On Identifying Disaster-Related Tweets: Matching-based or Learning-based?

Social media such as tweets are emerging as platforms contributing to situational awareness during disasters. Information shared on Twitter by both affected population (e.g., requesting assistance, warning) and those outside the impact zone (e.g., providing assistance) would help first responders, decision makers, and the public to understand the situation first-hand. Effective use of such information requires timely selection and analysis of tweets that are relevant to a particular disaster. Even though abundant tweets are promising as a data source, it is challenging to automatically identify relevant messages since tweet are short and unstructured, resulting to unsatisfactory classification performance of conventional learning-based approaches. Thus, we propose a simple yet effective algorithm to identify relevant messages based on matching keywords and hashtags, and provide a comparison between matching-based and learning-based approaches. To evaluate the two approaches, we put them into a framework specifically proposed for analyzing disaster-related tweets. Analysis results on eleven datasets with various disaster types show that our technique provides relevant tweets of higher quality and more interpretable results of sentiment analysis tasks when compared to learning approach

Shepherding Slots to Objects: Towards Stable and Robust Object-Centric Learning

Object-centric learning (OCL) aspires general and compositional understanding of scenes by representing a scene as a collection of object-centric representations. OCL has also been extended to multi-view image and video datasets to apply various data-driven inductive biases by utilizing geometric or temporal information in the multi-image data. Single-view images carry less information about how to disentangle a given scene than videos or multi-view images do. Hence, owing to the difficulty of applying inductive biases, OCL for single-view images remains challenging, resulting in inconsistent learning of object-centric representation. To this end, we introduce a novel OCL framework for single-view images, SLot Attention via SHepherding (SLASH), which consists of two simple-yet-effective modules on top of Slot Attention. The new modules, Attention Refining Kernel (ARK) and Intermediate Point Predictor and Encoder (IPPE), respectively, prevent slots from being distracted by the background noise and indicate locations for slots to focus on to facilitate learning of object-centric representation. We also propose a weak semi-supervision approach for OCL, whilst our proposed framework can be used without any assistant annotation during the inference. Experiments show that our proposed method enables consistent learning of object-centric representation and achieves strong performance across four datasets. Code is available at \url{https://github.com/object-understanding/SLASH}

A Review on the Applications of Crowdsourcing in Human Pathology

The advent of the digital pathology has introduced new avenues of diagnostic medicine. Among them, crowdsourcing has attracted researchers' attention in the recent years, allowing them to engage thousands of untrained individuals in research and diagnosis. While there exist several articles in this regard, prior works have not collectively documented them. We, therefore, aim to review the applications of crowdsourcing in human pathology in a semi-systematic manner. We firstly, introduce a novel method to do a systematic search of the literature. Utilizing this method, we, then, collect hundreds of articles and screen them against a pre-defined set of criteria. Furthermore, we crowdsource part of the screening process, to examine another potential application of crowdsourcing. Finally, we review the selected articles and characterize the prior uses of crowdsourcing in pathology

New-type of Multi-purpose Standard Radon Chamber in South Korea

Radon is an inert and a radioactive gas which is colorless, tasteless and odorless. As the radon decay proceeds, and if DNA damage continues beyond repair capacity of cells in the human body, it can cause severe health problems such as lung cancer in the long-term. There is a tendency that those countries where legal restriction on radon is strict, various studies related to radon are under way. In South Korea, radon has been regulated under recommendation level. Even though there are about 3 standard radon chambers in Korea, they have not been in an active use because of lack of demand. Also, most of them are specialized in calibration of radon detectors only. Recently, Korean government started giving some attention to radon issue and supporting radon research fields. Thus, this study was carried out to develop a new type of radon chamber for multi-purpose such as 1) radon emission rate from natural and artificial radon sources; 2) calibration of radon detectors; 3) evaluation of radon mitigation efficiency. Keywords: Radon, Radon Chamber, Indoor Air Quality, Chamber Desig

PG-RCNN: Semantic Surface Point Generation for 3D Object Detection

One of the main challenges in LiDAR-based 3D object detection is that the sensors often fail to capture the complete spatial information about the objects due to long distance and occlusion. Two-stage detectors with point cloud completion approaches tackle this problem by adding more points to the regions of interest (RoIs) with a pre-trained network. However, these methods generate dense point clouds of objects for all region proposals, assuming that objects always exist in the RoIs. This leads to the indiscriminate point generation for incorrect proposals as well. Motivated by this, we propose Point Generation R-CNN (PG-RCNN), a novel end-to-end detector that generates semantic surface points of foreground objects for accurate detection. Our method uses a jointly trained RoI point generation module to process the contextual information of RoIs and estimate the complete shape and displacement of foreground objects. For every generated point, PG-RCNN assigns a semantic feature that indicates the estimated foreground probability. Extensive experiments show that the point clouds generated by our method provide geometrically and semantically rich information for refining false positive and misaligned proposals. PG-RCNN achieves competitive performance on the KITTI benchmark, with significantly fewer parameters than state-of-the-art models. The code is available at https://github.com/quotation2520/PG-RCNN.Comment: Accepted by ICCV 202

Elastic seismic design of steel high-rise buildings in regions of strong wind and moderate seismicity

Lateral loading due to wind or earthquake is a major factor that affects the design of high-rise buildings. This paper highlights the problems associated with the seismic design of high-rise buildings in regions of strong wind and moderate seismicity. Seismic response analysis and performance evaluation were conducted for wind-designed concentrically braced steel high-rise buildings in order to check the feasibility of designing them per elastic seismic design criterion (or strength and stiffness solution) in such regions. Review of wind design and pushover analysis results indicated that wind-designed high-rise buildings possess significantly increased elastic seismic capacity due to the overstrength resulting from the wind serviceability criterion. The strength demand-to-capacity study showed that, due to the wind design overstrength, high-rise buildings with a slenderness ratio of larger than four or five can elastically withstand even the maximum considered earthquake (MCE) with the seismic performance level of immediate occupancy under the limited conditions of this study. A step-by-step seismic design procedure per the elastic criterion that is directly usable for practicing design engineers is also recommended.Financial support to this study provided by the Ministry of Construction and Trnasportation of Korea (03 R&D C04-01) is gratefully acknowledged