Evaluation of Performance of Bus Lanes on Urban Expressway Using Paramics Micro-simulation Model

AbstractUrban expressway, as the main skeleton of the road network, is the aorta between urban regions and urban external traffic communication, but also bears the commuter channel. It makes a large amount of traffic flow into the expressway, resulting the congestion in the expressway in many big cities, including Beijing. Taking the Beijing southwest third ring expressway for example, a simulation model was built using Paramics. The simulation model was pre-evaluated before and after the bus lanes set, and the model was post-evaluated to verify the validity of the model after the bus lanes were implemented, it has important theoretical and practical value

Social Media Would Not Lie: Prediction of the 2016 Taiwan Election via Online Heterogeneous Data

The prevalence of online media has attracted researchers from various domains to explore human behavior and make interesting predictions. In this research, we leverage heterogeneous social media data collected from various online platforms to predict Taiwan's 2016 presidential election. In contrast to most existing research, we take a "signal" view of heterogeneous information and adopt the Kalman filter to fuse multiple signals into daily vote predictions for the candidates. We also consider events that influenced the election in a quantitative manner based on the so-called event study model that originated in the field of financial research. We obtained the following interesting findings. First, public opinions in online media dominate traditional polls in Taiwan election prediction in terms of both predictive power and timeliness. But offline polls can still function on alleviating the sample bias of online opinions. Second, although online signals converge as election day approaches, the simple Facebook "Like" is consistently the strongest indicator of the election result. Third, most influential events have a strong connection to cross-strait relations, and the Chou Tzu-yu flag incident followed by the apology video one day before the election increased the vote share of Tsai Ing-Wen by 3.66%. This research justifies the predictive power of online media in politics and the advantages of information fusion. The combined use of the Kalman filter and the event study method contributes to the data-driven political analytics paradigm for both prediction and attribution purposes

DCANet: Dual Convolutional Neural Network with Attention for Image Blind Denoising

Noise removal of images is an essential preprocessing procedure for many computer vision tasks. Currently, many denoising models based on deep neural networks can perform well in removing the noise with known distributions (i.e. the additive Gaussian white noise). However eliminating real noise is still a very challenging task, since real-world noise often does not simply follow one single type of distribution, and the noise may spatially vary. In this paper, we present a new dual convolutional neural network (CNN) with attention for image blind denoising, named as the DCANet. To the best of our knowledge, the proposed DCANet is the first work that integrates both the dual CNN and attention mechanism for image denoising. The DCANet is composed of a noise estimation network, a spatial and channel attention module (SCAM), and a CNN with a dual structure. The noise estimation network is utilized to estimate the spatial distribution and the noise level in an image. The noisy image and its estimated noise are combined as the input of the SCAM, and a dual CNN contains two different branches is designed to learn the complementary features to obtain the denoised image. The experimental results have verified that the proposed DCANet can suppress both synthetic and real noise effectively. The code of DCANet is available at https://github.com/WenCongWu/DCANet

Approximated Prompt Tuning for Vision-Language Pre-trained Models

Prompt tuning is a parameter-efficient way to deploy large-scale pre-trained models to downstream tasks by adding task-specific tokens. In terms of vision-language pre-trained (VLP) models, prompt tuning often requires a large number of learnable tokens to bridge the gap between the pre-training and downstream tasks, which greatly exacerbates the already high computational overhead. In this paper, we revisit the principle of prompt tuning for Transformer-based VLP models and reveal that the impact of soft prompt tokens can be actually approximated via independent information diffusion steps, thereby avoiding the expensive global attention modeling and reducing the computational complexity to a large extent. Based on this finding, we propose a novel Approximated Prompt Tuning (APT) approach towards efficient VL transfer learning. To validate APT, we apply it to two representative VLP models, namely ViLT and METER, and conduct extensive experiments on a bunch of downstream tasks. Meanwhile, the generalization of APT is also validated on CLIP for image classification. The experimental results not only show the superior performance gains and computation efficiency of APT against the conventional prompt tuning methods, e.g., +6.6% accuracy and -64.62% additional computation overhead on METER, but also confirm its merits over other parameter-efficient transfer learning approaches

Preload monitoring of bolted L-shaped lap joints using virtual time reversal method

L-shaped bolt lap joints are commonly used in aerospace and civil structures. However, bolt joints are frequently subjected to loosening, and this has a significant effect on the safety and reliability of these structures. Therefore, bolt preload monitoring is very important, especially at the early stage of loosening. In this paper, a virtual time reversal guided wave method is presented to monitor preload of bolted L-shaped lap joints accurately and simply. In this method, a referenced reemitting signal (RRS) is extracted from the bolted structure in fully tightened condition. Then the RRS is utilized as the excitation signal for the bolted structure in loosening states, and the normalized peak amplitude of refocused wave packet is used as the tightness index (TIA). The proposed method is experimentally validated by L-shaped bolt joints with single and multiple bolts. Moreover, the selections of guided wave frequency and tightness index are also discussed. The results demonstrate that the relationship between TIA and bolt preload is linear. The detection sensitivity is improved significantly compared with time reversal (TR) method, particularly when bolt loosening is at its embryo stage. The results also show that TR method is an effective method for detection of the number of loosening bolts

DDX5 facilitates HIV-1 replication as a cellular co-factor of Rev.

HIV-1 Rev plays an important role in the late phase of HIV-1 replication, which facilitates export of unspliced viral mRNAs from the nucleus to cytoplasm in infected cells. Recent studies have shown that DDX1 and DDX3 are co-factors of Rev for the export of HIV-1 transcripts. In this report, we have demonstrated that DDX5 (p68), which is a multifunctional DEAD-box RNA helicase, functions as a new cellular co-factor of HIV-1 Rev. We found that DDX5 affects Rev function through the Rev-RRE axis and subsequently enhances HIV-1 replication. Confocal microscopy and co-immunoprecipitation analysis indicated that DDX5 binds to Rev and this interaction is largely dependent on RNA. If the DEAD-box motif of DDX5 is mutated, DDX5 loses almost all of its ability to bind to Rev, indicating that the DEAD-box motif of DDX5 is required for the interaction between DDX5 and Rev. Our data indicate that interference of DDX5-Rev interaction could reduce HIV-1 replication and potentially provide a new molecular target for anti-HIV-1 therapeutics