Is Robustness the Cost of Accuracy? -- A Comprehensive Study on the Robustness of 18 Deep Image Classification Models

The prediction accuracy has been the long-lasting and sole standard for comparing the performance of different image classification models, including the ImageNet competition. However, recent studies have highlighted the lack of robustness in well-trained deep neural networks to adversarial examples. Visually imperceptible perturbations to natural images can easily be crafted and mislead the image classifiers towards misclassification. To demystify the trade-offs between robustness and accuracy, in this paper we thoroughly benchmark 18 ImageNet models using multiple robustness metrics, including the distortion, success rate and transferability of adversarial examples between 306 pairs of models. Our extensive experimental results reveal several new insights: (1) linear scaling law - the empirical $\ell_2$ and $\ell_\infty$ distortion metrics scale linearly with the logarithm of classification error; (2) model architecture is a more critical factor to robustness than model size, and the disclosed accuracy-robustness Pareto frontier can be used as an evaluation criterion for ImageNet model designers; (3) for a similar network architecture, increasing network depth slightly improves robustness in $\ell_\infty$ distortion; (4) there exist models (in VGG family) that exhibit high adversarial transferability, while most adversarial examples crafted from one model can only be transferred within the same family. Experiment code is publicly available at \url{https://github.com/huanzhang12/Adversarial_Survey}.Comment: Accepted by the European Conference on Computer Vision (ECCV) 201

Sentiment Analysis of Tourism Reviews: An exploratory study based on CNNs built on LSTM model

This study is to develop a sentiment analysis system for customers’ review on a scenic site. It is based on Convolutional Neural Networks (CNNs) built on Long Short-Term Memory (LSTM) models for text feature extraction under a deep learning framework. The CNNs built on LSTM models applies convolutional filters of CNNs repeatedly operate on the output matrix of LSTM to obtain robust text feature vector. In this study, the optimal parameter configurations for each component of CNNs and LSTM are given individually in the first place. Then, the entire optimal parameter configuration for the integration recognition frame of the system is identified around the optimum of each component. The results demonstrate that, by employing such a method, the accuracy for sentiment analysis with CNNs built on LSTM model, compared with a single CNNs or LSTM model, is improved by 3.13% and 1.71% respectively

The Research on Operation of Obstructed Total Anomalous Pulmonary Venous Connection in Neonates

Objectives. Total anomalous pulmonary venous connection (TAPVC) is a rare congenital heart disease. This study aimed to evaluate the outcomes of TAPVC repair in neonates, controlling for anatomic subtypes and surgical techniques. Methods. Between 1997 and 2013, 88 patients (median age: 16 days) underwent repair for supracardiac (31), cardiac (18), infracardiac (36), or mixed (3) TAPVC. All the patients underwent emergency operation due to obstructed drainage. Supracardiac and infracardiac TAPVC repair included a side-to-side anastomosis between the pulmonary venous confluence and left atrium. Coronary sinus unroofing was preferred for cardiac TAPVC repair. Results. The early mortality rate was 2.3% (2/88 patients). The echocardiogram showed no obstruction in the pulmonary vein anastomosis, and flow rate was 1.1–1.42 m/s in the 3-year follow-up period. Conclusions. The accurate preoperative diagnosis, improved protection of heart function, use of pulmonary vein tissue to anastomose and avoid damage of the pulmonary vein, and delayed sternum closure can reduce the risk of mortality. The preoperative severity of pulmonary vein obstruction, the timing of the emergency operation, and infracardiac or mixed-type TAPVC can affect prognosis. Using our surgical technique, the TAPVC mortality among our patients was gradually reduced with remarkable results. However, careful monitoring of the patient with pulmonary vein restenosis and the timing and method of reoperation should also be given importance

Fast Consensus of Networked Multiagent Systems with Two-Hop Network

This paper studies the consensus convergence speed of multiagent systems (MASs) from two aspects including communication topology and the state of agents. Two-hop network is considered in the communication topology. A novel consensus protocol that includes the information of the states motions and their integrals is introduced. And the protocol has much faster convergence speed by choosing some appropriate weight values. The protocol can be applied to distributed control and large-scale systems. A numerical example is presented to illustrate the effectiveness and superiority of the proposed method

Knowledge-Rich Self-Supervision for Biomedical Entity Linking

Entity linking faces significant challenges such as prolific variations and prevalent ambiguities, especially in high-value domains with myriad entities. Standard classification approaches suffer from the annotation bottleneck and cannot effectively handle unseen entities. Zero-shot entity linking has emerged as a promising direction for generalizing to new entities, but it still requires example gold entity mentions during training and canonical descriptions for all entities, both of which are rarely available outside of Wikipedia. In this paper, we explore Knowledge-RIch Self-Supervision ($\tt KRISS$) for biomedical entity linking, by leveraging readily available domain knowledge. In training, it generates self-supervised mention examples on unlabeled text using a domain ontology and trains a contextual encoder using contrastive learning. For inference, it samples self-supervised mentions as prototypes for each entity and conducts linking by mapping the test mention to the most similar prototype. Our approach can easily incorporate entity descriptions and gold mention labels if available. We conducted extensive experiments on seven standard datasets spanning biomedical literature and clinical notes. Without using any labeled information, our method produces $\tt KRISSBERT$, a universal entity linker for four million UMLS entities that attains new state of the art, outperforming prior self-supervised methods by as much as 20 absolute points in accuracy