Multimodal Neurophysiological Representations of High School Students’ Situational Interest: A Machine Learning Approach

Interest plays a vital role in students’ learning performance. Accurately measuring situational interest in the classroom environment is important for understanding the learning mechanism and improving teaching. However, self-report measurements frequently encounter issues of subjectivity and ambiguity, and it is hard to collect dynamic self-report scales without disturbance in the naturalistic environment. Thanks to the development of neuroscience and portable biosensors, it has become possible to represent psychological states with neurophysiological features in the classroom environment. In this study, multimodal neurophysiological signals, including electroencephalograph (EEG), electrodermal activity (EDA), and photoplethysmography (PPG), were applied to represent situational interest under both laboratory (Study 1) and naturalistic (Study 2) paradigms. A total of 33 features were extracted, and 7 different statistical indicators were calculated for each of them across all the epochs. Among these features, 47 in Study 1 and 49 in Study 2 demonstrated significant correlation with self-report situational interest. Employing a machine learning model, the analysis yielded a mean absolute error (MAE) of 0.772 and mean squared error (MSE) of 0.883 for the dataset in Study 1. However, the model was not robust on data from Study 2. These findings offer empirical support for the conceptual framework of situational interest, demonstrate the potential of neurophysiological data in educational assessments, and also highlight the challenges in naturalistic paradigm

How machine learning informs ride-hailing services: A survey

In recent years, online ride-hailing services have emerged as an important component of urban transportation system, which not only provide significant ease for residents’ travel activities, but also shape new travel behavior and diversify urban mobility patterns. This study provides a thorough review of machine-learning-based methodologies for on-demand ride-hailing services. The importance of on-demand ride-hailing services in the spatio-temporal dynamics of urban traffic is first highlighted, with machine-learning-based macro-level ride-hailing research demonstrating its value in guiding the design, planning, operation, and control of urban intelligent transportation systems. Then, the research on travel behavior from the perspective of individual mobility patterns, including carpooling behavior and modal choice behavior, is summarized. In addition, existing studies on order matching and vehicle dispatching strategies, which are among the most important components of on-line ride-hailing systems, are collected and summarized. Finally, some of the critical challenges and opportunities in ride-hailing services are discussed

Rebalanced Zero-shot Learning

Zero-shot learning (ZSL) aims to identify unseen classes with zero samples during training. Broadly speaking, present ZSL methods usually adopt class-level semantic labels and compare them with instance-level semantic predictions to infer unseen classes. However, we find that such existing models mostly produce imbalanced semantic predictions, i.e. these models could perform precisely for some semantics, but may not for others. To address the drawback, we aim to introduce an imbalanced learning framework into ZSL. However, we find that imbalanced ZSL has two unique challenges: (1) Its imbalanced predictions are highly correlated with the value of semantic labels rather than the number of samples as typically considered in the traditional imbalanced learning; (2) Different semantics follow quite different error distributions between classes. To mitigate these issues, we first formalize ZSL as an imbalanced regression problem which offers empirical evidences to interpret how semantic labels lead to imbalanced semantic predictions. We then propose a re-weighted loss termed Re-balanced Mean-Squared Error (ReMSE), which tracks the mean and variance of error distributions, thus ensuring rebalanced learning across classes. As a major contribution, we conduct a series of analyses showing that ReMSE is theoretically well established. Extensive experiments demonstrate that the proposed method effectively alleviates the imbalance in semantic prediction and outperforms many state-of-the-art ZSL methods. Our code is available at https://github.com/FouriYe/ReZSL-TIP23.Comment: Accepted to IEEE Transactions on Image Processing (TIP) 202

Low Skeletal Muscle Mass Is Associated With Inferior Preoperative and Postoperative Shoulder Function in Elderly Rotator Cuff Tear Patients

BACKGROUND: The age-related loss of skeletal muscle mass is an important characteristic of sarcopenia, an increasingly recognized condition with systemic implications. However, its association with shoulder function in elderly patients with rotator cuff tears (RCT) remains unknown. This study aimed to investigate the relationship between low skeletal muscle mass and shoulder function in elderly RCT patients. METHODS: A retrospective analysis was conducted on RCT patients who underwent chest computed tomography (CT) scans for clinical evaluation. Preoperative CT scan images of the chest were used to calculate the cross-sectional area (CSA) of thoracic muscle at the T4 level. The medical records were reviewed. Shoulder function was assessed using the ASES score and CMS score both preoperatively and at the final follow-up. Data on the preoperative range of motion (ROM) for the affected shoulder, were collected for analysis. Subgroup analyses by sex were also performed. RESULTS: A total of 283 RCT patients, consisting of 95 males and 188 females, with a mean age of 66.22 ± 4.89(range, 60-95 years) years were included in this retrospective study. The low muscle mass group showed significantly higher level of c-reactive protein (CRP) and erythrocyte sedimentation rate (ESR) compared to the normal group(3.75 ± 6.64 mg/L vs. 2.17 ± 2.30 mg/L, p = 0.021; 19.08 ± 12.86 mm/H vs.15.95 ± 10.76 mm/H, p = 0.038; respectively). In the normal group, pre-operative passive ROM, including forward elevation, abduction, lateral rotation, and abductive external rotation, was significantly better than that in the low muscle mass group (127.18 ± 34.87° vs. 89.76 ± 50.61°; 119.83 ± 45.76° vs. 87.16 ± 53.32°; 37.96 ± 28.33° vs. 25.82 ± 27.82°; 47.71 ± 23.56° vs. 30.87 ± 27.76°, all p \u3c 0.01, respectively). Similar results were found in the active ROM of the shoulder. The female low muscle mass group exhibited significantly poorer passive and active ROM (p \u3c 0.05). The post-operative ASES scores and CMS scores of the female low muscle mass group were also statistically worse than those of the female normal group (p \u3c 0.05). CONCLUSIONS: The results of present study revealed that the low skeletal muscle mass is associated with inferior ROM of the shoulder and per- and post-operative shoulder function, especially for elderly female patients