Recent advances in deep learning, especially deep convolutional neural networks (CNNs), have led to significant improvement over previous semantic segmentation systems.Here we show how to improve pixel-wise semantic segmentation by manipulating convolution-related operations that are of both theoretical and practical value. First, we design dense upsampling convolution (DUC) to generate pixel-level prediction, which is able to capture and decode more detailed information that is generally missing in bilinear upsampling. Second, we propose a hybrid dilated convolution (HDC) framework in the encoding phase. This framework 1) effectively enlarges the receptive fields (RF) of the network to aggregate global information; 2) alleviates what we call the "gridding issue"caused by the standard dilated convolution operation. We evaluate our approaches thoroughly on the Cityscapes dataset, and achieve a state-of-art result of 80.1% mIOU in the test set at the time of submission. We also have achieved state-of-theart overall on the KITTI road estimation benchmark and the PASCAL VOC2012 segmentation task. Our source code can be found at https

Evidence suggests that intervertebral disc degeneration (IVDD) can be induced by Propionibacterium acnes (P. acnes), although the underlying mechanisms are unclear. In this study, we analyzed the pathological changes in degenerated human intervertebral discs (IVDs) infected with P. acnes. Compared with P. acnes-negative samples, P. acnes-positive IVDs showed increased apoptosis of nucleus pulposus cells (NPCs) concomitant with severe IVDD. Then, a P. acnes-inoculated IVD animal model was established, and severe IVDD was induced by P. acnes infection by promoting NPC apoptosis. The results suggested that P.acnes-induced apoptosis of NPCs via the Toll-like receptor 2 (TLR2)/c-Jun N-terminal kinase (JNK) pathway and mitochondrial-mediated cell death. In addition, P. acnes was found to activate autophagy, which likely plays a role in apoptosis of NPCs. Overall, these findings further validated the involvement of P. acnes in the pathology of IVDD and provided evidence that P. acnes-induced apoptosis of NPCs via the TLR2/JNK pathway is likely responsible for the pathology of IVDD.

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Attention mechanisms have been found effective for person re-identification (Re-ID). However, the learned "attentive" features are often not naturally uncorrelated or "diverse", which compromises the retrieval performance based on the Euclidean distance. We advocate the complementary powers of attention and diversity for Re-ID, by proposing an Attentive but Diverse Network (ABD-Net). ABD-Net seamlessly integrates attention modules and diversity regularizations throughout the entire network to learn features that are representative, robust, and more discriminative. Specifically, we introduce a pair of complementary attention modules, focusing on channel aggregation and position awareness, respectively. Then, we plug in a novel orthogonality constraint that efficiently enforces diversity on both hidden activations and weights. Through an extensive set of ablation study, we verify that the attentive and diverse terms each contributes to the performance boosts of ABD-Net. It consistently outperforms existing state-of-the-art methods on there popular person Re-ID benchmarks. * This also exploits attention mechanisms.• This is with a ResNet-152 backbone. This is with a DenseNet-121 backbone. ‡ Official codes are not released. We report the numbers in the original paper, which are better than our re-implementation.comes 3.40% for top-1 and 6.40% for mAP. We also considered SVDNet [13] and HA- CNN [50] which also proposed to generate diverse and uncorrelated feature embeddings. ABD-Net surpasses both with significant top-1 and mAP improvement. Overall, our observations endorse the superiority of ABD-Net by combing "attentive" and "diverse". VisualizationsAttention Pattern Visualization: We conduct a set of attention visualizations * * on the final output feature maps of the baseline (XE), baseline (XE) + PAM + CAM, and ABD-Net (XE), as shown in Fig.5. We notice that the feature maps from the baseline show little attentiveness. PAM + * * Grad-CAM visualization method [73]: https://github.com/ utkuozbulak/pytorch-cnn-visualizations; RAM visualization method [74] for testing images. More results can be found in the supplementary.

Several methods were developed to mine gene–gene relationships from expression data. Examples include correlation and mutual information methods for coexpression analysis, clustering and undirected graphical models for functional assignments, and directed graphical models for pathway reconstruction. Using an encoding for gene expression data, followed by deep neural networks analysis, we present a framework that can successfully address all of these diverse tasks. We show that our method, convolutional neural network for coexpression (CNNC), improves upon prior methods in tasks ranging from predicting transcription factor targets to identifying disease-related genes to causality inference. CNNC’s encoding provides insights about some of the decisions it makes and their biological basis. CNNC is flexible and can easily be extended to integrate additional types of genomics data, leading to further improvements in its performance.