. We present a new index for the aerospace sector and we investigate the statistical characteristics of this index. Our results show that this index is well described by Tsallis distribution. We explore this result and modify the standard Value-at-Risk (VaR), financial risk assessment methodology in order to reflect an asset which obeys Tsallis non-extensive statistics.
Basic networks: Definition and applications
2009
7 pages, 4 figures, 1 table.-- PMID: 19490867 [PubMed]
Interactive Effects of Explicit Emergent Structure: A Major Challenge for Cognitive Computational Modeling
2015
International audience; David Marr's (1982) three-level analysis of computational cognition argues for three distinct levels of cognitive information processingnamely, the computational, representational, and implementational levels. But Marr's levels areand were meant to bedescriptive, rather than interactive and dynamic. For this reason, we suggest that, had Marr been writing today, he might well have gone even farther in his analysis, including the emergence of structurein particular, explicit structure at the conceptual levelfrom lower levels, and the effect of explicit emergent structures on the level (or levels) that gave rise to them. The message is that today's cognitive scientists …
The Insect Mushroom Bodies: a Paradigm of Neural Reuse
2013
This paper is devoted to discuss the implementation of models,which are inspired by the fly Drosophila melanogaster and able to handle open problems in the field of robotics such as attention, expectation and sequence learning. The role of the Mushroom Bodies (MBs) in solving these tasks is analyzed in detail and a unifying plausible biologically inspired model is proposed. The developed neural structure is able to show different capabilities in line with the paradigm of neural reuse. The same neural circuit can be exploited to accomplish multiple tasks showing interesting capabilities such as attention, expectation and delayed match-to-sample. The simulation results here reported suggest a…
Tunable multifunctional topological insulators in ternary Heusler compounds
2010
Recently the Quantum Spin Hall effect (QSH) was theoretically predicted and experimentally realized in a quantum wells based on binary semiconductor HgTe[1-3]. QSH state and topological insulators are the new states of quantum matter interesting both for fundamental condensed matter physics and material science[1-11]. Many of Heusler compounds with C1b structure are ternary semiconductors which are structurally and electronically related to the binary semiconductors. The diversity of Heusler materials opens wide possibilities for tuning the band gap and setting the desired band inversion by choosing compounds with appropriate hybridization strength (by lattice parameter) and the magnitude o…
Electromagnetic behaviour of superconductive amorphous metals
2005
The penetration depth of the magnetic field into an amorphous superconductor is calculated. The ratio of the London penetration depth δL to the electron free path le under zero temperature is above unity for almost all amorphous metals. That is why pure metals, in a superconducting state, change from type I superconductors to type II superconductors during the crystalline–amorphous transition.
Effectively Predicting the Presence of Coronary Heart Disease Using Machine Learning Classifiers
2022
Coronary heart disease is one of the major causes of deaths around the globe. Predicating a heart disease is one of the most challenging tasks in the field of clinical data analysis. Machine learning (ML) is useful in diagnostic assistance in terms of decision making and prediction on the basis of the data produced by healthcare sector globally. We have also perceived ML techniques employed in the medical field of disease prediction. In this regard, numerous research studies have been shown on heart disease prediction using an ML classifier. In this paper, we used eleven ML classifiers to identify key features, which improved the predictability of heart disease. To introduce the prediction …