Identifying framework titanium in TS-1 zeolite by UV resonance Raman spectroscopy

Framework titanium in Ti-silicalite-1 (TS-1) zeolite was selectively identified by its resonance Raman bands using ultraviolet (W) Raman spectroscopy. Raman spectra of the TS-1 and silicalite-1 zeolites were obtained and compared using continuous wave laser lines at 244, 325, and 488 nm as the excitation sources. It was only with the excitation at 244 nm that resonance enhanced Raman bands at 490, 530, and 1125 cm(-1) appeared exclusively for the TS-1 zeolite. Furthermore, these bands increased in intensity with the crystallization time of the TS-1 zeolite. The Raman bands at 490, 530, and 1125 cm(-1) are identified as the framework titanium species because they only appeared when the laser excites the charge-transfer transition of the framework titanium species in the TS-1. No resonance Raman enhancement was detected for the bands of silicalite-1 zeolite and for the band at 960 cm(-1) of TS-1 with any of the excitation sources ranging from the visible tb UV regions. This approach can be applicable for the identification of other transition metal ions substituted in the framework of a zeolite or any other molecular sieve.

Hybrid simulation of ion cyclotron resonance in the solar wind: evolution of velocity distribution functions

Li, Xing; Habbal, S.R., (2005) 'Hybrid simulation of ion cyclotron resonance in the solar wind: evolution of velocity distribution functions', Journal of Geophysical Research 110(A10) pp.A10109 RAE2008

Adaptive Resonance Theory

SyNAPSE program of the Defense Advanced Projects Research Agency (Hewlett-Packard Company, subcontract under DARPA prime contract HR0011-09-3-0001, and HRL Laboratories LLC, subcontract #801881-BS under DARPA prime contract HR0011-09-C-0001); CELEST, an NSF Science of Learning Center (SBE-0354378)

Extracted Memory from Temporal Patterns Using Adaptive Resonance and Recurrent Network

We can recognize objects through receiving continuously huge temporal information including redundancy and noise, and can memorize them. This paper proposes a neural network model which extracts pre-recognized patterns from temporally sequential patterns which include redundancy, and memorizes the patterns temporarily. This model consists of an adaptive resonance system and a recurrent time-delay network. The extraction is executed by the matching mechanism of the adaptive resonance system, and the temporal information is processed and stored by the recurrent network. Simple simulations are examined to exemplify the property of extraction.

Fuzzy ART: An Adaptive Resonance Algorithm for Rapid, Stable Classification of Analog Patterns

The Fuzzy ART system introduced herein incorporates computations from fuzzy set theory into ART 1. For example, the intersection (n) operator used in ART 1 learning is replaced by the MIN operator (A) of fuzzy set theory. Fuzzy ART reduces to ART 1 in response to binary input vectors, but can also learn stable categories in response to analog input vectors. In particular, the MIN operator reduces to the intersection operator in the binary case. Learning is stable because all adaptive weights can only decrease in time. A preprocessing step, called complement coding, uses on-cell and off-cell responses to prevent category proliferation. Complement coding normalizes input vectors while preserving the amplitudes of individual feature activations.

ART 2-A: An Adaptive Resonance Algorithm for Rapid Category Learning and Recognition

This article introduces ART 2-A, an efficient algorithm that emulates the self-organizing pattern recognition and hypothesis testing properties of the ART 2 neural network architecture, but at a speed two to three orders of magnitude faster. Analysis and simulations show how the ART 2-A systems correspond to ART 2 dynamics at both the fast-learn limit and at intermediate learning rates. Intermediate learning rates permit fast commitment of category nodes but slow recoding, analogous to properties of word frequency effects, encoding specificity effects, and episodic memory. Better noise tolerance is hereby achieved without a loss of learning stability. The ART 2 and ART 2-A systems are contrasted with the leader algorithm. The speed of ART 2-A makes practical the use of ART 2 modules in large-scale neural computation.

Fuzzy ART: Fast Stable Learning and Categorization of Analog Patterns by an Adaptive Resonance System

A Fuzzy ART model capable of rapid stable learning of recognition categories in response to arbitrary sequences of analog or binary input patterns is described. Fuzzy ART incorporates computations from fuzzy set theory into the ART 1 neural network, which learns to categorize only binary input patterns. The generalization to learning both analog and binary input patterns is achieved by replacing appearances of the intersection operator (n) in AHT 1 by the MIN operator (Λ) of fuzzy set theory. The MIN operator reduces to the intersection operator in the binary case. Category proliferation is prevented by normalizing input vectors at a preprocessing stage. A normalization procedure called complement coding leads to a symmetric theory in which the MIN operator (Λ) and the MAX operator (v) of fuzzy set theory play complementary roles. Complement coding uses on-cells and off-cells to represent the input pattern, and preserves individual feature amplitudes while normalizing the total on-cell/off-cell vector. Learning is stable because all adaptive weights can only decrease in time. Decreasing weights correspond to increasing sizes of category "boxes". Smaller vigilance values lead to larger category boxes. Learning stops when the input space is covered by boxes. With fast learning and a finite input set of arbitrary size and composition, learning stabilizes after just one presentation of each input pattern. A fast-commit slow-recode option combines fast learning with a forgetting rule that buffers system memory against noise. Using this option, rare events can be rapidly learned, yet previously learned memories are not rapidly erased in response to statistically unreliable input fluctuations.

A Recurrent Cooperative/Competitive Field for Segmentation of Magnetic Resonance Brain Imagery

The Grey-White Decision Network is introduced as an application of an on-center, off-surround recurrent cooperative/competitive network for segmentation of magnetic resonance imaging (MRI) brain images. The three layer dynamical system relaxes into a solution where each pixel is labeled as either grey matter, white matter, or "other" matter by considering raw input intensity, edge information, and neighbor interactions. This network is presented as an example of applying a recurrent cooperative/competitive field (RCCF) to a problem with multiple conflicting constraints. Simulations of the network and its phase plane analysis are presented.

Adaptive Resonance Associative Map: A Hierarchical ART System for Fast Stable Associative Learning

This paper introduces a new class of predictive ART architectures, called Adaptive Resonance Associative Map (ARAM) which performs rapid, yet stable heteroassociative learning in real time environment. ARAM can be visualized as two ART modules sharing a single recognition code layer. The unit for recruiting a recognition code is a pattern pair. Code stabilization is ensured by restricting coding to states where resonances are reached in both modules. Simulation results have shown that ARAM is capable of self-stabilizing association of arbitrary pattern pairs of arbitrary complexity appearing in arbitrary sequence by fast learning in real time environment. Due to the symmetrical network structure, associative recall can be performed in both directions.

Optimized, unequal pulse spacing in multiple echo sequences improves refocusing in magnetic resonance.

A recent quantum computing paper (G. S. Uhrig, Phys. Rev. Lett. 98, 100504 (2007)) analytically derived optimal pulse spacings for a multiple spin echo sequence designed to remove decoherence in a two-level system coupled to a bath. The spacings in what has been called a "Uhrig dynamic decoupling (UDD) sequence" differ dramatically from the conventional, equal pulse spacing of a Carr-Purcell-Meiboom-Gill (CPMG) multiple spin echo sequence. The UDD sequence was derived for a model that is unrelated to magnetic resonance, but was recently shown theoretically to be more general. Here we show that the UDD sequence has theoretical advantages for magnetic resonance imaging of structured materials such as tissue, where diffusion in compartmentalized and microstructured environments leads to fluctuating fields on a range of different time scales. We also show experimentally, both in excised tissue and in a live mouse tumor model, that optimal UDD sequences produce different T(2)-weighted contrast than do CPMG sequences with the same number of pulses and total delay, with substantial enhancements in most regions. This permits improved characterization of low-frequency spectral density functions in a wide range of applications.

Surface plasmon resonance in nanostructured metal films under the Kretschmann configuration

We systematically investigated the surface plasmon resonance in one-dimensional (1D) subwavelength nanostructured metal films under the Kretschmann configuration. We calculated the reflectance, transmittance, and absorption for varying the dielectric fill factor, the period of the 1D nanostructure, and the metal film thickness. We have found that the small dielectric slits in the metal films reduce the surface plasmon resonance angle and move it toward the critical angle for total internal reflection. The reduction in surface plasmon resonance angle in nanostructured metal films is due to the increased intrinsic free electron oscillation frequency in metal nanostructures. Also we have found that the increasing the spatial frequency of the 1D nanograting reduces the surface plasmon resonance angle, which indicates that less momentum is needed to match the momentum of the surface plasmon-polariton. The variation in the nanostructured metal film thickness changes the resonance angle slightly, but mainly remains as a mean to adjust the coupling between the incident optical wave and the surface plasmon-polariton wave. © 2009 American Institute of Physics.

Spectral structure of the pygmy dipole resonance.

High-sensitivity studies of E1 and M1 transitions observed in the reaction 138Ba(gamma,gamma{'}) at energies below the one-neutron separation energy have been performed using the nearly monoenergetic and 100% linearly polarized photon beams of the HIgammaS facility. The electric dipole character of the so-called "pygmy" dipole resonance was experimentally verified for excitations from 4.0 to 8.6 MeV. The fine structure of the M1 "spin-flip" mode was observed for the first time in N=82 nuclei.

Magnetic resonance water proton relaxation in protein solutions and tissue: T(1rho) dispersion characterization.

BACKGROUND: Image contrast in clinical MRI is often determined by differences in tissue water proton relaxation behavior. However, many aspects of water proton relaxation in complex biological media, such as protein solutions and tissue are not well understood, perhaps due to the limited empirical data. PRINCIPAL FINDINGS: Water proton T(1), T(2), and T(1rho) of protein solutions and tissue were measured systematically under multiple conditions. Crosslinking or aggregation of protein decreased T(2) and T(1rho), but did not change high-field T(1). T(1rho) dispersion profiles were similar for crosslinked protein solutions, myocardial tissue, and cartilage, and exhibited power law behavior with T(1rho)(0) values that closely approximated T(2). The T(1rho) dispersion of mobile protein solutions was flat above 5 kHz, but showed a steep curve below 5 kHz that was sensitive to changes in pH. The T(1rho) dispersion of crosslinked BSA and cartilage in DMSO solvent closely resembled that of water solvent above 5 kHz but showed decreased dispersion below 5 kHz. CONCLUSIONS: Proton exchange is a minor pathway for tissue T(1) and T(1rho) relaxation above 5 kHz. Potential models for relaxation are discussed, however the same molecular mechanism appears to be responsible across 5 decades of frequencies from T(1rho) to T(1).