Feature pyramids are widely exploited by both the state-ofthe-art one-stage object detectors (e.g., DSSD, RetinaNet, RefineDet) and the two-stage object detectors (e.g., Mask R-CNN, DetNet) to alleviate the problem arising from scale variation across object instances. Although these object detectors with feature pyramids achieve encouraging results, they have some limitations due to that they only simply construct the feature pyramid according to the inherent multiscale, pyramidal architecture of the backbones which are originally designed for object classification task. Newly, in this work, we present Multi-Level Feature Pyramid Network (MLFPN) to construct more effective feature pyramids for detecting objects of different scales. First, we fuse multi-level features (i.e. multiple layers) extracted by backbone as the base feature. Second, we feed the base feature into a block of alternating joint Thinned U-shape Modules and Feature Fusion Modules and exploit the decoder layers of each Ushape module as the features for detecting objects. Finally, we gather up the decoder layers with equivalent scales (sizes) to construct a feature pyramid for object detection, in which every feature map consists of the layers (features) from multiple levels. To evaluate the effectiveness of the proposed MLFPN, we design and train a powerful end-to-end onestage object detector we call M2Det by integrating it into the architecture of SSD, and achieve better detection performance than state-of-the-art one-stage detectors. Specifically, on MS-COCO benchmark, M2Det achieves AP of 41.0 at speed of 11.8 FPS with single-scale inference strategy and AP of 44.2 with multi-scale inference strategy, which are the new state-of-the-art results among one-stage detectors. The code will be made available on https://github.com/ qijiezhao/M2Det.

Edited by Gianni Cesareni Keywords:Fibroblast growth factor-21 b-Klotho Fibroblast growth factor receptor Partial agonist a b s t r a c t Fibroblast growth factor-21 (FGF21) signaling requires the presence of b-Klotho, a co-receptor with a very short cytoplasmic domain. Here we show that FGF21 binds directly to b-Klotho through its Cterminus. Serial C-terminal truncations of FGF21 weakened or even abrogated its interaction with b-Klotho in a Biacore assay, and led to gradual loss of potency in a luciferase reporter assay but with little effect on maximal response. In contrast, serial N-terminal truncations of FGF21 had no impact on b-Klotho binding. Interestingly, several of them exhibited characteristics of partial agonists with minimal effects on potency. These data demonstrate that the C-terminus of FGF21 is critical for binding to b-Klotho and the N-terminus is critical for fibroblast growth factor receptor (FGFR) activation.

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Traffic forecasting is a challenging problem due to the complexity of jointly modeling spatio‐temporal dependencies at different scales. Recently, several hybrid deep learning models have been developed to capture such dependencies. These approaches typically utilize convolutional neural networks or graph neural networks (GNNs) to model spatial dependency and leverage recurrent neural networks (RNNs) to learn temporal dependency. However, RNNs are only able to capture sequential information in the time series, while being incapable of modeling their periodicity (e.g., weekly patterns). Moreover, RNNs are difficult to parallelize, making training and prediction less efficient. In this work we propose a novel deep learning architecture called Traffic Transformer to capture the continuity and periodicity of time series and to model spatial dependency. Our work takes inspiration from Google’s Transformer framework for machine translation. We conduct extensive experiments on two real‐world traffic data sets, and the results demonstrate that our model outperforms baseline models by a substantial margin.

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