Copy-Pasting Coherent Depth Regions Improves Contrastive Learning for Urban-Scene Segmentation

In this work, we leverage estimated depth to boost self-supervised contrastive learning for segmentation of urban scenes, where unlabeled videos are readily available for training self-supervised depth estimation. We argue that the semantics of a coherent group of pixels in 3D space is self-contained and invariant to the contexts in which they appear. We group coherent, semantically related pixels into coherent depth regions given their estimated depth and use copy-paste to synthetically vary their contexts. In this way, cross-context correspondences are built in contrastive learning and a context-invariant representation is learned. For unsupervised semantic segmentation of urban scenes, our method surpasses the previous state-of-the-art baseline by +7.14% in mIoU on Cityscapes and +6.65% on KITTI. For fine-tuning on Cityscapes and KITTI segmentation, our method is competitive with existing models, yet, we do not need to pre-train on ImageNet or COCO, and we are also more computationally efficient. Our code is available on https://github.com/LeungTsang/CPCDRComment: BMVC 2022 Best Student Paper Award(Honourable Mention

Continual Evolution: The Experience Over Three Semesters of a Librarian Embedded in an Online Evidence-Based Medicine Course for Physician Assistant Students

This column examines the experience, over three years, of a librarian embedded in an online Epidemiology and Evidence-based Medicine course, which is a requirement for students pursing a Master of Science in Physician Assistant Studies at Pace University. Student learning outcomes were determined, a video lecture was created, and student learning was assessed via a five-point test during year one. For years two and three, the course instructor asked the librarian to be responsible for two weeks of the course instruction and a total of 15 out of 100 possible points for the course. This gave the librarian flexibility to measure additional outcomes and gather more in-depth assessment data. The librarian then used the assessment data to target areas for improvement in the lessons and Blackboard tests. Revisions made by the librarian positively affected student achievement of learning outcomes, as measured by the assessment conducted the subsequent semester. Plans for further changes are also discussed

Improving Crowded Object Detection via Copy-Paste

Crowdedness caused by overlapping among similar objects is a ubiquitous challenge in the field of 2D visual object detection. In this paper, we first underline two main effects of the crowdedness issue: 1) IoU-confidence correlation disturbances (ICD) and 2) confused de-duplication (CDD). Then we explore a pathway of cracking these nuts from the perspective of data augmentation. Primarily, a particular copy-paste scheme is proposed towards making crowded scenes. Based on this operation, we first design a "consensus learning" method to further resist the ICD problem and then find out the pasting process naturally reveals a pseudo "depth" of object in the scene, which can be potentially used for alleviating CDD dilemma. Both methods are derived from magical using of the copy-pasting without extra cost for hand-labeling. Experiments show that our approach can easily improve the state-of-the-art detector in typical crowded detection task by more than 2% without any bells and whistles. Moreover, this work can outperform existing data augmentation strategies in crowded scenario.Comment: Accepted by AAAI202

Automated Soil and Air Temperature Monitoring Protocol

The purpose of this resource is to continuously measure soil and air temperature at one site. Students install four temperature probes; three are placed in the soil at three different depths and one is placed in an instrument shelter. Students use a data logger to record readings from the probes every 15 minutes. Students transfer the data to their school computers for analysis and submission to the GLOBE database. Educational levels: Primary elementary, Intermediate elementary, Middle school, High school

DSGN++: Exploiting Visual-Spatial Relation for Stereo-based 3D Detectors

Camera-based 3D object detectors are welcome due to their wider deployment and lower price than LiDAR sensors. We revisit the prior stereo modeling DSGN about the stereo volume constructions for representing both 3D geometry and semantics. We polish the stereo modeling and propose our approach, DSGN++, aiming for improving information flow throughout the 2D-to-3D pipeline in the following three main aspects. First, to effectively lift the 2D information to stereo volume, we propose depth-wise plane sweeping (DPS) that allows denser connections and extracts depth-guided features. Second, for better grasping differently spaced features, we present a novel stereo volume -- Dual-view Stereo Volume (DSV) that integrates front-view and top-view features and reconstructs sub-voxel depth in the camera frustum. Third, as the foreground region becomes less dominant in 3D space, we firstly propose a multi-modal data editing strategy -- Stereo-LiDAR Copy-Paste, which ensures cross-modal alignment and improves data efficiency. Without bells and whistles, extensive experiments in various modality setups on the popular KITTI benchmark show that our method consistently outperforms other camera-based 3D detectors for all categories. Code will be released at https://github.com/chenyilun95/DSGN2

The Game as Structure: Exploring Gendered Identities, Interactions and Macrostructures in the System of Sex Trafficking

Human trafficking, or trafficking in persons (TIP), is a crime where people profit from the exploitation of others through some form of labor (Polaris, 2015). In the U.S., the three most common forms of TIP are sex trafficking, domestic trafficking and agricultural trafficking (Human Rights Center, 2007). This study specifically focuses on the system of domestic sex trafficking and uses gender theory to explain its perpetuation. In order to explore how individuals’ gendered identities affect sex trafficking, and to explore how these identities affect the perpetuation of this crime, four survivors of sex trafficking and one law enforcement official were interviewed. Based on the interviews, it is evident that gendered identities and individuals’ gendered interactions greatly affect the internalities of sex trafficking. Gender as a multi-leveled structure that affects human behavior was evident in all narratives, which ultimately shed light on how this industry is perpetuated. Additionally, all of the participants reported having been affected by the power structures created by masculine identities in sex trafficking, which indicates that hegemonic masculinity is at play with regards to this industry. These masculine power structures fit well within the multi-leveled gender model, and they show how this model within sex trafficking is controlled by masculine identities. The narratives also provided insight into other unexpected phenomena within sex trafficking that are affected by gender, such as evidence of hegemonic masculinity within the anti-trafficking movement, and how traffickers employ capitalist ideals within this system to control women

Simple data analysis for biologists

This document provides a simple introduction to research methods and analysis tools for biologists or environmental scientists, with particular emphasis on fish biology in devleoping countries