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Low-density meter-scale plasma waveguides produced in meter-scale supersonic gas jets have paved the way for recent demonstrations of all-optical multi-gigaelectronvolt laser wakefield acceleration (LWFA). This paper reviews recent advances by the University of Maryland, which have enabled these results, focusing on the development of elongated supersonic gas jets up to ∼1 m in length, experimental and simulation studies of plasma waveguide formation, and a new three-stage model for relativistic pulse propagation dynamics in these waveguides. We also present results from recent LWFA experiments conducted at the Laboratory for Advanced Lasers and Extreme Photonics at Colorado State University demonstrating high charge, low divergence electron bunches to ∼10 GeV, with laser-to-electron beam efficiency of at least ∼30%. 

Although recent advances in machine learning have shown its success to learn from independent and identically distributed (IID) data, it is vulnerable to out-of-distribution (OOD) data in an open world. Domain generalization (DG) deals with such an issue and it aims to learn a model from multiple source domains that can be generalized to unseen target domains. Existing studies on DG have largely focused on stationary settings with homogeneous source domains. However, in many applications, domains may evolve along a specific direction (e.g., time, space). Without accounting for such non-stationary patterns, models trained with existing methods may fail to generalize on OOD data. In this paper, we study domain generalization in non-stationary environment. We first examine the impact of environmental non-stationarity on model performance and establish the theoretical upper bounds for the model error at target domains. Then, we propose a novel algorithm based on adaptive invariant representation learning, which leverages the non-stationary pattern to train a model that attains good performance on target domains. Experiments on both synthetic and real data validate the proposed algorithm. 

Abstract Recently proposed as a stable means of evaluating geometric compactness, theisoperimetric profileof a planar domain measures the minimum perimeter needed to inscribe a shape with prescribed area varying from 0 to the area of the domain. While this profile has proven valuable for evaluating properties of geographic partitions, existing algorithms for its computation rely on aggressive approximations and are still computationally expensive. In this paper, we propose a practical means of approximating the isoperimetric profile and show that for domains satisfying a“thick neck”condition, our approximation is exact. For more general domains, we show that our bound is still exact within a conservative regime and is otherwise an upper bound. Our method is based on a traversal of the medial axis which produces efficient and robust results. We compare our technique with the state‐of‐the‐art approximation to the isoperimetric profile on a variety of domains and show significantly tighter bounds than were previously achievable.