Learning Graph Convolutional Network for Skeleton-based Human Action Recognition by Neural Searching

Human action recognition from skeleton data, fueled by the Graph Convolutional Network (GCN), has attracted lots of attention, due to its powerful capability of modeling non-Euclidean structure data. However, many existing GCN methods provide a pre-defined graph and fix it through the entire network, which can loss implicit joint correlations. Besides, the mainstream spectral GCN is approximated by one-order hop, thus higher-order connections are not well involved. Therefore, huge efforts are required to explore a better GCN architecture. To address these problems, we turn to Neural Architecture Search (NAS) and propose the first automatically designed GCN for skeleton-based action recognition. Specifically, we enrich the search space by providing multiple dynamic graph modules after fully exploring the spatial-temporal correlations between nodes. Besides, we introduce multiple-hop modules and expect to break the limitation of representational capacity caused by one-order approximation. Moreover, a sampling- and memory-efficient evolution strategy is proposed to search an optimal architecture for this task. The resulted architecture proves the effectiveness of the higher-order approximation and the dynamic graph modeling mechanism with temporal interactions, which is barely discussed before. To evaluate the performance of the searched model, we conduct extensive experiments on two very large scaled datasets and the results show that our model gets the state-of-the-art results.Comment: Accepted by AAAI202

Data Leakage and Evaluation Issues in Micro-Expression Analysis

Micro-expressions have drawn increasing interest lately due to various potential applications. The task is, however, difficult as it incorporates many challenges from the fields of computer vision, machine learning and emotional sciences. Due to the spontaneous and subtle characteristics of micro-expressions, the available training and testing data are limited, which make evaluation complex. We show that data leakage and fragmented evaluation protocols are issues among the micro-expression literature. We find that fixing data leaks can drastically reduce model performance, in some cases even making the models perform similarly to a random classifier. To this end, we go through common pitfalls, propose a new standardized evaluation protocol using facial action units with over 2000 micro-expression samples, and provide an open source library that implements the evaluation protocols in a standardized manner. Code will be available in \url{https://github.com/tvaranka/meb}

Multi-level decision framework collision avoidance algorithm in emergency scenarios

With the rapid development of autonomous driving, the attention of academia has increasingly focused on the development of anti-collision systems in emergency scenarios, which have a crucial impact on driving safety. While numerous anti-collision strategies have emerged in recent years, most of them only consider steering or braking. The dynamic and complex nature of the driving environment presents a challenge to developing robust collision avoidance algorithms in emergency scenarios. To address the complex, dynamic obstacle scene and improve lateral maneuverability, this paper establishes a multi-level decision-making obstacle avoidance framework that employs the safe distance model and integrates emergency steering and emergency braking to complete the obstacle avoidance process. This approach helps avoid the high-risk situation of vehicle instability that can result from the separation of steering and braking actions. In the emergency steering algorithm, we define the collision hazard moment and propose a multi-constraint dynamic collision avoidance planning method that considers the driving area. Simulation results demonstrate that the decision-making collision avoidance logic can be applied to dynamic collision avoidance scenarios in complex traffic situations, effectively completing the obstacle avoidance task in emergency scenarios and improving the safety of autonomous driving

Hyperbolic Deep Neural Networks: A Survey

Recently, there has been a rising surge of momentum for deep representation learning in hyperbolic spaces due to theirhigh capacity of modeling data like knowledge graphs or synonym hierarchies, possessing hierarchical structure. We refer to the model as hyperbolic deep neural network in this paper. Such a hyperbolic neural architecture potentially leads to drastically compact model withmuch more physical interpretability than its counterpart in Euclidean space. To stimulate future research, this paper presents acoherent and comprehensive review of the literature around the neural components in the construction of hyperbolic deep neuralnetworks, as well as the generalization of the leading deep approaches to the Hyperbolic space. It also presents current applicationsaround various machine learning tasks on several publicly available datasets, together with insightful observations and identifying openquestions and promising future directions

Heightening of an Existing Embankment Dam: Results from Numerical Simulations

The old dam of the Zhushou Reservoir is a clay core rock-debris dam with a maximum height of 63.4 m. After heightening, the new dam is a concrete-faced rockfill dam with a maximum height of 98.1 m. In the initial design stage, a rigid connection is proposed between the cutoff wall and toe slab. After the concrete cutoff wall is built at the axis of the old dam, a complete cutoff system is composed of cutoff wall, toe slab, and face slab. In this paper, based on the static and dynamic tests of dam materials, the Shen Zhujiang double-yield surface elastic-plastic model is used as the static constitutive model, and the contact friction model is used as the contact surface model. The three-dimensional finite element method is used to simulate the construction filling and water storage process during operation. The simulation results show that the maximum horizontal displacement occurs in the dam body of the old dam and the maximum settlement occurs at the interface between the old and new dams. During the storage period, the cutoff wall will not be damaged, and the tensile stress of the local area at the junction of toe slab and bank slope has exceeded the allowable value for C30 plain concrete, so the reinforcement should be strengthened at this location

An investigation of occupants’ thermal requirements in indoor transitional space in entertainment buildings

Indoor transitional space is a popular buffer space between buildings’ interior and exterior environments nowadays. Maintaining a comfortable indoor thermal comfort for transitional spaces often poses challenges to building designers and engineers. Some existing studies have already explored this topic, but they are mainly carried out in academic buildings. There are, however, still many other types of buildings containing transitional space, including entertainment buildings such as theaters and tourist centers. To provide useful information about people’s thermal requirements in the transitional space of entertainment buildings, this study has adopted both field measurement and questionnaire methods. Additionally, the same method has been repeated in an academic setting as well, so the results can be compared with existing studies. By comparing participants’ thermal requirements, it indicates that people’s thermal requirements are significantly impacted by operative temperature, which can give architects suggestions to improve the thermal environment in transitional spaces. In addition, in transitional spaces, people had a high tolerance for their thermal environment, especially participants in entertainment buildings, who showed a fairly high thermal satisfaction rate of 96% in winter and 94% in summer, far beyond the rates of 89% and 73% in academic buildings. Combined with the analysis of participants’ thermal preferences and the reason people stay in transitional spaces, it implies a close relationship between participants’ thermal comfort differences and the function that transitional spaces provide

Creating a Forum for Library Professionals

This paper examines the establishment, growth, achievements, and future planning of the CALA Canada Chapter. Since its inception in June 2018, the Chapter has experienced significant growth, with the number of members doubling, and the number of life members also doubling. Currently there are a total of thirty members in the Chapter, comprising ten life members, eight overseas members, and seven student members, with the majority residing or working in Ontario. The Chapter has achieved notable milestones, including the organization of successful events such as conferences, workshops, and networking sessions. The Chapter has also contributed to the development of the library profession in Canada, particularly by promoting diversity and inclusivity. Looking forward, the Chapter plans to expand its reach and increase its membership by promoting itself in other regions of the country. The Chapter aims to continue providing valuable resources, programs, and opportunities for its members to enhance their professional development and foster collaboration. Through these efforts, the Canada Chapter aims to play an essential role in advancing the library profession in Canada and promoting its growth and innovation