Seismic pounding between adjacent buildings of unequal floor height

When the story heights of adjacent buildings are unequal, the inter-floor pounding maybe happen during earthquake. Employing substructures in pounding area, the analytical model of adjacent structures with unequal story height is developed, and the equations of motion considering pounding are derived. Based on analytical model, the inter-floor pounding responses of adjacent buildings with unequal story height are investigated. The corresponding parametrical studies are conducted and influence rules are concluded. The results show that the influences of inter-floor pounding in adjacent buildings on main structures are smaller than those of floor pounding. But the damages on pounding area are quite large. Moreover, the period ratio of structures, the initial gap and the pounding location have remarkable influence on responses of inter-floor pounding

Visual Commonsense R-CNN

We present a novel unsupervised feature representation learning method, Visual Commonsense Region-based Convolutional Neural Network (VC R-CNN), to serve as an improved visual region encoder for high-level tasks such as captioning and VQA. Given a set of detected object regions in an image (e.g., using Faster R-CNN), like any other unsupervised feature learning methods (e.g., word2vec), the proxy training objective of VC R-CNN is to predict the contextual objects of a region. However, they are fundamentally different: the prediction of VC R-CNN is by using causal intervention: P(Y|do(X)), while others are by using the conventional likelihood: P(Y|X). This is also the core reason why VC R-CNN can learn "sense-making" knowledge like chair can be sat -- while not just "common" co-occurrences such as chair is likely to exist if table is observed. We extensively apply VC R-CNN features in prevailing models of three popular tasks: Image Captioning, VQA, and VCR, and observe consistent performance boosts across them, achieving many new state-of-the-arts. Code and feature are available at https://github.com/Wangt-CN/VC-R-CNN.Comment: Accepted by CVPR 202

Management of Symptomatic Sacral Perineural Cysts

BACKGROUND: There has been no consensus on the optimal treatment of symptomatic sacral perineural cysts. Most previous reports concerning the management methods were either sporadic case reports or a series of limited cases. This study is to further optimize the management for patients with symptomatic sacral perineural cysts by analyzing the outcomes of a cohort of patients who were treated with different strategies. METHODS AND FINDINGS: We reviewed the outcomes of 15 patients with symptomatic sacral perineural cysts who were managed by three different modalities from 1998 through 2010. Six patients underwent microsurgical cyst fenestration and cyst wall imbrication. Seven patients underwent a modified surgical procedure, during which the cerebrospinal fluid leak aperture was located and repaired. Two patients were treated with medication and physical therapy. Outcomes of the patients were assessed by following up (13 months to 10 years). All of the six patients treated with microsurgical cyst fenestration and cyst wall imbrication experienced complete or substantial relief of their preoperative symptoms. However, the symptoms of one patient reappeared eight months after the operation. Another patient experienced a postoperative cerebrospinal fluid leakage. Six of the seven patients treated with the modified surgical operation experienced complete or substantial resolution of their preoperative symptoms, with only one patient who experienced temporary worsening of his preoperative urine incontinence, which disappeared gradually one month later. No new postoperative neurological deficits, no cerebrospinal fluid leaks and no recurrence were observed in the seven patients. The symptoms of the two patients treated with conservative measures aggravated with time. CONCLUSIONS: Microsurgical operation should be a treatment consideration in patients with symptomatic sacral perineural cysts. Furthermore, the surgical procedure with partial cyst removal and aperture repair for prevention of cerebrospinal fluid leakage seemed to be more simple and effective

Attention-based Class Activation Diffusion for Weakly-Supervised Semantic Segmentation

Extracting class activation maps (CAM) is a key step for weakly-supervised semantic segmentation (WSSS). The CAM of convolution neural networks fails to capture long-range feature dependency on the image and result in the coverage on only foreground object parts, i.e., a lot of false negatives. An intuitive solution is ``coupling'' the CAM with the long-range attention matrix of visual transformers (ViT) We find that the direct ``coupling'', e.g., pixel-wise multiplication of attention and activation, achieves a more global coverage (on the foreground), but unfortunately goes with a great increase of false positives, i.e., background pixels are mistakenly included. This paper aims to tackle this issue. It proposes a new method to couple CAM and Attention matrix in a probabilistic Diffusion way, and dub it AD-CAM. Intuitively, it integrates ViT attention and CAM activation in a conservative and convincing way. Conservative is achieved by refining the attention between a pair of pixels based on their respective attentions to common neighbors, where the intuition is two pixels having very different neighborhoods are rarely dependent, i.e., their attention should be reduced. Convincing is achieved by diffusing a pixel's activation to its neighbors (on the CAM) in proportion to the corresponding attentions (on the AM). In experiments, our results on two challenging WSSS benchmarks PASCAL VOC and MS~COCO show that AD-CAM as pseudo labels can yield stronger WSSS models than the state-of-the-art variants of CAM

NPA: Neural News Recommendation with Personalized Attention

News recommendation is very important to help users find interested news and alleviate information overload. Different users usually have different interests and the same user may have various interests. Thus, different users may click the same news article with attention on different aspects. In this paper, we propose a neural news recommendation model with personalized attention (NPA). The core of our approach is a news representation model and a user representation model. In the news representation model we use a CNN network to learn hidden representations of news articles based on their titles. In the user representation model we learn the representations of users based on the representations of their clicked news articles. Since different words and different news articles may have different informativeness for representing news and users, we propose to apply both word- and news-level attention mechanism to help our model attend to important words and news articles. In addition, the same news article and the same word may have different informativeness for different users. Thus, we propose a personalized attention network which exploits the embedding of user ID to generate the query vector for the word- and news-level attentions. Extensive experiments are conducted on a real-world news recommendation dataset collected from MSN news, and the results validate the effectiveness of our approach on news recommendation