Explore the vast range of titles available.

Recently, wearable devices have become a prominent health care application domain by incorporating a growing number of sensors and adopting smart machine learning technologies. One closely related topic is the strategy of combining the wearable device technology with skill assessment, which can be used in wearable device apps for coaching and/or personal training. Particularly pertinent to skill assessment based on high-dimensional time series data from wearable sensors is classifying whether a player is an expert or a beginner, which skills the player is exercising, and extracting some low-dimensional representations useful for coaching. In this paper, we present a deep learning-based coaching assistant method, which can provide useful information in supporting table tennis practice. Our method uses a combination of LSTM (Long short-term memory) with a deep state space model and probabilistic inference. More precisely, we use the expressive power of LSTM when handling high-dimensional time series data, and state space model and probabilistic inference to extract low-dimensional latent representations useful for coaching. Experimental results show that our method can yield promising results for characterizing high-dimensional time series patterns and for providing useful information when working with wearable IMU (Inertial measurement unit) sensors for table tennis coaching.

Recently, data from built-in sensors in smartphones have been readily available, and analyzing data for various types of health information from smartphone users has become a popular health care application area. Among relevant issues in the area, one of the most prominent topics is analyzing the characteristics of human movements. In this paper, we focus on characterizing the human movements of walking and running based on a novel machine learning approach. Since walking and running are human fundamental activities, analyzing their characteristics promptly and automatically during daily smartphone use is particularly valuable. In this paper, we propose a machine learning approach, referred to as ’two-stage latent dynamics modeling and filtering’ (TS-LDMF) method, where we combine a latent space modeling stage with a nonlinear filtering stage, for characterizing individual dynamic walking and running patterns by analyzing smartphone sensor data. For the task of characterizing movements, the proposed method makes use of encoding the high-dimensional sequential data from movements into random variables in a low-dimensional latent space. The use of random variables in the latent space, often called latent variables, is particularly useful, because it is capable of conveying compressed information concerning movements and efficiently handling the uncertainty originating from high-dimensional sequential observation. Our experimental results show that the proposed use of two-stage latent dynamics modeling and filtering yields promising results for characterizing individual dynamic walking and running patterns.

This study aims to empirically analyze the direct effects of local government competencies on regional economic revitalization within the broader context of local communities and to investigate the moderating role of social trust in this relationship. Using panel data constructed from the 2012–2019 Seoul Survey provided by the Seoul Metropolitan Government and panel data from South Korea’s National Statistical Office, we employed feasible generalized least squares to account for potential heteroscedasticity and serial correlation. The results demonstrate that local government competencies positively impact regional economic vitality within local communities, with high levels of social trust among residents in these communities further strengthening this positive effect. This study highlights the theoretical importance of integrating resource-based and social capital theories to advance the field of urban regeneration and emphasizes the role of local communities in economic development. The findings suggest that even where local government competencies may be limited, a strong foundation of community social trust within local communities can drive economic revitalization. This underscores the need for central and local governments to actively enhance social trust within communities as a means of fostering sustainable economic growth.

Suspended particulate matter (SPM) is an indispensable component of water environments. Its fate and transport involve various physical and biogeochemical cycles. This paper provides a comprehensive review of SPM dynamics by integrating insights from biogeochemical processes, spatiotemporal observation techniques, and numerical modeling approaches. It also explores methods for diagnosing SPM-mediated biogeochemical processes, such as the flocculation kinetics test and organic matter composition analysis. Advances in remote sensing, in situ monitoring, and high-resolution retrieval algorithms are discussed, highlighting their significance in detecting and quantifying SPM concentrations across varying spatial and temporal scales. Furthermore, this review examines integrated models that incorporate population balance equations on the basis of flocculation kinetics into multi-dimensional sediment transport models. The results from this study provide valuable insights into SPM dynamics, ultimately enhancing our knowledge of SPM behavior and transport in water environments. However, uncertainties remain due to limited field data on flocculation kinetics and the need for parameter optimization in numerical models. Addressing these gaps through enhanced fieldwork and model refinement will significantly improve our ability to predict and manage SPM dynamics, which is critical for sustainable aquatic ecosystem management in an era of rapid environmental change.

Racial inequalities in older adult health are well-documented. Compared to Whites, Blacks have greater risks of disability, dementia, multimorbidity and mortality. These differences in age-related health are paralleled by race differences in context. In particular, approximately 80% of older Blacks were born in Jim Crow South no matter where they currently live compared to 30% percent of older Whites. In the United States, the U.S. South has historically structured the life course experiences of Black and White older adults differently through legalized racial segregation. As a result, the role of association of Southern context with later life health risks is expected to differ substantially across the groups. We focus on three critical dimensions of population health to better understand the role of context for race difference in health – cognitive and physical functioning as well as mortality. Drawing on the Health and Retirement study from 2000-2016, we estimate a series of hazard models for each of the health outcomes separately for each race group. This approach promises new insights into the role of context for within-group variation in the health outcomes as well as for potential between-group differences for each specific outcome. Whereas for Whites Southern birth regardless of residence was associated with increased risk across all three conditions, for Black older adults we found more heterogeneity. Those who were born and lived in the South had the highest risk of dementia. Southern residence was associated with an increased risk of mortality, but its effect was ambiguous for risk of disability.

Wastewater sludge is used as an alternative fuel due to its high organic content and calorific value. However, influent characteristics and operational practices of wastewater treatment plants (WWTPs) can increase the sulfur content of sludge, devaluing it as a fuel. Thus, we investigated the biochemical mechanisms that elevate the sulfur content of sludge in a full-scale industrial WWTP receiving wastewater of the textile dyeing industry and a domestic WWTP by monitoring the sulfate, sulfur, and iron contents and the biochemical transformation of sulfate to sulfur in the wastewater and sludge treatment streams. A batch sulfate reduction rate test and microbial 16S rRNA and dsrB gene sequencing analyses were applied to assess the potential and activity of sulfate-reducing bacteria and their effect on sulfur deposition. This study indicated that the primary clarifier and anaerobic digester prominently reduced sulfate concentration through biochemical sulfate reduction and iron–sulfur complexation under anaerobic conditions, from 1247 mg/L in the influent to 6.2~59.8 mg/L in the industrial WWTP and from 46.7 mg/L to 0~0.8 mg/L in the domestic WWTPs. The anaerobic sludge, adapted in the high sulfate concentration of the industrial WWTP, exhibited a two times higher specific sulfate reduction rate (0.13 mg SO42−/gVSS/h) and sulfur content (3.14% DS) than the domestic WWTP sludge. Gene sequencing analysis of the population structure of common microbes and sulfate-reducing bacteria indicated the diversity of microorganisms involved in biochemical sulfate reduction in the sulfur cycle, supporting the data revealed by chemical analysis and batch tests.

Recent Text-to-SQL methods leverage large language models (LLMs) by incorporating feedback from the database management system. While these methods effectively address execution errors in SQL queries, they struggle with database mismatches -- errors that do not trigger execution exceptions. Database mismatches include issues such as condition mismatches and stricter constraint mismatches, both of which are more prevalent in real-world scenarios. To address these challenges, we propose a tool-assisted agent framework for SQL inspection and refinement, equipping the LLM-based agent with two specialized tools: a retriever and a detector, designed to diagnose and correct SQL queries with database mismatches. These tools enhance the capability of LLMs to handle real-world queries more effectively. We also introduce Spider-Mismatch, a new dataset specifically constructed to reflect the condition mismatch problems encountered in real-world scenarios. Experimental results demonstrate that our method achieves the highest performance on the averaged results of the Spider and Spider-Realistic datasets in few-shot settings, and it significantly outperforms baseline methods on the more realistic dataset, Spider-Mismatch.

Multi-Modal Entity Alignment aims to discover identical entities across heterogeneous knowledge graphs. While recent studies have delved into fusion paradigms to represent entities holistically, the elimination of features irrelevant to alignment and modal inconsistencies is overlooked, which are caused by inherent differences in multi-modal features. To address these challenges, we propose a novel strategy of progressive modality freezing, called PMF, that focuses on alignmentrelevant features and enhances multi-modal feature fusion. Notably, our approach introduces a pioneering cross-modal association loss to foster modal consistency. Empirical evaluations across nine datasets confirm PMF's superiority, demonstrating stateof-the-art performance and the rationale for freezing modalities. Our code is available at https://github.com/ninibymilk/PMF-MMEA.

Interactive Object Grasping (IOG) is the task of identifying and grasping the desired object via human-robot natural language interaction. Current IOG systems assume that a human user initially specifies the target object's category (e.g., bottle). Inspired by pragmatics, where humans often convey their intentions by relying on context to achieve goals, we introduce a new IOG task, Pragmatic-IOG, and the corresponding dataset, Intention-oriented Multi-modal Dialogue (IM-Dial). In our proposed task scenario, an intention-oriented utterance (e.g., \"I am thirsty\") is initially given to the robot. The robot should then identify the target object by interacting with a human user. Based on the task setup, we propose a new robotic system that can interpret the user's intention and pick up the target object, Pragmatic Object Grasping (PROGrasp). PROGrasp performs Pragmatic-IOG by incorporating modules for visual grounding, question asking, object grasping, and most importantly, answer interpretation for pragmatic inference. Experimental results show that PROGrasp is effective in offline (i.e., target object discovery) and online (i.e., IOG with a physical robot arm) settings. Code and data are available at https://github.com/gicheonkang/prograsp.