The prevalence of depression is escalating, especially among youth, which has become a critical mental health concern. Current assessment methods, relying heavily on questionnaires, clinical observations, and AI-driven analyses, are limited by their focus on single-event data, failing to encapsulate the nuanced expressions of depressive symptoms. Moreover, a significant oversight in existing research is the underutilization of electromyogram (EMG) alongside electroencephalogram (EEG) data, which could provide a more holistic view of unconscious body behaviors. Additionally, given the high variability of depression among individuals, traditional analysis models are in urgent need of refinement to accommodate the personality of different individuals. To address these limitations, we propose M3ADD, a novel benchmark for Automatic Depression Detection that employs a Multimodal, Multitask, and Multievent framework. We collected EEG and EMG data from 97 participants across varied events (interview, reading tasks, walking), coupled with standardized questionnaires assessing depression, wellbeing, and personality, enriching our multitask learning approach. Our benchmark recognition algorithm leverages multitask learning, channel and interactive attention mechanisms to synthesize event-specific and modal-specific features, enhancing adaptability to individual differences and improving data utilization efficiency. M3ADD surpasses existing models by achieving 87% accuracy in detecting depression and 95% accuracy in assessing wellbeing, providing a promising avenue for early identification.

The conversion of monocular RGB images to depth maps is crucial in robotic applications. Current supervised learning approaches, dependent on high-quality RGB-Depth pairs, struggle with indoor environments characterized by multiple objects and fluctuating lighting, leading to inaccurate and unstable depth estimations. Additionally, commercial depth cameras on robots produce incomplete and light-sensitive depth maps, further degrading estimation quality. To surmount these issues, We created a comprehensive dataset of 9,600 RGB-Depth image pairs, capturing a range of indoor scenes under various lighting (dim, normal, and strong lighting), and interference conditions (local strong light interference, specular reflection interference, background similarity interference, and combinations of these factors). This dataset serves as a foundation for our proposed monocular RGB-to-depth mapping framework, which employs a hierarchical similarity loss to enhance the model’s structural feature learning from reference depth maps, improving the fidelity of estimated depth maps. We also integrated adversarial training and attention mechanisms to refine the depth consistency between the estimated and original depth maps. Experiments show our method surpasses current benchmarks in metrics like Threshold Accuracy, SILog, and MSE, demonstrating its robustness and potential for real-world applications, even with poor-quality inputs.

Depression is a widespread mental health issue affecting diverse age groups, with notable prevalence among college students and the elderly. However, existing datasets and detection methods primarily focus on young adults, neglecting the broader age spectrum and individual differences that influence depression manifestation. Current approaches often establish a direct mapping between multimodal data and depression indicators, failing to capture the complexity and diversity of depression across individuals. This challenge includes two tracks based on age-specific subsets: Track 1 uses the MPDD-Elderly dataset for detecting depression in older adults, and Track 2 uses the MPDD-Young dataset for detecting depression in younger participants. The Multimodal Personality-aware Depression Detection (MPDD) Challenge aims to address this gap by incorporating multimodal data alongside individual difference factors. We provide a baseline model that fuses audio and video modalities with individual difference information to detect depression manifestations in diverse populations. This challenge aims to promote the development of more personalized and accurate de pression detection methods, advancing mental health research and fostering inclusive detection systems. More details are available on the official challenge website: https://hacilab.github.io/MPDDChallenge.github.io.

This paper focuses on dynamic integrated routing in multifiber Internet protocol/wavelength-division multiplexing (IP/WDM) networks, which can be implemented through either one-step routing (OSR) or two-step routing (TSR) approach. Based on an extended layered-graph, two resource assignment strategies, termed channel-level balance (CLB) and link-level balance (LLB), are proposed to balance the traffic in the network at different levels. To further improve the performance, a parameter K is introduced to make a dynamic tradeoff between the logical-layer links and the optical-layer links. Simulation studies are carried out for various topologies. The results show that LLB is better than CLB in most cases, and LLB combined with OSR has the optimal performance. Also, we find that the routing approach and the resource assignment strategy individually play different roles with different values of r/sub l/ that is introduced to indicate the resource richness of the network. As a multifiber network is functionally equivalent to a single-fiber network with limited wavelength conversion, we investigate the effects of wavelength conversion by studying the multifiber IP/WDM networks. The analysis shows that, when the granularity of each connection request is much smaller than the wavelength granularity, wavelength conversion may increase the request blocking probability in the network.