scholarly journals Phase Coherence in Multiple Pulse Optical Spectroscopy

1983 ◽  
Vol 2 (2) ◽  
pp. 37-51 ◽  
Author(s):  
Warren S. Warren ◽  
Ahmed H. Zewail
Keyword(s):  
Optical Spectroscopy ◽  
Chem Phys ◽  
Detection Methods ◽  
Optical Transitions ◽  
Multiple Pulse ◽  
Pulse Trains ◽  
Multilevel Systems ◽  
Relaxation Parameters ◽  
Vibrational Levels ◽  
A New Technique

In this paper we describe a new technique for the generation of multiple pulse phase coherent sequences in optical spectroscopy. The technique is an extension of the acousto-optic modulation and fluorescence detection methods developed for optical transitions by Zewail and Orlowski (Zewail et al., Chem. Phys. Lett.48, 256 (1977); Orlowski et al., Chem. Phys. Lett.54, 197 (1978)). Application of these multiple pulse trains (of different phases) to optical transitions of two-level and multilevel systems is demonstrated experimentally. It is shown that they can be used to (i) suppress spontaneous emission background, (ii) enhance coherent transients such as photon echoes, (iii) measure additional relaxation parameters in systems with complex rotational-vibrational levels, and (iv) enhance the effective laser bandwidths through composite pulse trains, as demonstrated on I2 gas. Finally, the potential of this development is extended to the possibility of observing selective multiquantum excitation in molecules.

Potential energy curve of N2 revisited

2011 ◽  
Vol 76 (4) ◽  
pp. 327-341 ◽  
Author(s):  
Vladimír Špirko ◽  
Xiangzhu Li ◽  
Josef Paldus
Keyword(s):  
Potential Energy ◽  
Chem Phys ◽  
Nitrogen Molecule ◽  
Basis Set ◽  
Basis Sets ◽  
Energy Curve ◽  
Vibrational Levels ◽  
Complete Basis Set ◽  
State Potential ◽  
Excellent Description

Recently generated ground state potential energy curves (PECs) for the nitrogen molecule, as obtained with the reduced multireference (RMR) coupled-cluster (CC) method with singles and doubles (RMR-CCSD), and its version corrected for the secondary triples RMR-CCSD(T), using cc-pVXZ basis sets with X = D, T, and Q, as well as the extrapolated complete basis set (cbs) limit (X. Li and J. Paldus: J. Chem. Phys. 2008, 129, 054104), are compared with both the highly accurate theoretical configuration interaction PEC of Gdanitz (Chem. Phys. Lett. 1998, 283, 253) and analytic PECs obtained by fitting an extensive set of experimental data (R. J. Le Roy et al.: J. Chem. Phys. 2006, 125, 164310). These results are analyzed using a morphing procedure based on the reduced potential curve (RPC) method of Jenč. It is found that an RPC fit of both theoretical potentials can be achieved with only a few parameters. The RMR PECs are found to provide an excellent description of experimentally available vibrational levels, but significantly deviate from those of Gdanitz’s PEC for highly stretched geometries, yet still do provide a qualitatively correct PECs that lie within the region delimited by Le Roy’s analytical PECs.

scholarly journals Self-Supervised Learning for Generalizable Out-of-Distribution Detection

2020 ◽  
Vol 34 (04) ◽  
pp. 5216-5223 ◽  
Author(s):  
Sina Mohseni ◽  
Mandar Pitale ◽  
JBS Yadawa ◽  
Zhangyang Wang
Keyword(s):  
Neural Networks ◽  
Autonomous Vehicles ◽  
Deep Neural Networks ◽  
State Of The Art ◽  
Feature Learning ◽  
Detection Methods ◽  
Training Set ◽  
Safety Critical ◽  
Multiple Image ◽  
A New Technique

The real-world deployment of Deep Neural Networks (DNNs) in safety-critical applications such as autonomous vehicles needs to address a variety of DNNs' vulnerabilities, one of which being detecting and rejecting out-of-distribution outliers that might result in unpredictable fatal errors. We propose a new technique relying on self-supervision for generalizable out-of-distribution (OOD) feature learning and rejecting those samples at the inference time. Our technique does not need to pre-know the distribution of targeted OOD samples and incur no extra overheads compared to other methods. We perform multiple image classification experiments and observe our technique to perform favorably against state-of-the-art OOD detection methods. Interestingly, we witness that our method also reduces in-distribution classification risk via rejecting samples near the boundaries of the training set distribution.

Joint TDOA and FDOA Estimation for Interleaved Pulse Trains From Multiple Pulse Radiation Sources

2020 ◽  
Vol 56 (5) ◽  
pp. 4099-4111
Author(s):  
Fuhe Ma ◽  
Zhang-Meng Liu ◽  
Fucheng Guo ◽  
Daowang Feng ◽  
Le Yang
Keyword(s):  
Pulse Radiation ◽  
Multiple Pulse ◽  
Pulse Trains ◽  
Radiation Sources
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