Numerical investigation of effective nonlinear coefficient model for coupled third harmonic generation

In this paper, the optimal solution of effective nonlinear coefficient of quasi-phase-matching (QPM) crystals for coupled third harmonic generation (CTHG) was numerically investigated. The effective nonlinear coefficient of CTHG was converted to an Ising model for optimizing domain length distributions of aperiodically poled lithium niobate (APPLN) crystals with lengths as 0.5 mm and 1 mm, and fundamental wavelengths ranging from 1000 nm to 6000 nm. A method for reconstructing crystal domain poling weight curve of coupled nonlinear processes was also proposed, which demonstrated the optimal conversion ratio between two coupled nonlinear processes at each place along the crystal. In addition, by applying the semidefinite programming, the upper bound on the effective nonlinear coefficients deff for different fundamental wavelengths were calculated. The research can be extended to any coupled dual \c{hi}(2) process and will help us to understand better the dynamics of coupled nonlinear interactions based on QPM crystals.Comment: 16 page

Online Nash Welfare Maximization Without Predictions

Nash welfare maximization is widely studied because it balances efficiency and fairness in resource allocation problems. Banerjee, Gkatzelis, Gorokh, and Jin (2022) recently introduced the model of online Nash welfare maximization with predictions for $T$ divisible items and $N$ agents with additive utilities. They gave online algorithms whose competitive ratios are logarithmic. We initiate the study of online Nash welfare maximization \emph{without predictions}, assuming either that the agents' utilities for receiving all items differ by a bounded ratio, or that their utilities for the Nash welfare maximizing allocation differ by a bounded ratio. We design online algorithms whose competitive ratios only depend on the logarithms of the aforementioned ratios of agents' utilities and the number of agents

Identification of Causal Structure with Latent Variables Based on Higher Order Cumulants

Causal discovery with latent variables is a crucial but challenging task. Despite the emergence of numerous methods aimed at addressing this challenge, they are not fully identified to the structure that two observed variables are influenced by one latent variable and there might be a directed edge in between. Interestingly, we notice that this structure can be identified through the utilization of higher-order cumulants. By leveraging the higher-order cumulants of non-Gaussian data, we provide an analytical solution for estimating the causal coefficients or their ratios. With the estimated (ratios of) causal coefficients, we propose a novel approach to identify the existence of a causal edge between two observed variables subject to latent variable influence. In case when such a causal edge exits, we introduce an asymmetry criterion to determine the causal direction. The experimental results demonstrate the effectiveness of our proposed method.Comment: Accepted by AAAI 202

Code Generation as a Dual Task of Code Summarization

Code summarization (CS) and code generation (CG) are two crucial tasks in the field of automatic software development. Various neural network-based approaches are proposed to solve these two tasks separately. However, there exists a specific intuitive correlation between CS and CG, which have not been exploited in previous work. In this paper, we apply the relations between two tasks to improve the performance of both tasks. In other words, exploiting the duality between the two tasks, we propose a dual training framework to train the two tasks simultaneously. In this framework, we consider the dualities on probability and attention weights, and design corresponding regularization terms to constrain the duality. We evaluate our approach on two datasets collected from GitHub, and experimental results show that our dual framework can improve the performance of CS and CG tasks over baselines.Comment: To appear at the 33rd Conference on Neural Information Processing Systems (NeurIPS) 201

Supercontinuum generation and carrier envelope offset frequency measurement in a tapered single-mode fiber

We report supercontinuum generation by launching femtosecond Yb fiber laser pulses into a tapered single-mode fiber of 3 um core diameter. A spectrum of more than one octave, from 550 to 1400 nm, has been obtained with an output power of 1.3 W at a repetition rate of 250 MHz, corresponding to a coupling efficiency of up to 60%. By using a typical f-2f interferometer, the carrier envelope offset frequency was measured and found to have a signal-to-noise ratio of nearly 30 dB.Comment: 10 pages, accepted by Appl Phys

SelFLoc: Selective Feature Fusion for Large-scale Point Cloud-based Place Recognition

Point cloud-based place recognition is crucial for mobile robots and autonomous vehicles, especially when the global positioning sensor is not accessible. LiDAR points are scattered on the surface of objects and buildings, which have strong shape priors along different axes. To enhance message passing along particular axes, Stacked Asymmetric Convolution Block (SACB) is designed, which is one of the main contributions in this paper. Comprehensive experiments demonstrate that asymmetric convolution and its corresponding strategies employed by SACB can contribute to the more effective representation of point cloud feature. On this basis, Selective Feature Fusion Block (SFFB), which is formed by stacking point- and channel-wise gating layers in a predefined sequence, is proposed to selectively boost salient local features in certain key regions, as well as to align the features before fusion phase. SACBs and SFFBs are combined to construct a robust and accurate architecture for point cloud-based place recognition, which is termed SelFLoc. Comparative experimental results show that SelFLoc achieves the state-of-the-art (SOTA) performance on the Oxford and other three in-house benchmarks with an improvement of 1.6 absolute percentages on mean average recall@1