Fuzzy ART Choice Functions

Adaptive Resonance Theory (ART) models are real-time neural networks for category learning, pattern recognition, and prediction. Unsupervised fuzzy ART and supervised fuzzy ARTMAP networks synthesize fuzzy logic and ART by exploiting the formal similarity between tile computations of fuzzy subsethood and the dynamics of ART category choice, search, and learning. Fuzzy ART self-organizes stable recognition categories in response to arbitrary sequences of analog or binary input patterns. It generalizes the binary ART 1 model, replacing the set-theoretic intersection (∩) with the fuzzy intersection(∧), or component-wise minimum. A normalization procedure called complement coding leads to a symmetric theory in which the fuzzy intersection and the fuzzy union (∨), or component-wise maximum, play complementary roles. A geometric interpretation of fuzzy ART represents each category as a box that increases in size as weights decrease. This paper analyzes fuzzy ART models that employ various choice functions for category selection. One such function minimizes total weight change during learning. Benchmark simulations compare peformance of fuzzy ARTMAP systems that use different choice functions.Advanced Research Projects Agency (ONR N00014-92-J-4015); National Science Foundation (IRI-90-00530); Office of Naval Research (N00014-91-J-4100

Atomic nuclei decay modes by spontaneous emission of heavy ions

The great majority of the known nuclides with Z>40, including the so-called stable nuclides, are metastable with respect to several modes of spontaneous superasymmetric splitting. A model extended from the fission theory of alpha decay allows one to estimate the lifetimes and the branching ratios relative to the alpha decay for these natural radioactivities. From a huge amount of systematic calculations it is concluded that the process should proceed with maximum intensity in the trans-lead nuclei, where the minimum lifetime is obtained from parent-emitted heavy ion combinations leading to a magic (208Pb) or almost magic daughter nucleus. More than 140 nuclides with atomic number smaller than 25 are possible candidates to be emitted from heavy nuclei, with half-lives in the range of 1010–1030 s: 5He, 8–10Be, 11,12B, 12–16C, 13–17N, 15–22O, 18–23F, 20–26Ne, 23–28Na, 23–30Mg, 27–32Al, 28–36Si, 31–39P, 32–42S, 35–45Cl, 37–47Ar, 40–49 K, 42-51. . .Ca, 44–53 Sc, 46–53Ti, 48–54V, and 49–55 Cr. The shell structure and the pairing effects are clearly manifested in these new decay modes

Heavy cluster decay of trans-zirconium "stable" nuclides

By using the analytical superasymmetric fission model it is shown that all ‘‘stable’’ nuclei lighter than lead with Z>40 are metastable relative to the spontaneous emission of nuclear clusters. An even-odd effect is included in the zero point vibration energy. Half-lives in the range 1040–1050 s are obtained for Z>62. The region of metastability against these new decay modes is extended beyond that for α decay and in some cases, in the competing region, the emission rates for nuclear clusters are larger than for α decay

Plasmonics in graphene at infra-red frequencies

We point out that plasmons in doped graphene simultaneously enable low-losses and significant wave localization for frequencies below that of the optical phonon branch $\hbar\omega_{Oph}\approx 0.2$ eV. Large plasmon losses occur in the interband regime (via excitation of electron-hole pairs), which can be pushed towards higher frequencies for higher doping values. For sufficiently large dopings, there is a bandwidth of frequencies from $\omega_{Oph}$ up to the interband threshold, where a plasmon decay channel via emission of an optical phonon together with an electron-hole pair is nonegligible. The calculation of losses is performed within the framework of a random-phase approximation and number conserving relaxation-time approximation. The measured DC relaxation-time serves as an input parameter characterizing collisions with impurities, whereas the contribution from optical phonons is estimated from the influence of the electron-phonon coupling on the optical conductivity. Optical properties of plasmons in graphene are in many relevant aspects similar to optical properties of surface plasmons propagating on dielectric-metal interface, which have been drawing a lot of interest lately because of their importance for nanophotonics. Therefore, the fact that plasmons in graphene could have low losses for certain frequencies makes them potentially interesting for nanophotonic applications.Comment: 5 figure

On and Off-diagonal Sturmian operator: dynamic and spectral dimension

We study two versions of quasicrystal model, both subcases of Jacobi matrices. For Off-diagonal model, we show an upper bound of dynamical exponent and the norm of the transfer matrix. We apply this result to the Off-diagonal Fibonacci Hamiltonian and obtain a sub-ballistic bound for coupling large enough. In diagonal case, we improve previous lower bounds on the fractal box-counting dimension of the spectrum.Comment: arXiv admin note: text overlap with arXiv:math-ph/0502044 and arXiv:0807.3024 by other author

ART Neural Networks for Remote Sensing Image Analysis

ART and ARTMAP neural networks for adaptive recognition and prediction have been applied to a variety of problems, including automatic mapping from remote sensing satellite measurements, parts design retrieval at the Boeing Company, medical database prediction, and robot vision. This paper features a self-contained introduction to ART and ARTMAP dynamics. An application of these networks to image processing is illustrated by means of a remote sensing example. The basic ART and ARTMAP networks feature winner-take-all (WTA) competitive coding, which groups inputs into discrete recognition categories. WTA coding in these networks enables fast learning, which allows the network to encode important rare cases but which may lead to inefficient category proliferation with noisy training inputs. This problem is partially solved by ART-EMAP, which use WTA coding for learning but distributed category representations for test-set prediction. Recently developed ART models (dART and dARTMAP) retain stable coding, recognition, and prediction, but allow arbitrarily distributed category representation during learning as well as performance

Automatic learning of gait signatures for people identification

This work targets people identification in video based on the way they walk (i.e. gait). While classical methods typically derive gait signatures from sequences of binary silhouettes, in this work we explore the use of convolutional neural networks (CNN) for learning high-level descriptors from low-level motion features (i.e. optical flow components). We carry out a thorough experimental evaluation of the proposed CNN architecture on the challenging TUM-GAID dataset. The experimental results indicate that using spatio-temporal cuboids of optical flow as input data for CNN allows to obtain state-of-the-art results on the gait task with an image resolution eight times lower than the previously reported results (i.e. 80x60 pixels).Comment: Proof of concept paper. Technical report on the use of ConvNets (CNN) for gait recognition. Data and code: http://www.uco.es/~in1majim/research/cnngaitof.htm

Art Neural Networks for Remote Sensing: Vegetation Classification from Landsat TM and Terrain Data

A new methodology for automatic mapping from Landsat Thematic Mapper (TM) and terrain data, based on the fuzzy ARTMAP neural network, is developed. System capabilities are tested on a challenging remote sensing classification problem, using spectral and terrain features for vegetation classification in the Cleveland National Forest. After training at the pixel level, system performance is tested at the stand level, using sites not seen during training. Results are compared to those of maximum likelihood classifiers, as well as back propagation neural networks and K Nearest Neighbor algorithms. ARTMAP dynamics are fast, stable, and scalable, overcoming common limitations of back propagation, which did not give satisfactory performance. Best results are obtained using a hybrid system based on a convex combination of fuzzy ARTMAP and maximum likelihood predictions. A prototype remote sensing example introduces each aspect of data processing and fuzzy ARTMAP classification. The example shows how the network automatically constructs a minimal number of recognition categories to meet accuracy criteria. A voting strategy improves prediction and assigns confidence estimates by training the system several times on different orderings of an input set.National Science Foundation (IRI 94-01659, SBR 93-00633); Office of Naval Research (N00014-95-l-0409, N00014-95-0657