Revealing the Transition Dynamics from Q Switching to Mode Locking in a Soliton Laser.

Q switching (QS) and mode locking (ML) are the two main techniques enabling generation of ultrashort pulses. Here, we report the first observation of pulse evolution and dynamics in the QS-ML transition stage, where the ML soliton formation evolves from the QS pulses instead of relaxation oscillations (or quasi-continuous-wave oscillations) reported in previous studies. We discover a new way of soliton buildup in an ultrafast laser, passing through four stages: initial spontaneous noise, QS, beating dynamics, and ML. We reveal that multiple subnanosecond pulses coexist within the laser cavity during the QS, with one dominant pulse transforming into a soliton when reaching the ML stage. We propose a theoretical model to simulate the spectrotemporal beating dynamics (a critical process of QS-ML transition) and the Kelly sidebands of the as-formed solitons. Numerical results show that beating dynamics is induced by the interference between a dominant pulse and multiple subordinate pulses with varying temporal delays, in agreement with experimental observations. Our results allow a better understanding of soliton formation in ultrafast lasers, which have widespread applications in science and technology

Formation Control for Moving Target Enclosing via Relative Localization

In this paper, we investigate the problem of controlling multiple unmanned aerial vehicles (UAVs) to enclose a moving target in a distributed fashion based on a relative distance and self-displacement measurements. A relative localization technique is developed based on the recursive least square estimation (RLSE) technique with a forgetting factor to estimates both the ``UAV-UAV'' and ``UAV-target'' relative positions. The formation enclosing motion is planned using a coupled oscillator model, which generates desired motion for UAVs to distribute evenly on a circle. The coupled-oscillator-based motion can also facilitate the exponential convergence of relative localization due to its persistent excitation nature. Based on the generation strategy of desired formation pattern and relative localization estimates, a cooperative formation tracking control scheme is proposed, which enables the formation geometric center to asymptotically converge to the moving target. The asymptotic convergence performance is analyzed theoretically for both the relative localization technique and the formation control algorithm. Numerical simulations are provided to show the efficiency of the proposed algorithm. Experiments with three quadrotors tracking one target are conducted to evaluate the proposed target enclosing method in real platforms.Comment: 8 Pages, accepted by IEEE CDC 202

End-to-End Pareto Set Prediction with Graph Neural Networks for Multi-objective Facility Location

The facility location problems (FLPs) are a typical class of NP-hard combinatorial optimization problems, which are widely seen in the supply chain and logistics. Many mathematical and heuristic algorithms have been developed for optimizing the FLP. In addition to the transportation cost, there are usually multiple conflicting objectives in realistic applications. It is therefore desirable to design algorithms that find a set of Pareto solutions efficiently without enormous search cost. In this paper, we consider the multi-objective facility location problem (MO-FLP) that simultaneously minimizes the overall cost and maximizes the system reliability. We develop a learning-based approach to predicting the distribution probability of the entire Pareto set for a given problem. To this end, the MO-FLP is modeled as a bipartite graph optimization problem and two graph neural networks are constructed to learn the implicit graph representation on nodes and edges. The network outputs are then converted into the probability distribution of the Pareto set, from which a set of non-dominated solutions can be sampled non-autoregressively. Experimental results on MO-FLP instances of different scales show that the proposed approach achieves a comparable performance to a widely used multi-objective evolutionary algorithm in terms of the solution quality while significantly reducing the computational cost for search.Comment: 14 pages, 3 figure

An Edge-Aware Graph Autoencoder Trained on Scale-Imbalanced Data for Travelling Salesman Problems

Recent years have witnessed a surge in research on machine learning for combinatorial optimization since learning-based approaches can outperform traditional heuristics and approximate exact solvers at a lower computation cost. However, most existing work on supervised neural combinatorial optimization focuses on TSP instances with a fixed number of cities and requires large amounts of training samples to achieve a good performance, making them less practical to be applied to realistic optimization scenarios. This work aims to develop a data-driven graph representation learning method for solving travelling salesman problems (TSPs) with various numbers of cities. To this end, we propose an edge-aware graph autoencoder (EdgeGAE) model that can learn to solve TSPs after being trained on solution data of various sizes with an imbalanced distribution. We formulate the TSP as a link prediction task on sparse connected graphs. A residual gated encoder is trained to learn latent edge embeddings, followed by an edge-centered decoder to output link predictions in an end-to-end manner. To improve the model's generalization capability of solving large-scale problems, we introduce an active sampling strategy into the training process. In addition, we generate a benchmark dataset containing 50,000 TSP instances with a size from 50 to 500 cities, following an extremely scale-imbalanced distribution, making it ideal for investigating the model's performance for practical applications. We conduct experiments using different amounts of training data with various scales, and the experimental results demonstrate that the proposed data-driven approach achieves a highly competitive performance among state-of-the-art learning-based methods for solving TSPs.Comment: 35 pages, 7 figure

Drought Tolerance Dissection and Molecular Breeding in Alfalfa

Drought stress is one of the leading impediments that limit the productivity of global alfalfa (Medicago sativa). The underlying molecular and genetic mechanisms for drought tolerance in alfalfa remain largely unclear. In order to fully reveal the transcriptional changes of alfalfa in response to abiotic stress, the alfalfa transcriptome database under mannitol (simulated drought stress), NaCl (simulated salt stress), or exogenous ABA application was built via various RNA-seq technologies. Through further screening of the transcriptome database, a number of genes significantly induced by drought stress, such as the Nuclear Transport Factor 2-like (MsNTF2L), Drought-Induced Unknown Protein 1 (MsDIUP1), and MsNST1, were identified. These three genes were transferred into alfalfa by overexpression and RNAi techniques, and their physiological characteristics and transcriptional level response were synthetically studied. Alfalfa MsNTF2L-OE plants have been approved by the Ministry of Agriculture of China to carry out the field test in Gansu Province. Furthermore, we constructed a GWAS population and obtained 50 excellent plants with strong drought tolerance and high hay-yield. These studies provide a theoretical foundation for drought-tolerant molecular breeding of alfalfa

TTVFI: Learning Trajectory-Aware Transformer for Video Frame Interpolation

Video frame interpolation (VFI) aims to synthesize an intermediate frame between two consecutive frames. State-of-the-art approaches usually adopt a two-step solution, which includes 1) generating locally-warped pixels by flow-based motion estimations, 2) blending the warped pixels to form a full frame through deep neural synthesis networks. However, due to the inconsistent warping from the two consecutive frames, the warped features for new frames are usually not aligned, which leads to distorted and blurred frames, especially when large and complex motions occur. To solve this issue, in this paper we propose a novel Trajectory-aware Transformer for Video Frame Interpolation (TTVFI). In particular, we formulate the warped features with inconsistent motions as query tokens, and formulate relevant regions in a motion trajectory from two original consecutive frames into keys and values. Self-attention is learned on relevant tokens along the trajectory to blend the pristine features into intermediate frames through end-to-end training. Experimental results demonstrate that our method outperforms other state-of-the-art methods in four widely-used VFI benchmarks. Both code and pre-trained models will be released soon