Strengthening of Rural Bridges using Rapid-Installation FRP Technology: Route 63 Bridge No. H356, Phelps County

This report presents the use of externally bonded fiber reinforced polymers (FRP) laminates for the flexural strengthening of a concrete bridge. The bridge selected for this project is a two-span simply supported reinforced concrete slab with no transverse steel reinforcement located in Phelps County, MO. The original construction combined with the presence of very rigid parapets caused the formation of a 1-inch wide longitudinal crack, which resulted in the slab to behave as two separate elements. The structural behavior was verified using a finite element model (FEM) of the bridge. The bridge analysis was performed for maximum loads determined in accordance with AASHTO 4th edition. The strengthening scheme was designed in compliance with the ACI 440.2R-08 design guide for externally bonded FRP materials, to avoid further cracking and such that the transverse flexural capacity be higher than the cracking moment. The FRP strengthening technique was rapidly implemented. After the strengthening, a load test was performed to validate the bridge model and evaluate the structural behavior according to the AASHTO specifications. The bridge deck was retrofitted after the longitudinal crack was injected with epoxy to allow continuity in the cross section

A Novel Counterfactual Data Augmentation Method for Aspect-Based Sentiment Analysis

Aspect-based-sentiment-analysis (ABSA) is a fine-grained sentiment evaluation task, which analyzes the emotional polarity of the evaluation aspects. Generally, the emotional polarity of an aspect exists in the corresponding opinion expression, whose diversity has great impact on model's performance. To mitigate this problem, we propose a novel and simple counterfactual data augmentation method to generate opinion expressions with reversed sentiment polarity. In particular, the integrated gradients are calculated to locate and mask the opinion expression. Then, a prompt combined with the reverse expression polarity is added to the original text, and a Pre-trained language model (PLM), T5, is finally was employed to predict the masks. The experimental results shows the proposed counterfactual data augmentation method performs better than current augmentation methods on three ABSA datasets, i.e. Laptop, Restaurant, and MAMS.Comment: Camera-ready for ACML 202

rDNA internal transcribed spacer sequence analysis of Lycoris Hert.

The interspecific relationships of Lycoris species were studied by internal transcribed spacer (ITS) sequences. ITS fragments of 14 species were amplified, sequenced and analysed. The results showed that ITS sequences of 14 species were different from each other and the ITS lengths of 14 species were about 652 bp. The GC content of ITS2 sequences was bigger than that of ITS1. Clustering results based on ITS sequences showed that Lycoris species could be divided into three clades. The classification was basically consistent with those of karyotype and morphology. This paper suggested that the likelihood of hybrid origin of Lycoris species was supported and ITS could be used as a good molecular marker to identify plants of Lycoris.Keywords: Lycoris Hert., internal transcribed spacer (ITS), molecular taxonomy, interspecific relationshipAfrican Journal of Biotechnology Vol. 11(29), pp. 7361-7365, 10 April, 201

OnlineRefer: A Simple Online Baseline for Referring Video Object Segmentation

Referring video object segmentation (RVOS) aims at segmenting an object in a video following human instruction. Current state-of-the-art methods fall into an offline pattern, in which each clip independently interacts with text embedding for cross-modal understanding. They usually present that the offline pattern is necessary for RVOS, yet model limited temporal association within each clip. In this work, we break up the previous offline belief and propose a simple yet effective online model using explicit query propagation, named OnlineRefer. Specifically, our approach leverages target cues that gather semantic information and position prior to improve the accuracy and ease of referring predictions for the current frame. Furthermore, we generalize our online model into a semi-online framework to be compatible with video-based backbones. To show the effectiveness of our method, we evaluate it on four benchmarks, \ie, Refer-Youtube-VOS, Refer-DAVIS17, A2D-Sentences, and JHMDB-Sentences. Without bells and whistles, our OnlineRefer with a Swin-L backbone achieves 63.5 J&F and 64.8 J&F on Refer-Youtube-VOS and Refer-DAVIS17, outperforming all other offline methods.Comment: Accepted by ICCV2023. The code is at https://github.com/wudongming97/OnlineRefe

Referring Multi-Object Tracking

Existing referring understanding tasks tend to involve the detection of a single text-referred object. In this paper, we propose a new and general referring understanding task, termed referring multi-object tracking (RMOT). Its core idea is to employ a language expression as a semantic cue to guide the prediction of multi-object tracking. To the best of our knowledge, it is the first work to achieve an arbitrary number of referent object predictions in videos. To push forward RMOT, we construct one benchmark with scalable expressions based on KITTI, named Refer-KITTI. Specifically, it provides 18 videos with 818 expressions, and each expression in a video is annotated with an average of 10.7 objects. Further, we develop a transformer-based architecture TransRMOT to tackle the new task in an online manner, which achieves impressive detection performance and outperforms other counterparts. The dataset and code will be available at https://github.com/wudongming97/RMOT.Comment: Accpeted by CVPR 2023. The dataset and code will be available at https://github.com/wudongming97/RMO

Language Prompt for Autonomous Driving

A new trend in the computer vision community is to capture objects of interest following flexible human command represented by a natural language prompt. However, the progress of using language prompts in driving scenarios is stuck in a bottleneck due to the scarcity of paired prompt-instance data. To address this challenge, we propose the first object-centric language prompt set for driving scenes within 3D, multi-view, and multi-frame space, named NuPrompt. It expands Nuscenes dataset by constructing a total of 35,367 language descriptions, each referring to an average of 5.3 object tracks. Based on the object-text pairs from the new benchmark, we formulate a new prompt-based driving task, \ie, employing a language prompt to predict the described object trajectory across views and frames. Furthermore, we provide a simple end-to-end baseline model based on Transformer, named PromptTrack. Experiments show that our PromptTrack achieves impressive performance on NuPrompt. We hope this work can provide more new insights for the autonomous driving community. Dataset and Code will be made public at \href{https://github.com/wudongming97/Prompt4Driving}{https://github.com/wudongming97/Prompt4Driving}