Flexible Network Binarization with Layer-wise Priority

How to effectively approximate real-valued parameters with binary codes plays a central role in neural network binarization. In this work, we reveal an important fact that binarizing different layers has a widely-varied effect on the compression ratio of network and the loss of performance. Based on this fact, we propose a novel and flexible neural network binarization method by introducing the concept of layer-wise priority which binarizes parameters in inverse order of their layer depth. In each training step, our method selects a specific network layer, minimizes the discrepancy between the original real-valued weights and its binary approximations, and fine-tunes the whole network accordingly. During the iteration of the above process, it is significant that we can flexibly decide whether to binarize the remaining floating layers or not and explore a trade-off between the loss of performance and the compression ratio of model. The resulting binary network is applied for efficient pedestrian detection. Extensive experimental results on several benchmarks show that under the same compression ratio, our method achieves much lower miss rate and faster detection speed than the state-of-the-art neural network binarization method.Comment: More experiments on image classification are planne

Proactive Resilience Building through Route Diversity: A Close Look at the Metro System from the Travelers’ Perspective

Travel demand plays a moderate role in the resilience impact assessment of public transport network disruptions. We analyze how travelers can proactively build transport resilience by responding to adverse events using alternative routes. We consider route diversity (i.e., the numbers of alternative routes for all origin–destination (OD) pairs) as a measure of the network’s capability to accommodate route choice behavioral change and look for potential proactive travelers from the spatial distribution of OD pairs with alternative routes in the Beijing subway network. We further investigate how proactive resilience can be built by choosing alternative routes with the least extra time cost

Relational Learning between Multiple Pulmonary Nodules via Deep Set Attention Transformers

Diagnosis and treatment of multiple pulmonary nodules are clinically important but challenging. Prior studies on nodule characterization use solitary-nodule approaches on multiple nodular patients, which ignores the relations between nodules. In this study, we propose a multiple instance learning (MIL) approach and empirically prove the benefit to learn the relations between multiple nodules. By treating the multiple nodules from a same patient as a whole, critical relational information between solitary-nodule voxels is extracted. To our knowledge, it is the first study to learn the relations between multiple pulmonary nodules. Inspired by recent advances in natural language processing (NLP) domain, we introduce a self-attention transformer equipped with 3D CNN, named {NoduleSAT}, to replace typical pooling-based aggregation in multiple instance learning. Extensive experiments on lung nodule false positive reduction on LUNA16 database, and malignancy classification on LIDC-IDRI database, validate the effectiveness of the proposed method.Comment: 2020 IEEE 17th International Symposium on Biomedical Imaging (ISBI 2020

Probabilistic Radiomics: Ambiguous Diagnosis with Controllable Shape Analysis

Radiomics analysis has achieved great success in recent years. However, conventional Radiomics analysis suffers from insufficiently expressive hand-crafted features. Recently, emerging deep learning techniques, e.g., convolutional neural networks (CNNs), dominate recent research in Computer-Aided Diagnosis (CADx). Unfortunately, as black-box predictors, we argue that CNNs are "diagnosing" voxels (or pixels), rather than lesions; in other words, visual saliency from a trained CNN is not necessarily concentrated on the lesions. On the other hand, classification in clinical applications suffers from inherent ambiguities: radiologists may produce diverse diagnosis on challenging cases. To this end, we propose a controllable and explainable {\em Probabilistic Radiomics} framework, by combining the Radiomics analysis and probabilistic deep learning. In our framework, 3D CNN feature is extracted upon lesion region only, then encoded into lesion representation, by a controllable Non-local Shape Analysis Module (NSAM) based on self-attention. Inspired from variational auto-encoders (VAEs), an Ambiguity PriorNet is used to approximate the ambiguity distribution over human experts. The final diagnosis is obtained by combining the ambiguity prior sample and lesion representation, and the whole network named $DenseSharp^{+}$ is end-to-end trainable. We apply the proposed method on lung nodule diagnosis on LIDC-IDRI database to validate its effectiveness.Comment: MICCAI 2019 (early accept), with supplementary material

Topological surface electronic states in candidate nodal-line semimetal CaAgAs

We investigate systematically the bulk and surface electronic structure of the candidate nodal-line semimetal CaAgAs by angle resolved photoemission spectroscopy and density functional calculations. We observed a metallic, linear, non-$k_z$-dispersive surface band that coincides with the high-binding-energy part of the theoretical topological surface state, proving the topological nontriviality of the system. An overall downshift of the experimental Fermi level points to a rigid-band-like $p$-doping of the samples, due possibly to Ag vacancies in the as-grown crystals.Comment: 6 pages, 5 figure

Disentangled Deep Autoencoding Regularization for Robust Image Classification

In spite of achieving revolutionary successes in machine learning, deep convolutional neural networks have been recently found to be vulnerable to adversarial attacks and difficult to generalize to novel test images with reasonably large geometric transformations. Inspired by a recent neuroscience discovery revealing that primate brain employs disentangled shape and appearance representations for object recognition, we propose a general disentangled deep autoencoding regularization framework that can be easily applied to any deep embedding based classification model for improving the robustness of deep neural networks. Our framework effectively learns disentangled appearance code and geometric code for robust image classification, which is the first disentangling based method defending against adversarial attacks and complementary to standard defense methods. Extensive experiments on several benchmark datasets show that, our proposed regularization framework leveraging disentangled embedding significantly outperforms traditional unregularized convolutional neural networks for image classification on robustness against adversarial attacks and generalization to novel test data.Comment: 9 page