In this paper we present the system called Bardic, which was developed over three years as the core technology in the Narrative for Sensemaking project, an effort to automatically generate narrative from low-level eve...
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Adolescents with obesity face numerous health risks and encounter barriers that lead to physical inactivity. We developed a virtual reality sports system, named REVERIE (Real-World Exercise and VR-Based Exercise Resea...
Adolescents with obesity face numerous health risks and encounter barriers that lead to physical inactivity. We developed a virtual reality sports system, named REVERIE (Real-World Exercise and VR-Based Exercise Research in Education), which used deep reinforcement learning to train transformer-based virtual coaching agents, offering immersive and effective sports guidance, with biomechanical performance comparable to real-world physical sports. We integrated REVERIE into a randomized controlled trial involving an 8-week intervention in adolescents with excess body weight (n = 227). Participants were randomized (1:1:1:1:1) to physical table tennis, physical soccer, REVERIE table tennis, REVERIE soccer or control. REVERIE sports intervention was effective in reducing primary outcome fat mass (mean -4.28 kg (95% confidence interval (CI) -6.35 to -2.22), relative to control), with no significant difference compared with physical sports (mean -5.06 kg (95% CI -7.13 to -2.98), relative to control). For secondary outcomes, decreases in liver enzymes and low-density lipoprotein cholesterol levels were found in physical and REVERIE sports groups compared to control. Physical and REVERIE sports showed improvements in physical fitness, psychological well-being and sports willingness after an 8-week intervention, which remained at the 6-month follow-up in the REVERIE sports group. REVERIE sports demonstrated superior cognitive enhancements compared to physical sports in exploratory analyses, as evidenced by olfactory tests (total score: mean 2.84 (95% CI 1.15 to 4.53)) and working memory paradigm (2-back accuracy: mean 10.88% (95% CI 1.19% to 20.56%)). Functional magnetic resonance imaging exhibited that REVERIE sports enhanced neural efficiency and neuroplasticity. Multi-omics analyses revealed distinct changes induced by REVERIE sports that were closely associated with cognitive improvement. Minimal injury rates were 7.69% for REVERIE and 13.48% for physical sports, with no
This volume presents selected, peer-reviewed, short papers that were accepted for presentation in the 5th International Conference on Variable Neighborhood Search (ICVNS'17) which was held in Ouro Preto, Brazil, d...
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This volume presents selected, peer-reviewed, short papers that were accepted for presentation in the 5th International Conference on Variable Neighborhood Search (ICVNS'17) which was held in Ouro Preto, Brazil, during October 2–4, 2017.
In this paper we present the system called Bardic, which was developed over three years as the core technology in the Narrative for Sensemaking project, an effort to automatically generate narrative from low-level eve...
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In this study, we assessed the mechanical response of samples from human atherosclerotic diseased media and fibrous cap via uniaxial tensile testing. Results show a pronounced hysteresis phenomenon caused by viscoelas...
In this study, we assessed the mechanical response of samples from human atherosclerotic diseased media and fibrous cap via uniaxial tensile testing. Results show a pronounced hysteresis phenomenon caused by viscoelasticity during the loading-unloading process. An inverse analysis method with finite element modeling was employed to identify the material parameter values for a viscoelastic anisotropic (VA) constitutive model through matching simulation predictions of load-displacement curves with experimental measurements. The identified material parameter values can be used in simulation studies of diseased human carotid arteries, including investigations of inflation processes associated with stenting or angioplasty.
Data generated across time may not be easily comparable in its original form thus potentially leading to results that may be perceived as unfair to some. We investigate quality assessment of scholarly researchers from...
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Deep learning (DL) models have provided state-of-the-art performance in various medical imaging benchmarking challenges, including the Brain Tumor Segmentation (BraTS) challenges. However, the task of focal pathology ...
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Quantum key distribution (QKD) promises information theoretic secure key as long as the device performs as assumed in the theoretical model. One of the assumptions is an absence of information leakage about individual...
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Computational competitions are the standard for benchmarking medical image analysis algorithms, but they typically use small curated test datasets acquired at a few centers, leaving a gap to the reality of diverse mul...
Computational competitions are the standard for benchmarking medical image analysis algorithms, but they typically use small curated test datasets acquired at a few centers, leaving a gap to the reality of diverse multicentric patient data. To this end, the Federated Tumor Segmentation (FeTS) Challenge represents the paradigm for real-world algorithmic performance evaluation. The FeTS challenge is a competition to benchmark (i) federated learning aggregation algorithms and (ii) state-of-the-art segmentation algorithms, across multiple international sites. Weight aggregation and client selection techniques were compared using a multicentric brain tumor dataset in realistic federated learning simulations, yielding benefits for adaptive weight aggregation, and efficiency gains through client sampling. Quantitative performance evaluation of state-of-the-art segmentation algorithms on data distributed internationally across 32 institutions yielded good generalization on average, albeit the worst-case performance revealed data-specific modes of failure. Similar multi-site setups can help validate the real-world utility of healthcare AI algorithms in the future.
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