3D Architected Carbon Electrodes for Energy Storage

The ability to design a particular geometry of porous electrodes at multiple length scales in a lithium‐ion battery can significantly and positively influence battery performance because it enables control over distribution of current and potential and can enhance ion and electron transport. 3D architecturally designed carbon electrodes are developed, whose structural factors are independently controlled and whose dimensions span micrometers to centimeters, using digital light processing and pyrolysis. These free‐standing lattice electrodes are comprised of monolithic glassy carbon beams, are lightweight, with a relative density of 0.1–0.35, and mechanically robust, with a maximum precollapse stress of 27 MPa, which facilitates electrode recycling. The specific strength is 101 kN m kg⁻¹, comparable to that of 6061 aluminum alloy. These carbon electrodes can reach a mass loading of 70 mg cm⁻² and an areal capacity of 3.2 mAh cm⁻² at a current density of 2.4 mA cm⁻². It is demonstrated that this approach allows for independent design of structural factors, i.e., beam diameter, electrode thickness, and surface morphology, enabling control over Li‐ion transport length, overpotential and battery performance, not available for slurry‐based electrodes. This multiscale approach to design of electrodes may open substantial performance‐enhancing capabilities for solid‐ and liquid‐state batteries, flow batteries, and fuel cells

Boosting API Recommendation with Implicit Feedback

Developers often need to use appropriate APIs to program efficiently, but it is usually a difficult task to identify the exact one they need from a vast of candidates. To ease the burden, a multitude of API recommendation approaches have been proposed. However, most of the currently available API recommenders do not support the effective integration of users' feedback into the recommendation loop. In this paper, we propose a framework, BRAID (Boosting RecommendAtion with Implicit FeeDback), which leverages learning-to-rank and active learning techniques to boost recommendation performance. By exploiting users' feedback information, we train a learning-to-rank model to re-rank the recommendation results. In addition, we speed up the feedback learning process with active learning. Existing query-based API recommendation approaches can be plugged into BRAID. We select three state-of-the-art API recommendation approaches as baselines to demonstrate the performance enhancement of BRAID measured by Hit@k (Top-k), MAP, and MRR. Empirical experiments show that, with acceptable overheads, the recommendation performance improves steadily and substantially with the increasing percentage of feedback data, comparing with the baselines.Comment: 15 pages, 4 figure

ROME: Robustifying Memory-Efficient NAS via Topology Disentanglement and Gradients Accumulation

Single-path based differentiable neural architecture search has great strengths for its low computational cost and memory-friendly nature. However, we surprisingly discover that it suffers from severe searching instability which has been primarily ignored, posing a potential weakness for a wider application. In this paper, we delve into its performance collapse issue and propose a new algorithm called RObustifying Memory-Efficient NAS (ROME). Specifically, 1) for consistent topology in the search and evaluation stage, we involve separate parameters to disentangle the topology from the operations of the architecture. In such a way, we can independently sample connections and operations without interference; 2) to discount sampling unfairness and variance, we enforce fair sampling for weight update and apply a gradient accumulation mechanism for architecture parameters. Extensive experiments demonstrate that our proposed method has strong performance and robustness, where it mostly achieves state-of-the-art results on a large number of standard benchmarks.Comment: Observe new collapse in memory efficient NAS and address it using ROM