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  1. Abstract

    Monoculture switchgrass and restored prairie are promising perennial feedstock sources for bioenergy production on the lands unsuitable for conventional agriculture. Such lands often display contrasting topography that influences soil characteristics and interactions between plant growth and soil C gains. This study aimed at elucidating the influences of topography and plant systems on the fate of C originated from switchgrass plants and on its relationships with soil pore characteristics. For that, switchgrass plants were grown in intact soil cores collected from two contrasting topographies, namely steep slopes and topographical depressions, in the fields in multi-year monoculture switchgrass and restored prairie vegetation. The13C pulse labeling allowed tracing the C of switchgrass origin, which X-ray computed micro-tomography enabled in-detail characterization of soil pore structure. In eroded slopes, the differences between the monoculture switchgrass and prairie in terms of total and microbial biomass C were greater than those in topographical depressions. While new switchgrass increased the CO2emission in depressions, it did not significantly affect the CO2emission in slopes. Pores of 18–90 µm Ø facilitated the accumulation of new C in soil, while > 150 µm Ø pores enhanced the mineralization of the new C. These findings suggest that polyculture prairie located in slopes can be particularly beneficial in facilitating soil C accrual and reduce C losses as CO2.

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  2. Free, publicly-accessible full text available October 1, 2024
  3. Vision Transformer (ViT) has demonstrated promising performance in various computer vision tasks, and recently attracted a lot of research attention. Many recent works have focused on proposing new architectures to improve ViT and deploying it into real-world applications. However, little effort has been made to analyze and understand ViT’s architecture design space and its implication of hardware-cost on different devices. In this work, by simply scaling ViT’s depth, width, input size, and other basic configurations, we show that a scaled vanilla ViT model without bells and whistles can achieve comparable or superior accuracy-efficiency trade-off than most of the latest ViT variants. Specifically, compared to DeiT-Tiny, our scaled model achieves a\(\uparrow 1.9\% \)higher ImageNet top-1 accuracy under the same FLOPs and a\(\uparrow 3.7\% \)better ImageNet top-1 accuracy under the same latency on an NVIDIA Edge GPU TX2. Motivated by this, we further investigate the extracted scaling strategies from the following two aspects: (1) “can these scaling strategies be transferred across different real hardware devices?”; and (2) “can these scaling strategies be transferred to different ViT variants and tasks?”. For (1), our exploration, based on various devices with different resource budgets, indicates that the transferability effectiveness depends on the underlying device together with its corresponding deployment tool; for (2), we validate the effective transferability of the aforementioned scaling strategies obtained from a vanilla ViT model on top of an image classification task to the PiT model, a strong ViT variant targeting efficiency, as well as object detection and video classification tasks. In particular, when transferred to PiT, our scaling strategies lead to a boosted ImageNet top-1 accuracy of from\(74.6\% \)to\(76.7\% \)(\(\uparrow 2.1\% \)) under the same 0.7G FLOPs; and when transferred to the COCO object detection task, the average precision is boosted by\(\uparrow 0.7\% \)under a similar throughput on a V100 GPU.

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    Free, publicly-accessible full text available August 21, 2024
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