Nonlinearity-Compensation-Free Optical Frequency Domain Reflectometry Based on Electrically-Controlled Optical Frequency Sweep

A nonlinearity-compensation-free optical frequency domain reflectometry(OFDR)scheme is proposed and experimentally demonstrated based on the electrically-controlled optical frequency sweep.In the proposed scheme,the linear frequency sweep light is generated by propagating an ultra-narrow-linewidth continuous-wave(CW)light through an electro-optic frequency shifter which consists of a dual-parallel Mach-Zehnder modulator(DPMZM)and an electronic 90°hybrid,where the electro-optic frequency shifter is driven by a linear frequency modulated signal generated by a direct digital synthesizer(DDS).Experimental results show that the spatial resolution and signal-to-noise ratio(SNR)of the proposed OFDR scheme without the nonlinear phase compensation are comparable to those of OFDR employing a commercial tunable laser source(TLS),an auxiliary interferometer,and a software-based nonlinear phase compensation method.The proposed OFDR scheme is helpful to reduce the complexity of the optical structure and eliminate the difficulty of developing the nonlinear phase compensation algorithm.

Privacy-Preserving Frank-Wolfe on Shuffle Model

In this paper,we design the differentially private variants of the classical Frank-Wolfe algorithm with shuffle model in the optimization of machine learning.Under weak assumptions and the generalized linear loss(GLL)structure,we propose a noisy Frank-Wolfe with shuffle model algorithm(NoisyFWS)and a noisy variance-reduced Frank-Wolfe with the shuffle model algorithm(NoisyVRFWS)by adding calibrated laplace noise under shuffling scheme in thel_(p)(p∈[1,2])-case,and study their privacy as well as utility guarantees for the H?lder smoothness GLL.In particular,the privacy guarantees are mainly achieved by using advanced composition and privacy amplification by shuffling.The utility bounds of the Noisy FWS and NoisyVRFWS are analyzed and obtained the optimal excess population risksO(n-(1+α/4α+log(d)√log(1/δ)/n∈and O(n-1+α/4α+log(d)√log1(+δ)/n^(2)∈with gradient complexity O(n(1+α)^(2)/4α^(2)forα∈[1/√3,1].It turns out that the risk rates under shuffling scheme are a nearly-dimension independent rate,which is consistent with the previous work in some cases.In addition,there is a vital tradeoff between(α,L)-Holder smoothness GLL and the gradient complexity.The linear gradient complexity O(n)is showed by the parameterα=1.