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Early detection plays a crucial role in the treatment of depression. Therefore, numerous studies have focused on social media platforms, where individuals express their emotions, aiming to achieve early detection of depression. However, the majority of existing approaches often rely on specific features, leading to limited scalability across different types of social media datasets, such as text, images, or videos. To overcome this limitation, we introduce a Multimodal Object-Oriented Graph Attention Model (MOGAM), which can be applied to diverse types of data, offering a more scalable and versatile solution. Furthermore, to ensure that our model can capture authentic symptoms of depression, we only include vlogs from users with a clinical diagnosis. To leverage the diverse features of vlogs, we adopt a multimodal approach and collect additional metadata such as the title, description, and duration of the vlogs. To effectively aggregate these multimodal features, we employed a cross-attention mechanism. MOGAM achieved an accuracy of 0.871 and an F1-score of 0.888. Moreover, to validate the scalability of MOGAM, we evaluated its performance with a benchmark dataset and achieved comparable results with prior studies (0.61 F1-score). In conclusion, we believe that the proposed model, MOGAM, is an effective solution for detecting depression in social media, offering potential benefits in the early detection and treatment of this mental health condition., Comment: 12 pages, 3 figures, 4 tables

The extra CO2 that has already been released into the atmosphere has to be removed in order to create a world that is carbon neutral. Technologies have been created to remove carbon dioxide from wet flue gas or even directly from ambient air, however these technologies are not widely deployed yet. New generations of creative CO2 capture sorbents have been produced as a consequence of recent improvements in material assembly and surface chemistry. We summarize recent progress on water-stable and encapsulated metal-organic frameworks (MOFs) for CO2 capture under a wide range of environmental and operating conditions. In particular, newly developed water-stable MOFs and hydrophobic coating technologies are discussed with insights into their materials discovery and the synergistic effects between different components of these hybrid sorbent systems. The future perspectives and directions of water-stable and encapsulated MOFs are also given for Direct Air Capture of CO2 and CO2 capture from wet flue gas.

Motivated by the fact that the data rate of non-orthogonal multiple access (NOMA) can be greatly increased with the help of the distributed antenna system (DAS), we presents a framework in which the DAS contributes not only to the data rate but also the energy harvesting of simultaneous wireless information and power transfer (SWIPT) enabled NOMA. This study considers the sum-rate maximization problem and the max-min fairness problem for SWIPT-enabled NOMA in DAS and proposes two different schemes of power splitting and power allocation for SWIPT and NOMA, respectively, with imperfect channel state information (CSI). Numerical results validate the theoretical findings and demonstrate that the proposed framework of using SWIPT-enabled NOMA in DAS achieves the higher data rates than the existing SWIPT-enabled NOMA while guaranteeing the minimum harvested energy., Comment: 5pages, 5figures

This paper explores caching of fractions of a video content, not caching of an entire content, to increase the expected video quality. We first show that the highest-quality content is better to be cached and propose the caching policy of video chunks having different qualities. Our caching policy utilizes the characteristics of video contents that video files can be encoded into multiple versions with different qualities, each file consists of many chunks, and chunks can have different qualities. Extensive performance evaluations are conducted to show that caching of content fractions, rather than an entire content, can improve the expected video quality especially when the channel conditions is sufficiently good to cooperate with nearby BS or helpers., Comment: 5 pages, 5 figures, (will be) submitted to IEEE Wireless Communications Letter

The skip-gram (SG) model learns word representation by predicting the words surrounding a center word from unstructured text data. However, not all words in the context window contribute to the meaning of the center word. For example, less relevant words could be in the context window, hindering the SG model from learning a better quality representation. In this paper, we propose an enhanced version of the SG that leverages context information to produce word representation. The proposed model, Contextual Skip-gram, is designed to predict contextual words with both the center words and the context information. This simple idea helps to reduce the impact of irrelevant words on the training process, thus enhancing the final performance

Although deep RL models have shown a great potential for solving various types of tasks with minimal supervision, several key challenges remain in terms of learning from limited experience, adapting to environmental changes, and generalizing learning from a single task. Recent evidence in decision neuroscience has shown that the human brain has an innate capacity to resolve these issues, leading to optimism regarding the development of neuroscience-inspired solutions toward sample-efficient, and generalizable RL algorithms. We show that the computational model combining model-based and model-free control, which we term the prefrontal RL, reliably encodes the information of high-level policy that humans learned, and this model can generalize the learned policy to a wide range of tasks. First, we trained the prefrontal RL, and deep RL algorithms on 82 subjects' data, collected while human participants were performing two-stage Markov decision tasks, in which we manipulated the goal, state-transition uncertainty and state-space complexity. In the reliability test, which includes the latent behavior profile and the parameter recoverability test, we showed that the prefrontal RL reliably learned the latent policies of the humans, while all the other models failed. Second, to test the ability to generalize what these models learned from the original task, we situated them in the context of environmental volatility. Specifically, we ran large-scale simulations with 10 Markov decision tasks, in which latent context variables change over time. Our information-theoretic analysis showed that the prefrontal RL showed the highest level of adaptability and episodic encoding efficacy. This is the first attempt to formally test the possibility that computational models mimicking the way the brain solves general problems can lead to practical solutions to key challenges in machine learning.