Experimental Study on Variation Strategies for Complex Social Pedestrian Groups in Conflict Conditions

The paper concentrates on an experimental study of the variation strategies of complex social pedestrian groups in conflict conditions. We tracked the trajectories of group members and analysed the configuration of both the complex group and its subgroups when the groups walked through a narrowing passage, passed by an obstacle or faced counter flows. We summarized the variation strategies of complex groups when they faced these conflict conditions. The effect of groups on the crowd was also studied. It was found that groups could have significant effect on self-organization of the crowd. The results in the paper could be applied in modelling pedestrian group decision and behaviour and analysing crowd dynamics

ECSTM Studies of the Electrocatalyst Stability for the AAEM Fuel Cell

poster abstractAlkaline fuel cells (AFC) have come to the forefront of fuel cell research due to the friendlier environment they provide to the cell’s components in comparison to acid-based Proton Exchange Membrane (PEM) fuel cells. The AFC shows real world application of 60% efficiency, but suffers from long term degradation due to the formation of carbonate precipitates formed from carbon dioxide. A solid-state form of the AFC, the alkaline anion exchange membrane (AAEM) fuel cell, is under development to overcome the degradation, due to the usage of liquid potassium hydroxide (KOH) or sodium hydroxide (NaOH) electrolytes in the AFC. Also, the AFC are known to have a higher rate of contamination and therefore need higher purity fuel than their acidic counterparts. This problem is eliminated by the AAEM fuel cell. The cathode, which consists of the catalyst, ionomer and current supports in the AAEM fuel cell or the AFC, is the key component that determines the cell’s performance and stability. The material found to work best for the AAEM fuel cell is platinum (Pt). The issue with Pt as a catalyst material for these fuel cells is that is it very cost prohibitive for mass production. Therefore, other metals are being investigated to find a material with less cost, but perform as well as the Pt in AAEM fuel cells. Several theories have been proposed as to the cause of cathode degradation. It was found that an increase in current density, temperature and ligand (OH-) concentration accelerated corrosion of catalysts and carbon supports. Studies have been done on the catalyst material of Pt, as well as the highly oriented pytolytic graphite (HOPG). HOPG is a carbon-based material that Pt is deposited upon. So far, most of these studies were done in acid media. The objective of this work is to develop an in situ electrochemical scanning tunneling microcopy (ECSTM) method for characterizing stability of nano-Pt and HOPG substrate under operation conditions of an AFC. Future research will characterize the stability of other metal nanostructure in an attempt to find cheaper and effective alternatives to Platinum

ViCo: Engaging Video Comment Generation with Human Preference Rewards

Engaging video comments play an important role in video social media, as they are the carrier of feelings, thoughts, or humor of the audience. Preliminary works have made initial exploration for video comment generation by adopting caption-style encoder-decoder models. However, comment generation presents some unique challenges distinct from caption generation, which makes these methods somewhat less effective at generating engaging comments. In contrast to the objective and descriptive nature of captions, comments tend to be inherently subjective, making it hard to quantify and evaluate the engagement of comments. Furthermore, the scarcity of truly engaging comments brings difficulty to collecting enough high-quality training examples. In this paper, we propose ViCo with three novel designs to tackle the above challenges for generating engaging Video Comments. Firstly, to quantify the engagement of comments, we utilize the number of "likes" each comment receives as a proxy of human preference after an appropriate debiasing procedure. Secondly, to automatically evaluate the engagement of comments, we train a reward model to align its judgment to the above proxy. Our user studies indicate that this reward model effectively aligns with human judgments. Lastly, to alleviate the scarcity of high-quality comments, an initial generator is trained on readily available but noisy data to generate comments. Then the reward model is employed to offer feedback on the generated comments, thus optimizing the initial generator. To facilitate the research of video commenting, we collect a large video comment-dataset (ViCo-20k) with rich metadata from a popular video website. Experiments on ViCo-20k show that the comments generated by our ViCo model exhibit the best performance in terms of both quantitative and qualitative results, particularly when engagement is considered

Edge Accelerated Robot Navigation with Hierarchical Motion Planning

Low-cost autonomous robots suffer from limited onboard computing power, resulting in excessive computation time when navigating in cluttered environments. This paper presents Edge Accelerated Robot Navigation, or EARN for short, to achieve real-time collision avoidance by adopting hierarchical motion planning (HMP). In contrast to existing local or edge motion planning solutions that ignore the interdependency between low-level motion planning and high-level resource allocation, EARN adopts model predictive switching (MPS) that maximizes the expected switching gain w.r.t. robot states and actions under computation and communication resource constraints. As such, each robot can dynamically switch between a point-mass motion planner executed locally to guarantee safety (e.g., path-following) and a full-shape motion planner executed non-locally to guarantee efficiency (e.g., overtaking). The crux to EARN is a two-time scale integrated decision-planning algorithm based on bilevel mixed-integer optimization, and a fast conditional collision avoidance algorithm based on penalty dual decomposition. We validate the performance of EARN in indoor simulation, outdoor simulation, and real-world environments. Experiments show that EARN achieves significantly smaller navigation time and collision ratios than state-of-the-art navigation approaches.Comment: 12 pages, 14 figures, 1 table, submitted to IEEE for possible publicatio

TeViS:Translating Text Synopses to Video Storyboards

A video storyboard is a roadmap for video creation which consists of shot-by-shot images to visualize key plots in a text synopsis. Creating video storyboards, however, remains challenging which not only requires cross-modal association between high-level texts and images but also demands long-term reasoning to make transitions smooth across shots. In this paper, we propose a new task called Text synopsis to Video Storyboard (TeViS) which aims to retrieve an ordered sequence of images as the video storyboard to visualize the text synopsis. We construct a MovieNet-TeViS dataset based on the public MovieNet dataset. It contains 10K text synopses each paired with keyframes manually selected from corresponding movies by considering both relevance and cinematic coherence. To benchmark the task, we present strong CLIP-based baselines and a novel VQ-Trans. VQ-Trans first encodes text synopsis and images into a joint embedding space and uses vector quantization (VQ) to improve the visual representation. Then, it auto-regressively generates a sequence of visual features for retrieval and ordering. Experimental results demonstrate that VQ-Trans significantly outperforms prior methods and the CLIP-based baselines. Nevertheless, there is still a large gap compared to human performance suggesting room for promising future work. The code and data are available at: \url{https://ruc-aimind.github.io/projects/TeViS/}Comment: Accepted to ACM Multimedia 202

Oxidation of metals at the chromium oxide interface

Metal thin-film deposition, over the Cr2O3 surface of CrO2 thin-film substrates, exhibits a redox reaction at the interface. The transition metal forms an oxide in combination with the reduction of the near-surface chromium oxide to Cr2O3 . The insulating barrier layer Cr2O3 increases with the formation of Pb3 O4 in Pb/Cr2O3 /CrO2 and CoO in Co/Cr2O3 /CrO2 junctions, respectively

Anxiety and depression in dry eye patients during the COVID-19 pandemic: Mental state investigation and influencing factor analysis

ObjectiveInvestigate the anxiety and depression states among dry eye (DE) patients during the COVID-19 outbreak and analyze their influence factors.MethodsThe study was conducted in a tertiary eye hospital in Tianjin, China from March–April 2021. Four hundred twenty-eight DE patients were tested with the Ocular Surface Disease Index, Short Healthy Anxiety Inventory, Hospital Anxiety and Depression Scale, and Pittsburgh Sleep Quality Index. Descriptive statistics was used to assess the difference between DE with depression or anxiety among different groups. And multiple linear regression was used to explore factors that influence anxiety and depression in DE patients.ResultsThe incidence rates of anxiety and depression among DE patients during COVID-19 were 27.34 and 26.87%, respectively. The proportion with comorbid anxiety and depression was 24.30%. Patients' education level (t = −3.001, P < 0.05; t = −3.631, P < 0.05), course of disease (t = 2.341, P < 0.05; t = 2.444, P < 0.05), health anxiety (t = 3.015, P < 0.05; t = 2.731, P < 0.05), and subjective sleep quality (t = 3.610, P < 0.05; t = 4.203, P < 0.05) had certain influences on anxiety and depression.ConclusionThe results showed that subjective symptoms of DE patients were related to depression and anxiety. Higher education, shorter disease duration, lower health anxiety levels, and better subjective sleep quality were associated with the reduced depressive and anxiety symptoms in DE patients. These findings could be deemed beneficial to the treatment and prevention of DE during the COVID-19 epidemic