Interactive Visual Analytics for Agent-Based Simulation: Street-Crossing Behavior at Signalized Pedestrian Crossing

To design a pedestrian crossing area reasonably can be a demanding task for traffic planners. There are several challenges, including determining the appropriate dimensions, and ensuring that pedestrians are exposed to the least risks. Pedestrian safety is especially obscure to analyze, given that many people in Stockholm cross the street illegally by running against the red light. To cope with these challenges, computational approaches of trajectory data visual analytics can be used to support the analytical reasoning process. However, it remains an unexplored field regarding how to visualize and communicate the street-crossing spatio-temporal data effectively. Moreover, the rendering also needs to deal with a growing data size for a more massive number of people. This thesis proposes a web-based interactive visual analytics tool for pedestrians' street-crossing behavior under various flow rates. The visualization methodology is also presented, which is then evaluated to have achieved satisfying communication and rendering effectiveness for maximal 180 agents over 100 seconds. In terms of the visualization scenario, pedestrians either wait for the red light or cross the street illegally; all people can choose to stop by a buffer island before they finish crossing. The visualization enables the analysis under multiple flow rates for 1) pedestrian movement, 2) space utilization, 3) crossing frequency in time-series, and 4) illegal frequency. Additionally, to acquire the initial trajectory data, Optimal Reciprocal Collision Avoidance (ORCA) algorithm is engaged in the crowd simulation. Then different visualization techniques are utilized to comply with user demands, including map animation, data aggregation, and time-series graph

Efficient Private ERM for Smooth Objectives

In this paper, we consider efficient differentially private empirical risk minimization from the viewpoint of optimization algorithms. For strongly convex and smooth objectives, we prove that gradient descent with output perturbation not only achieves nearly optimal utility, but also significantly improves the running time of previous state-of-the-art private optimization algorithms, for both $\epsilon$-DP and $(\epsilon, \delta)$-DP. For non-convex but smooth objectives, we propose an RRPSGD (Random Round Private Stochastic Gradient Descent) algorithm, which provably converges to a stationary point with privacy guarantee. Besides the expected utility bounds, we also provide guarantees in high probability form. Experiments demonstrate that our algorithm consistently outperforms existing method in both utility and running time

Evaluation of Individual Contribution in Blended Collaborative Learning

With the deepening of classroom teaching reform, blended collaborative learning has become a common collaborative learning method, and its significance and value has been verified by many parties. However, there is still a lack of quantitative analysis and detailed insight into the internal interaction dynamics of the group at the individual level. There are limitations in the evaluation dimensions and methods of individual contribution in collaborative learning in previous studies, so it is difficult to obtain a comprehensive evaluation of individual contribution. The purpose of this study is to build an effective evaluation model of individual contribution in blended collaborative learning. Discussion recordings and text data in collaboration were collected in a non-invasive way to validate the model. Based on evaluation model, the characteristics and rules behind the data deeply were explored, the collaborative process of the blended collaborative learning was analyzed and mined, and the characteristics of learners\u27 contribution were summarized to support the development of blended collaborative learning

Evidence linking exposure of fish primary macrophages to antibiotics activates the NF-kB pathway.

Low doses of antibiotics are ubiquitous in the marine environment and may exert negative effects on non-target aquatic organisms. Using primary macrophages of common carp, we investigated the mechanisms of action following exposure to several common antibiotics; cefotaxime, enrofloxacin, tetracycline, sulfamonomethoxine, and their mixtures, and explored the immunomodulatory effects associated with the nuclear factor-κB (NF-κB) signaling pathway. A KEGG pathway analysis was conducted using the sixty-six differentially expressed genes found in all treatments, and showed that exposure to 100 μg/L of antibiotics could affect regulation of the NF-κB signaling pathway, suggesting that activation of NF-κB is a common response in all four classes of antibiotics. In addition, the four antibiotics induced nf-κb and NF-κB-associated cytokines expression, as verified by qPCR, however, these induction responses by four antibiotics were minor when compared to the same concentration of LPS treatment (100 μg/L). Antagonists of NF-κB blocked many of the immune effects of the antibiotics, providing evidence that NF-κB pathways mediate the actions of all four antibiotics. Moreover, exposure to environmentally relevant, low levels (0.01-100 μg/L) of antibiotics induced a NF-κB-mediated immune response, including endogenous generation of ROS, activity of antioxidant enzymes, as well as expression of cytokine and apoptosis. Moreover, exposure to mixtures of antibiotics presented greater effects on most tested immunological parameters than exposure to a single antibiotic, suggesting additive effects from multiple antibiotics in the environment. This study demonstrates that exposure of fish primary macrophages to low doses of antibiotics activates the NF-kB pathway

Fault diagnosis for PV arrays considering dust impact based on transformed graphical feature of characteristic curves and convolutional neural network with CBAM modules

Various faults can occur during the operation of PV arrays, and both the dust-affected operating conditions and various diode configurations make the faults more complicated. However, current methods for fault diagnosis based on I-V characteristic curves only utilize partial feature information and often rely on calibrating the field characteristic curves to standard test conditions (STC). It is difficult to apply it in practice and to accurately identify multiple complex faults with similarities in different blocking diodes configurations of PV arrays under the influence of dust. Therefore, a novel fault diagnosis method for PV arrays considering dust impact is proposed. In the preprocessing stage, the Isc-Voc normalized Gramian angular difference field (GADF) method is presented, which normalizes and transforms the resampled PV array characteristic curves from the field including I-V and P-V to obtain the transformed graphical feature matrices. Then, in the fault diagnosis stage, the model of convolutional neural network (CNN) with convolutional block attention modules (CBAM) is designed to extract fault differentiation information from the transformed graphical matrices containing full feature information and to classify faults. And different graphical feature transformation methods are compared through simulation cases, and different CNN-based classification methods are also analyzed. The results indicate that the developed method for PV arrays with different blocking diodes configurations under various operating conditions has high fault diagnosis accuracy and reliability

Strontium chloride improves bone mass by affecting the gut microbiota in young male rats

IntroductionBone mass accumulated in early adulthood is an important determinant of bone mass throughout the lifespan, and inadequate bone deposition may lead to associated skeletal diseases. Recent studies suggest that gut bacteria may be potential factors in boosting bone mass. Strontium (Sr) as a key bioactive element has been shown to improve bone quality, but the precise way that maintains the equilibrium of the gut microbiome and bone health is still not well understood.MethodsWe explored the capacity of SrCl2 solutions of varying concentrations (0, 100, 200 and 400 mg/kg BW) on bone quality in 7-week-old male Wistar rats and attempted to elucidate the mechanism through gut microbes.ResultsThe results showed that in a Wistar rat model under normal growth conditions, serum Ca levels increased after Sr-treatment and showed a dose-dependent increase with Sr concentration. Three-point mechanics and Micro-CT results showed that Sr exposure enhanced bone biomechanical properties and improved bone microarchitecture. In addition, the osteoblast gene markers BMP, BGP, RUNX2, OPG and ALP mRNA levels were significantly increased to varying degrees after Sr treatment, and the osteoclast markers RANKL and TRAP were accompanied by varying degrees of reduction. These experimental results show that Sr improves bones from multiple angles. Further investigation of the microbial population revealed that the composition of the gut microbiome was changed due to Sr, with the abundance of 6 of the bacteria showing a different dose dependence with Sr concentration than the control group. To investigate whether alterations in bacterial flora were responsible for the effects of Sr on bone remodeling, a further pearson correlation analysis was done, 4 types of bacteria (Ruminococcaceae_UCG-014, Lachnospiraceae_NK4A136_group, Alistipes and Weissella) were deduced to be the primary contributors to Sr-relieved bone loss. Of these, we focused our analysis on the most firmly associated Ruminococcaceae_UCG-014.DiscussionTo summarize, our current research explores changes in bone mass following Sr intervention in young individuals, and the connection between Sr-altered intestinal flora and potentially beneficial bacteria in the attenuation of bone loss. These discoveries underscore the importance of the “gut-bone” axis, contributing to an understanding of how Sr affects bone quality, and providing a fresh idea for bone mass accumulation in young individuals and thereby preventing disease due to acquired bone mass deficiency

Fast Chain-of-Thought: A Glance of Future from Parallel Decoding Leads to Answers Faster

In this work, we propose FastCoT, a model-agnostic framework based on parallel decoding without any further training of an auxiliary model or modification to the LLM itself. FastCoT uses a size-varying context window whose size changes with position to conduct parallel decoding and auto-regressive decoding simultaneously, thus fully utilizing GPU computation resources. In FastCoT, the parallel decoding part provides the LLM with a quick glance of the future composed of approximate tokens, which could lead to faster answers compared to regular autoregressive decoding used by causal transformers. We also provide an implementation of parallel decoding within LLM, which supports KV-cache generation and batch processing. Through extensive experiments, we demonstrate that FastCoT saves inference time by nearly 20% with only a negligible performance drop compared to the regular approach. Additionally, we show that the context window size exhibits considerable robustness for different tasks

V2XP-ASG: Generating Adversarial Scenes for Vehicle-to-Everything Perception

Recent advancements in Vehicle-to-Everything communication technology have enabled autonomous vehicles to share sensory information to obtain better perception performance. With the rapid growth of autonomous vehicles and intelligent infrastructure, the V2X perception systems will soon be deployed at scale, which raises a safety-critical question: \textit{how can we evaluate and improve its performance under challenging traffic scenarios before the real-world deployment?} Collecting diverse large-scale real-world test scenes seems to be the most straightforward solution, but it is expensive and time-consuming, and the collections can only cover limited scenarios. To this end, we propose the first open adversarial scene generator V2XP-ASG that can produce realistic, challenging scenes for modern LiDAR-based multi-agent perception systems. V2XP-ASG learns to construct an adversarial collaboration graph and simultaneously perturb multiple agents' poses in an adversarial and plausible manner. The experiments demonstrate that V2XP-ASG can effectively identify challenging scenes for a large range of V2X perception systems. Meanwhile, by training on the limited number of generated challenging scenes, the accuracy of V2X perception systems can be further improved by 12.3\% on challenging and 4\% on normal scenes. Our code will be released at https://github.com/XHwind/V2XP-ASG.Comment: ICRA 2023, see https://github.com/XHwind/V2XP-AS