Development of a tunable mid-IR difference frequency laser source for highly sensitive airborne trace gas detection

The development of a compact tunable mid-IR laser system at 3.5 μm for quantitative airborne spectroscopic trace gas absorption measurements is reported. The mid-IR laser system is based on difference frequency generation (DFG) in periodically poled LiNbO₃ and utilizes optical fiber amplified near-IR diode and fiber lasers as pump sources operating at 1083 nm and 1562 nm, respectively. This paper describes the optical sensor architecture, performance characteristics of individual pump lasers and DFG, as well as its application to wavelength modulation spectroscopy employing an astigmatic Herriott multi-pass gas absorption cell. This compact system permits detection of formaldehyde with a minimal detectable concentration (1σ replicate precision) of 74 parts-per-trillion by volume (pptv) for 1 min of averaging time and was achieved using calibrated gas standards, zero air background and rapid dual-beam subtraction. This corresponds to a pathlength-normalized replicate fractional absorption sensitivity of 2.5×10^-10 cm^-1. Received: 29 April 2002 / Published online: 21 August 2002 RID="*" ID="*"Corresponding author. Fax: +1-303/497-1492, E-mail: dr@ucar.edu     

Mode beating in spot-size converter lasers

An anomalous modulation in the wavelength spectrum has been observed in lasers with spot-size converters. This intensity modulation is shown to be caused by beating between the fundamental lasing mode and radiation modes in the taper. This results in a periodic modulation in the net gain spectrum, which causes wavelength jumps between adjacent net gain maxima, and a drive current dependent spectral width that is expected to affect system performance. The amplitude of this spectral modulation is reduced significantly by either using an angled rear-facet which reflects the beating radiation modes away from the laser axis, or by using a nonlinear, adiabatic taper.     

Noise waveforms with repetitive phase and nonrepetitive amplitude

By use of frequency-shifted feedback lasers, noise with a stationary amplitude and a periodically stationary phase is generated. The ensemble-averaged time correlation function is periodic, whereas the power spectrum is broadband, resulting in a waveform that does not obey the Wiener-Khintchine relations. An application to multiple-access communications is discussed.     

A Particle-Mesh Method for the Shallow Water Equations Near Geostrophic Balance

In this paper we outline a new particle-mesh method for rapidly rotating shallow water flows based on a set of regularized equations of motion. The time-stepping method uses an operator splitting of the equations into an Eulerian gravity wave part and a Lagrangian advection part. An essential ingredient is the advection of absolute vorticity by means of translated radial basis functions. We show that this implies exact conservation of enstrophy. The method is tested on two model problems based on the qualitative features of the solutions obtained (i.e., dispersion or smoothness of potential vorticity contours) as well as on the increase in mean divergence level.     

Total cross section for hadron production bye + e − annihilation between 9.4 and 9.5 GeV

The total cross section fore ⁺ e ^? annihilation into hadrons for center of mass energies from 9.4 to 9.5 GeV has been measured with the nonmagnetic DESY-Heidelberg detector at DORIS. A value ofR=σ_had/σ_µµ=3.8±0.7 for the continuum region around the Υ (9.46) resonance has been determined. The ratioΓ _ee Γ _had/Γ _tot of electronic, hadronic and total widths has been reevaluated to be (1.00±0.23) keV for the Υ resonance and (0.37±0.16) keV for the Υ′. In addition, a search for directly produced pohotons from Υ decays of the type Υ→γ+gluon+gluon has been performed. The Υ decay into muon pairs has also been searched for.     

Probabilistic Predictions with Federated Learning

Probabilistic predictions with machine learning are important in many applications. These are commonly done with Bayesian learning algorithms. However, Bayesian learning methods are computationally expensive in comparison with non-Bayesian methods. Furthermore, the data used to train these algorithms are often distributed over a large group of end devices. Federated learning can be applied in this setting in a communication-efficient and privacy-preserving manner but does not include predictive uncertainty. To represent predictive uncertainty in federated learning, our suggestion is to introduce uncertainty in the aggregation step of the algorithm by treating the set of local weights as a posterior distribution for the weights of the global model. We compare our approach to state-of-the-art Bayesian and non-Bayesian probabilistic learning algorithms. By applying proper scoring rules to evaluate the predictive distributions, we show that our approach can achieve similar performance as the benchmark would achieve in a non-distributed setting.     

A Unified Approach to Local Quantum Uncertainty and Interferometric Power by Metric Adjusted Skew Information

Local quantum uncertainty and interferometric power were introduced by Girolami et al. as geometric quantifiers of quantum correlations. The aim of the present paper is to discuss their properties in a unified manner by means of the metric adjusted skew information defined by Hansen.     

On a Variational Definition for the Jensen-Shannon Symmetrization of Distances Based on the Information Radius

We generalize the Jensen-Shannon divergence and the Jensen-Shannon diversity index by considering a variational definition with respect to a generic mean, thereby extending the notion of Sibson’s information radius. The variational definition applies to any arbitrary distance and yields a new way to define a Jensen-Shannon symmetrization of distances. When the variational optimization is further constrained to belong to prescribed families of probability measures, we get relative Jensen-Shannon divergences and their equivalent Jensen-Shannon symmetrizations of distances that generalize the concept of information projections. Finally, we touch upon applications of these variational Jensen-Shannon divergences and diversity indices to clustering and quantization tasks of probability measures, including statistical mixtures.