Wavelet Integrated CNNs for Noise-Robust Image Classification

Convolutional Neural Networks (CNNs) are generally prone to noise interruptions, i.e., small image noise can cause drastic changes in the output. To suppress the noise effect to the final predication, we enhance CNNs by replacing max-pooling, strided-convolution, and average-pooling with Discrete Wavelet Transform (DWT). We present general DWT and Inverse DWT (IDWT) layers applicable to various wavelets like Haar, Daubechies, and Cohen, etc., and design wavelet integrated CNNs (WaveCNets) using these layers for image classification. In WaveCNets, feature maps are decomposed into the low-frequency and high-frequency components during the down-sampling. The low-frequency component stores main information including the basic object structures, which is transmitted into the subsequent layers to extract robust high-level features. The high-frequency components, containing most of the data noise, are dropped during inference to improve the noise-robustness of the WaveCNets. Our experimental results on ImageNet and ImageNet-C (the noisy version of ImageNet) show that WaveCNets, the wavelet integrated versions of VGG, ResNets, and DenseNet, achieve higher accuracy and better noise-robustness than their vanilla versions.Comment: CVPR accepted pape

Accelerating Generic Graph Neural Networks via Architecture, Compiler, Partition Method Co-Design

Graph neural networks (GNNs) have shown significant accuracy improvements in a variety of graph learning domains, sparking considerable research interest. To translate these accuracy improvements into practical applications, it is essential to develop high-performance and efficient hardware acceleration for GNN models. However, designing GNN accelerators faces two fundamental challenges: the high bandwidth requirement of GNN models and the diversity of GNN models. Previous works have addressed the first challenge by using more expensive memory interfaces to achieve higher bandwidth. For the second challenge, existing works either support specific GNN models or have generic designs with poor hardware utilization. In this work, we tackle both challenges simultaneously. First, we identify a new type of partition-level operator fusion, which we utilize to internally reduce the high bandwidth requirement of GNNs. Next, we introduce partition-level multi-threading to schedule the concurrent processing of graph partitions, utilizing different hardware resources. To further reduce the extra on-chip memory required by multi-threading, we propose fine-grained graph partitioning to generate denser graph partitions. Importantly, these three methods make no assumptions about the targeted GNN models, addressing the challenge of model variety. We implement these methods in a framework called SwitchBlade, consisting of a compiler, a graph partitioner, and a hardware accelerator. Our evaluation demonstrates that SwitchBlade achieves an average speedup of $1.85\times$ and energy savings of $19.03\times$ compared to the NVIDIA V100 GPU. Additionally, SwitchBlade delivers performance comparable to state-of-the-art specialized accelerators

Thieno [2, 3-d] pyrimidine inhibits gastric cancer cell proliferation via the down-regulation of bcl-2 and survivin expressions

Purpose: To investigate the effect of thieno [2, 3-d] pyrimidine on gastric cancer (GC) cell proliferation, and elucidate the mechanism of action involved. Methods: Human GC cells (MKN1, MKN28 and SGC 7901) were cultured in RPMI-1640 medium supplemented with 10 % fetal bovine serum (FBS) and 1 % penicillin/ streptomycin solution at 37 °C for 24 h in a humidified atmosphere of 5 % CO2 and 95 % air. After attaining 60 - 70 % confluency, the cells were treated with serum-free medium and graded concentrations of thieno [2, 3-d] pyrimidine (0 – 12 µM) for 24 h. Normal cell culture without thieno [2, 3-d] pyrimidine served as control group. The cells were used in logarithmic growth phase. Cell viability and apoptosis were assessed using 3 (4,5 dimethyl thiazol 2 yl) 2,5 diphenyl 2H tetrazolium bromide (MTT), and flow cytometric assays, respectively. The levels of expression of ZNF139, B cell lymphoma 2 (bcl-2) and survivin in MKN1 cells and orthotopically transplanted mice were determined using Western blotting and real-time quantitative polymerase chain reaction (qRT-PCR). Results: Treatment of MKN1, MKN28 and SGC 7901 cells with thieno [2, 3-d] pyrimidine for 72 h led to significant and dose-dependent reductions in their viabilities, as well as significant and dose-dependent increases in the number of apoptotic cells (p < 0.05). The results of qRT-PCR and Western blotting showed that ZNF139 mRNA and protein expressions in MKN1 cells were significantly down-regulated by thieno [2, 3-d] pyrimidine treatment (p < 0.05). Thieno [2, 3-d] pyrimidine treatment significantly and dose-dependently down-regulated the expressions of bcl 2 and survivin proteins in MKN1 cells and orthotopically transplanted mice (p < 0.05). It also significantly and dose-dependently inhibited the proliferation of GC cells in orthotopic mouse model of GC after 31 days of treatment (p < 0.05). Conclusion: These results suggest that thieno [2, 3-d] pyrimidine suppresses the proliferation of GC cells via down-regulation of the expressions of ZNF139, bcl 2 and sur¬vivin. Thus, it has potentials for development for the management of gastric cancer

Discriminative Elastic-Net Regularized Linear Regression

In this paper, we aim at learning compact and discriminative linear regression models. Linear regression has been widely used in different problems. However, most of the existing linear regression methods exploit the conventional zeroone matrix as the regression targets, which greatly narrows the flexibility of the regression model. Another major limitation of theses methods is that the learned projection matrix fails to precisely project the image features to the target space due to their weak discriminative capability. To this end, we present an elastic-net regularized linear regression (ENLR) framework, and develop two robust linear regression models which possess the following special characteristics. First, our methods exploit two particular strategies to enlarge the margins of different classes by relaxing the strict binary targets into a more feasible variable matrix. Second, a robust elastic-net regularization of singular values is introduced to enhance the compactness and effectiveness of the learned projection matrix. Third, the resulting optimization problem of ENLR has a closed-form solution in each iteration, which can be solved efficiently. Finally, rather than directly exploiting the projection matrix for recognition, our methods employ the transformed features as the new discriminate representations to make final image classification. Compared with the traditional linear regression model and some of its variants, our method is much more accurate in image classification. Extensive experiments conducted on publicly available datasets well demonstrate that the proposed framework can outperform the state-of-the-art methods. The MATLAB codes of our methods can be available at http://www.yongxu.org/lunwen.html

The Role of Multiple Re-Entry Tears in Type B Aortic Dissection Progression: A Longitudinal Study Using a Controlled Swine Model

False lumen (FL) expansion often occurs in type B aortic dissection (TBAD) and has been associated with the presence of re-entry tears. This longitudinal study aims to elucidate the role of re-entry tears in the progression of TBAD using a controlled swine model, by assessing aortic hemodynamics through combined imaging and computational modeling

Viewpoint-Aware Loss with Angular Regularization for Person Re-Identification

Although great progress in supervised person re-identification (Re-ID) has been made recently, due to the viewpoint variation of a person, Re-ID remains a massive visual challenge. Most existing viewpoint-based person Re-ID methods project images from each viewpoint into separated and unrelated sub-feature spaces. They only model the identity-level distribution inside an individual viewpoint but ignore the underlying relationship between different viewpoints. To address this problem, we propose a novel approach, called \textit{Viewpoint-Aware Loss with Angular Regularization }(\textbf{VA-reID}). Instead of one subspace for each viewpoint, our method projects the feature from different viewpoints into a unified hypersphere and effectively models the feature distribution on both the identity-level and the viewpoint-level. In addition, rather than modeling different viewpoints as hard labels used for conventional viewpoint classification, we introduce viewpoint-aware adaptive label smoothing regularization (VALSR) that assigns the adaptive soft label to feature representation. VALSR can effectively solve the ambiguity of the viewpoint cluster label assignment. Extensive experiments on the Market1501 and DukeMTMC-reID datasets demonstrated that our method outperforms the state-of-the-art supervised Re-ID methods