Towards Better Accuracy-efficiency Trade-offs: Divide and Co-training

The width of a neural network matters since increasing the width will necessarily increase the model capacity. However, the performance of a network does not improve linearly with the width and soon gets saturated. In this case, we argue that increasing the number of networks (ensemble) can achieve better accuracy-efficiency trade-offs than purely increasing the width. To prove it, one large network is divided into several small ones regarding its parameters and regularization components. Each of these small networks has a fraction of the original one's parameters. We then train these small networks together and make them see various views of the same data to increase their diversity. During this co-training process, networks can also learn from each other. As a result, small networks can achieve better ensemble performance than the large one with few or no extra parameters or FLOPs. Small networks can also achieve faster inference speed than the large one by concurrent running on different devices. We validate our argument with 8 different neural architectures on common benchmarks through extensive experiments. The code is available at \url{https://github.com/mzhaoshuai/Divide-and-Co-training}

Design Principles for High-Capacity Mn-Based Cation-Disordered Rocksalt Cathodes

Mn-based Li-excess cation-disordered rocksalt (DRX) oxyfluorides are promising candidates for next-generation rechargeable battery cathodes owing to their large energy densities, the earth abundance, and low cost of Mn. In this work, we synthesized and electrochemically tested four representative compositions in the Li-Mn-O-F DRX chemical space with various Li and F content. While all compositions achieve higher than 200 mAh g−1 initial capacity and good cyclability, we show that the Li-site distribution plays a more important role than the metal-redox capacity in determining the initial capacity, whereas the metal-redox capacity is more closely related to the cyclability of the materials. We apply these insights and generate a capacity map of the Li-Mn-O-F chemical space, LixMn2-xO2-yFy (1.167 ≤ x ≤ 1.333, 0 ≤ y ≤ 0.667), which predicts both accessible Li capacity and Mn-redox capacity. This map allows the design of compounds that balance high capacity with good cyclability

Direct fiber vector eigenmode multiplexing transmission seeded by integrated optical vortex emitters

Spatial modes have received substantial attention over the last decades and are used in optical communication applications. In fiber-optic communications, the employed linearly polarized modes and phase vortex modes carrying orbital angular momentum can be synthesized by fiber vector eigenmodes. To improve the transmission capacity and miniaturize the communication system, straightforward fiber vector eigenmode multiplexing and generation of fiber-eigenmode-like polarization vortices (vector vortex modes) using photonic integrated devices are of substantial interest. Here, we propose and demonstrate direct fiber vector eigenmode multiplexing transmission seeded by integrated optical vortex emitters. By exploiting vector vortex modes (radially and azimuthally polarized beams) generated from silicon microring resonators etched with angular gratings, we report data-carrying fiber vector eigenmode multiplexing transmission through a 2-km large-core fiber, showing low-level mode crosstalk and favorable link performance. These demonstrations may open up added capacity scaling opportunities by directly accessing multiple vector eigenmodes in the fiber and provide compact solutions to replace bulky diffractive optical elements for generating various optical vector beams