Core Viewpoint - The article emphasizes the growing importance of tabular data in AI applications across various sectors, including finance, healthcare, education, recommendation systems, and scientific research [1]. Group 1: Background and Importance of Tabular Data - Tabular data is fundamentally a structured representation of information, offering inherent advantages in organizing and expressing complex data relationships [3]. - The rise of deep learning has led to significant advancements in fields like computer vision and natural language processing, making the application of deep neural networks (DNN) to tabular data a research hotspot [6]. Group 2: Deep Learning Approaches to Tabular Data - The research categorizes deep learning methods for tabular data into three types: specialized methods, transferable methods, and general methods, reflecting the evolution of deep learning technology and the enhancement of model generalization capabilities [7][19]. - Specialized methods are the earliest and most widely used, focusing on obtaining high-quality representations from feature and sample levels [9]. - Transferable methods leverage pre-trained models to improve learning efficiency and reduce reliance on computational resources and data scale [12]. - General methods extend the generalization ability of pre-trained tabular models to various heterogeneous downstream tasks without additional fine-tuning [19]. Group 3: Challenges in Tabular Data Learning - Tabular data presents unique challenges, including feature heterogeneity, lack of spatial or sequential structure, low-quality and missing data, and the importance of feature engineering [22][23][25][26]. - The presence of class imbalance in many tabular datasets can lead to biased predictions, necessitating specific strategies for model training [27]. - Scalability to large datasets poses additional challenges, particularly as dimensionality increases, raising the risk of overfitting [28]. Group 4: Evaluation and Benchmarking - The article discusses the importance of robust evaluation methods for tabular models, highlighting the need for diverse benchmark datasets to assess model performance across different tasks and feature types [36]. - Performance evaluation metrics for classification tasks include accuracy, AUC, and F1 score, while regression tasks typically use MSE, MAE, and R² [32][33]. - Recent research emphasizes the need for comprehensive benchmarks that include semantically rich datasets to enhance the evaluation of tabular models [38][39].

Core Viewpoint - The aging population globally is driving significant growth opportunities in the eldercare robotics industry, with the first international standard for eldercare robots being established, marking a new phase of standardization and intelligence in the sector [1][2]. Industry Development - By 2050, the global population aged 60 and above is expected to reach 2.1 billion, with those aged 80 and above at 426 million, highlighting the urgent need for eldercare solutions [1]. - The eldercare robotics market is diversifying, with countries like China, Japan, Germany, the USA, and Italy leading in technology development and product application [1][2]. - The international standard for eldercare robots, initiated by China, provides benchmarks for design, manufacturing, testing, and certification, addressing the lack of unified performance specifications in the market [2][3]. Technological Advancements - New information technologies such as 5G, AI, IoT, and big data are enhancing the capabilities of eldercare robots in areas like mobility assistance, health care, and emotional companionship [1][2]. - Advanced sensors are crucial for eldercare robots, enabling real-time monitoring of elderly individuals' activities and responding to emergencies [2][3]. Market Potential - The establishment of international standards is expected to improve product quality and performance, increase consumer trust, and expand market demand for eldercare robots [3]. - China is well-positioned for rapid development in the eldercare robotics sector, supported by government policies and innovations from local companies [3]. Product Innovations - The GARMI robot from Germany is designed for daily living assistance, medical care, and social interaction, showcasing high levels of human-robot interaction [5][6]. - Japan's AIREC robot utilizes deep neural networks for complex tasks, demonstrating the potential of technology to address challenges posed by an aging population and a shortage of caregivers [8][9]. Future Outlook - The eldercare robotics industry is expected to evolve, with advancements leading to more diverse functionalities and improved quality of life for the elderly [3]. - The collaboration between research teams and healthcare institutions is crucial for the acceptance and effectiveness of eldercare robots [7][10].