Traffic Flow Characteristics and Lane Use Strategies for Connected and Automated Vehicle in Mixed Traffic Conditions

Managed lanes, such as a dedicated lane for connected and automated vehicles (CAVs), can provide not only technological accommodation but also desired market incentives for road users to adopt CAVs in the near future. In this paper, we investigate traffic flow characteristics with two configurations of the managed lane across different market penetration rates and quantify the benefits from the perspectives of lane-level headway distribution, fuel consumption, communication density, and overall network performance. The results highlight the benefits of implementing managed lane strategies for CAVs: 1) a dedicated CAV lane significantly extends the stable region of the speed-flow diagram and yields a greater road capacity. As the result shows, the highest flow rate is 3,400 vehicles per hour per lane at 90% market penetration rate with one CAV lane; 2) the concentration of CAVs in one lane results in a narrower headway distribution (with smaller standard deviation) even with partial market penetration; 3) a dedicated CAV lane is also able to eliminate duel-bell-shape distribution that is caused by the heterogeneous traffic flow; and 4) a dedicated CAV lane creates a more consistent CAV density, which facilitates communication activity and decreases the probability of packet dropping

MAMO: Masked Multimodal Modeling for Fine-Grained Vision-Language Representation Learning

Multimodal representation learning has shown promising improvements on various vision-language tasks. Most existing methods excel at building global-level alignment between vision and language while lacking effective fine-grained image-text interaction. In this paper, we propose a jointly masked multimodal modeling method to learn fine-grained multimodal representations. Our method performs joint masking on image-text input and integrates both implicit and explicit targets for the masked signals to recover. The implicit target provides a unified and debiased objective for vision and language, where the model predicts latent multimodal representations of the unmasked input. The explicit target further enriches the multimodal representations by recovering high-level and semantically meaningful information: momentum visual features of image patches and concepts of word tokens. Through such a masked modeling process, our model not only learns fine-grained multimodal interaction, but also avoids the semantic gap between high-level representations and low- or mid-level prediction targets (e.g. image pixels), thus producing semantically rich multimodal representations that perform well on both zero-shot and fine-tuned settings. Our pre-trained model (named MAMO) achieves state-of-the-art performance on various downstream vision-language tasks, including image-text retrieval, visual question answering, visual reasoning, and weakly-supervised visual grounding

VAST: A Vision-Audio-Subtitle-Text Omni-Modality Foundation Model and Dataset

Vision and text have been fully explored in contemporary video-text foundational models, while other modalities such as audio and subtitles in videos have not received sufficient attention. In this paper, we resort to establish connections between multi-modality video tracks, including Vision, Audio, and Subtitle, and Text by exploring an automatically generated large-scale omni-modality video caption dataset called VAST-27M. Specifically, we first collect 27 million open-domain video clips and separately train a vision and an audio captioner to generate vision and audio captions. Then, we employ an off-the-shelf Large Language Model (LLM) to integrate the generated captions, together with subtitles and instructional prompts into omni-modality captions. Based on the proposed VAST-27M dataset, we train an omni-modality video-text foundational model named VAST, which can perceive and process vision, audio, and subtitle modalities from video, and better support various tasks including vision-text, audio-text, and multi-modal video-text tasks (retrieval, captioning and QA). Extensive experiments have been conducted to demonstrate the effectiveness of our proposed VAST-27M corpus and VAST foundation model. VAST achieves 22 new state-of-the-art results on various cross-modality benchmarks. Code, model and dataset will be released at https://github.com/TXH-mercury/VAST.Comment: 23 pages, 5 figure

Measuring Retiming Responses of Passengers to a Prepeak Discount Fare by Tracing Smart Card Data: A Practical Experiment in the Beijing Subway

Understanding passengers’ responses to fare changes is the basis to design reasonable price policies. This work aims to explore retiming responses of travelers changing departure times due to a prepeak discount pricing strategy in the Beijing subway in China, using smart card records from an automatic fare collection (AFC) system. First, a new set of classification indicators is established to segment passengers through a two-step clustering approach. Then, the potentially influenced passengers for the fare policy are identified, and the shifted passengers who changed their departure time are detected by tracing changes in passengers’ expected departure times before and after the policy. Lastly, the fare elasticity of departure time is defined to measure the retiming responses of passengers. Two scenarios are studied of one month (short term) and six months (middle term) after the policy. The retiming elasticity of different passenger groups, retiming elasticity over time, and retiming elasticity functions of shifted time are measured. The results show that there are considerable differences in the retiming elasticities of different passenger groups; low-frequency passengers are more sensitive to discount fares than high-frequency passengers. The retiming elasticity decreases greatly with increasing shifted time, and 30 minutes is almost the maximum acceptable shifted time for passengers. Moreover, the retiming elasticity of passengers in the middle term is approximately twice that in the short term. Applications of fare optimization are also executed, and the results suggest that optimizing the valid time window of the discount fares is a feasible way to improve the congestion relief effect of the policy, while policy makers should be cautious to change fare structures and increase discounts. Document type: Articl

Role of methyltransferase-like enzyme 3 and methyltransferase-like enzyme 14 in urological cancers

N6-methyladenosine (m6A) modifications can be found in eukaryotic messenger RNA (mRNA), long non-coding RNA (lncRNA), and microRNA (miRNA). Several studies have demonstrated a close relationship between m6A modifications and cancer cells. Methyltransferase-like enzyme 3 (METTL3) and methyltransferase-like enzyme 14 (METTL14) are two major enzymes involved in m6A modifications that play vital roles in various cancers. However, the roles and regulatory mechanisms of METTL3 and METTL14 in urological cancers are largely unknown. In this review, we summarize the current research results for METTL3 and METTL14 and identify potential pathways involving these enzymes in kidney, bladder, prostate, and testicular cancer. We found that METTL3 and METTL14 have different expression patterns in four types of urological cancers. METTL3 is highly expressed in bladder and prostate cancer and plays an oncogenic role on cancer cells; however, its expression and role are opposite in kidney cancer. METTL14 is expressed at low levels in kidney and bladder cancer, where it has a tumor suppressive role. Low METTL3 or METTL14 expression in cancer cells negatively regulates cell growth-related pathways (e.g., mTOR, EMT, and P2XR6) but positively regulates cell death-related pathways (e.g., P53, PTEN, and Notch1). When METTL3 is highly expressed, it positively regulates the NF-kB and SHH-GL1pathways but negatively regulates PTEN. These results suggest that although METTL3 and METTL14 have different expression levels and regulatory mechanisms in urological cancers, they control cancer cell fate via cell growth- and cell death-related pathways. These findings suggest that m6A modification may be a potential new therapeutic target in urological cancer

MIEC-type ceramic membranes for the oxygen separation technology

Mixed ionic-electronic conducting ceramic membrane-based oxygen separation technology attracts great attention as a promising alternative for oxygen production. The oxygen-transport membranes should not only exhibit a high oxygen flux but also show good stability under CO2-containing atmospheres. Therefore, designing and optimization, as well as practical application of membrane materials with good CO2 stability is a challenge. In this work, apart from discussion of literature data, authors’ own results are provided, which are focused on materia - related issues, including development of electrode materials exhibiting high ionic and electronic conductivities