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Abstract Materials that blend physical properties that are usually mutually exclusive could facilitate devices with novel functionalities. For example, the doped perovskite alkaline earth stannates BaSnO 3 and SrSnO 3 show the intriguing combination of high light transparency and high electrical conductivity. Understanding such emergent physics requires deep insight into the materials’ electronic structures. Moreover, the band structure at the surfaces of those materials can deviate significantly from their bulk counterparts, thereby unlocking novel physical phenomena. Employing angle-resolved photoemission spectroscopy and ab initio calculations, we reveal the existence of a 2-dimensional metallic state at the SnO 2 -terminated surface of 1% La-doped BaSnO 3 thin films. The observed surface state is characterized by a distinct carrier density and a lower effective mass compared to the bulk conduction band, of about 0.12 m e . These particular surface state properties place BaSnO 3 among the materials suitable for engineering highly conductive transition metal oxide heterostructures. 

There has been significant interest in leveraging limited look-ahead to achieve low competitive ratios for online convex optimization (OCO). However, existing online algorithms (such as Averaging Fixed Horizon Control (AFHC)) that can leverage look-ahead to reduce the competitive ratios still produce competitive ratios that grow unbounded as the coefficient ratio (i.e., the maximum ratio of the switching-cost coefficient and the service-cost coefficient) increases. On the other hand, the regularization method can attain a competitive ratio that remains bounded when the coefficient ratio is large, but it does not benefit from look-ahead. In this paper, we propose a new algorithm, called Regularization with Look-Ahead (RLA), that can get the best of both AFHC and the regularization method, i.e., its competitive ratio decreases with the look-ahead window size when the coefficient ratio is small, and remains bounded when the coefficient ratio is large. We also provide a matching lower bound for the competitive ratios of all online algorithms with look-ahead, which differs from the achievable competitive ratio of RLA by a factor that only depends on the problem size. The competitive analysis of RLA involves a non-trivial generalization of online primal-dual analysis to the case with look-ahead. 

We investigate competitive online algorithms for online convex optimization (OCO) problems with linear in-stage costs, switching costs and ramp constraints. While OCO problems have been extensively studied in the literature, there are limited results on the corresponding online solutions that can attain small competitive ratios. We first develop a powerful computational framework that can compute an optimized competitive ratio based on the class of affine policies. Our computational framework can handle a fairly general class of costs and constraints. Compared to other competitive results in the literature, a key feature of our proposed approach is that it can handle scenarios where infeasibility may arise due to hard feasibility constraints. Second, we design a robustification procedure to produce an online algorithm that can attain good performance for both average-case and worst-case inputs. We conduct a case study on Network Functions Virtualization (NFV) orchestration and scaling to demonstrate the effectiveness of our proposed methods.