Steady State of Pedestrian Flow in Bottleneck Experiments

Experiments with pedestrians could depend strongly on initial conditions. Comparisons of the results of such experiments require to distinguish carefully between transient state and steady state. In this work, a feasible algorithm - Cumulative Sum Control Chart - is proposed and improved to automatically detect steady states from density and speed time series of bottleneck experiments. The threshold of the detection parameter in the algorithm is calibrated using an autoregressive model. Comparing the detected steady states with previous manually selected ones, the modified algorithm gives more reproducible results. For the applications, three groups of bottleneck experiments are analysed and the steady states are detected. The study about pedestrian flow shows that the difference between the flows in all states and in steady state mainly depends on the ratio of pedestrian number to bottleneck width. When the ratio is higher than a critical value (approximately 115 persons/m), the flow in all states is almost identical with the flow in steady state. Thus we have more possibilities to compare the flows from different experiments, especially when the detection of steady states is difficult.Comment: 19 pages, 7 figure

The Effect of Weather on Track Performances: A Panel Data Analysis on Carolina Godiva Track Club Summer Track Races

The objective of this paper is to study of how running times are affected by weather, in particular, focusing on how temperature and dew point will have an effect on the track performances using panel data. The research technique employed in the paper is linear mixed model method, which is commonly used to model datasets where multiple individuals perform over time. The regression results and descriptive statistics indicate that more sophisticated models than linear mixed model need to be used. The coding language used in the paper is R.Bachelor of Scienc

Graph Neural Aggregation-diffusion with Metastability

Continuous graph neural models based on differential equations have expanded the architecture of graph neural networks (GNNs). Due to the connection between graph diffusion and message passing, diffusion-based models have been widely studied. However, diffusion naturally drives the system towards an equilibrium state, leading to issues like over-smoothing. To this end, we propose GRADE inspired by graph aggregation-diffusion equations, which includes the delicate balance between nonlinear diffusion and aggregation induced by interaction potentials. The node representations obtained through aggregation-diffusion equations exhibit metastability, indicating that features can aggregate into multiple clusters. In addition, the dynamics within these clusters can persist for long time periods, offering the potential to alleviate over-smoothing effects. This nonlinear diffusion in our model generalizes existing diffusion-based models and establishes a connection with classical GNNs. We prove that GRADE achieves competitive performance across various benchmarks and alleviates the over-smoothing issue in GNNs evidenced by the enhanced Dirichlet energy.Comment: 10 pages, 2 figure

Link-Context Learning for Multimodal LLMs

The ability to learn from context with novel concepts, and deliver appropriate responses are essential in human conversations. Despite current Multimodal Large Language Models (MLLMs) and Large Language Models (LLMs) being trained on mega-scale datasets, recognizing unseen images or understanding novel concepts in a training-free manner remains a challenge. In-Context Learning (ICL) explores training-free few-shot learning, where models are encouraged to ``learn to learn" from limited tasks and generalize to unseen tasks. In this work, we propose link-context learning (LCL), which emphasizes "reasoning from cause and effect" to augment the learning capabilities of MLLMs. LCL goes beyond traditional ICL by explicitly strengthening the causal relationship between the support set and the query set. By providing demonstrations with causal links, LCL guides the model to discern not only the analogy but also the underlying causal associations between data points, which empowers MLLMs to recognize unseen images and understand novel concepts more effectively. To facilitate the evaluation of this novel approach, we introduce the ISEKAI dataset, comprising exclusively of unseen generated image-label pairs designed for link-context learning. Extensive experiments show that our LCL-MLLM exhibits strong link-context learning capabilities to novel concepts over vanilla MLLMs. Code and data will be released at https://github.com/isekai-portal/Link-Context-Learning.Comment: 10 pages, 8 figure

Patients With Floaters: Answers From Virtual Assistants and Large Language Models

OBJECTIVES: Floaters, a common complaint among patients of all ages, was used as a query term because it affects 30% of all people searching for eye care. The American Academy of Ophthalmology website\u27s floaters section was used as a source for questions and answers (www.aao.org). Floaters is a visual obstruction that moves with the movement of the eye. They can be associated with retinal detachment, which can lead to vision loss. With the advent of large language model (LLM) chatbots ChatGPT, Bard versus virtual assistants (VA), Google Assistant, and Alexa, we analyzed their responses to floaters. METHODS: Using AAO.org, Public & Patients, and its related subsection, EyeHealth A-Z : Floaters and Flashes link, we asked four questions: (1) What are floaters? (2) What are flashes? (3) Flashes and Migraines? (4) Floaters and Flashes Treatment? to ChatGPT, Bard, Google Assistant, and Alexa. The American Academy of Ophthalmology (AAO) keywords were identified if they were highlighted. The Flesch-Kincaid Grade Level formula approved by the U.S. Department of Education, was used to evaluate the reading comprehension level for the responses. RESULTS: Of the chatbots and virtual assistants, Google Assistant is the only one that uses the term ophthalmologist. There is no mention of the urgency or emergency nature of floaters. AAO.org shows a lower reading level vs the LLMs and VA ( CONCLUSION: Currently, ChatGPT, Bard, Google Assistant, and Alexa are similar. Factual information is present but all miss the urgency of the diagnosis of a retinal detachment. Translational relevance: Both the LLM and virtual assistants are free and our patients will use them to obtain floaters information. There may be errors of omission with ChatGPT and a lack of urgency to seek a physician\u27s care

Energy-related CO₂ emission accounts and datasets for 40 emerging economies in 2010-2019

Since 2000, CO2 emissions from emerging economies have outstripped those of developed economies. To limit global warming to under 1.5gg C by 2100, over 100 emerging economies have proposed net-zero carbon targets. Yet the supportive data are lacking-no inventory of CO2 emission outlines detailed sources by sector or distribution at the subnational level for these economies. Here, we redress the balance by establishing a dataset for an energy-related CO2 emission inventory that covers 47 sectors and eight energy types in 40 emerging economies (10.5281/zenodo.7309360, Cui et al., 2021). Their emissions, growing rapidly by 3.0g%gyr-1, reached 7.5gGt in 2019 and were sourced primarily in coal and oil (34.6g% and 28.1g%, respectively) and consumed by the power and transportation sectors. Meanwhile, among African countries in this group, biomass combustion was responsible for 34.7g%-96.2g% of emissions. Our dataset fills a data gap by providing a detailed, robust emission accounting baseline for emerging economies-an advance that will support emission reduction policymaking at global, national, and subnational levels.</p

Understanding Oversmoothing in Diffusion-Based GNNs From the Perspective of Operator Semigroup Theory

This paper presents a novel study of the oversmoothing issue in diffusion-based Graph Neural Networks (GNNs). Diverging from extant approaches grounded in random walk analysis or particle systems, we approach this problem through operator semigroup theory. This theoretical framework allows us to rigorously prove that oversmoothing is intrinsically linked to the ergodicity of the diffusion operator. This finding further poses a general and mild ergodicity-breaking condition, encompassing the various specific solutions previously offered, thereby presenting a more universal and theoretically grounded approach to mitigating oversmoothing in diffusion-based GNNs. Additionally, we offer a probabilistic interpretation of our theory, forging a link with prior works and broadening the theoretical horizon. Our experimental results reveal that this ergodicity-breaking term effectively mitigates oversmoothing measured by Dirichlet energy, and simultaneously enhances performance in node classification tasks

Association of autoimmune and allergic diseases with senile cataract: a bidirectional two-sample Mendelian randomization study

BackgroundMany observational studies have been reported that patients with autoimmune or allergic diseases seem to have a higher risk of developing senile cataract, but the views are not consistent. In order to minimize the influence of reverse causality and potential confounding factors, we performed Mendelian Randomization (MR) analysis to investigate the genetic causal associations between autoimmune, allergic diseases and senile cataract.MethodsSingle nucleotide polymorphisms associated with ten common autoimmune and allergic diseases were obtained from the IEU Open genome-wide association studies (GWAS) database. Summary-level GWAS statistics for clinically diagnosed senile cataract were obtained from the FinnGen research project GWAS, which consisted of 59,522 individuals with senile cataracts and 312,864 control individuals. MR analysis was conducted using mainly inverse variance weighted (IVW) method and further sensitivity analysis was performed to test robustness.ResultsAs for ten diseases, IVW results confirmed that type 1 diabetes (OR = 1.06; 95% CI = 1.05-1.08; p = 2.24×10-12), rheumatoid arthritis (OR = 1.05; 95% CI = 1.02-1.08; p = 1.83×10-4), hypothyroidism (OR = 2.4; 95% CI = 1.42-4.06; p = 1.12×10-3), systemic lupus erythematosus (OR = 1.02; 95% CI = 1.01-1.03; p = 2.27×10-3), asthma (OR = 1.02; 95% CI = 1.01-1.03; p = 1.2×10-3) and allergic rhinitis (OR = 1.07; 95% CI = 1.02-1.11; p = 2.15×10-3) were correlated with the risk of senile cataract. Celiac disease (OR = 1.04; 95% CI = 1.01-1.08; P = 0.0437) and atopic dermatitis (OR = 1.05; 95% CI = 1.01-1.10; P = 0.0426) exhibited a suggestive connection with senile cataract after Bonferroni correction. These associations are consistent across weighted median and MR Egger methods, with similar causal estimates in direction and magnitude. Sensitivity analysis further proved that these associations were reliable.ConclusionsThe results of the MR analysis showed that there were causal relationships between type 1 diabetes, rheumatoid arthritis, hypothyroidism, systemic lupus erythematosus, asthma, allergic rhinitis and senile cataract. To clarify the possible role of autoimmune and allergy in the pathophysiology of senile cataract, further studies are needed