by jsendak | Aug 21, 2024 | Computer Science
Trends in International Magnetoencephalography (MEG) Research: An Analysis
Bibliometric methods have been employed to examine trends in international Magnetoencephalography (MEG) research from 2013 to 2022. With a limited volume of domestic literature on MEG, this analysis primarily focuses on the global research landscape, providing valuable insights from the past decade as a representative sample.
Utilizing bibliometric methods to explore and analyze progress, hotspots, and developmental trends, this study takes a comprehensive approach to international MEG research since 1995 up to 2022.
Growth in MEG Research
The findings of this analysis indicate a dynamic and steady growth trend in the overall number of publications in the field of MEG. Over the years, there has been a consistent increase in the volume of research conducted on MEG, reflecting the growing interest and recognition of its potential in studying brain function.
Prolific Authors and Journals
According to the data, Ryusuke Kakigi has emerged as the most prolific author in MEG research. His contributions have played a significant role in advancing the field and shaping its future directions. As for journals, Neuroimage has been identified as the most prolific journal in publishing MEG research. Its commitment to disseminating high-quality research has established it as a leading platform for MEG-related studies.
Current Hotspots in MEG Research
The analysis highlights several current hotspots in MEG research. These hotspots include resting state, networks, functional connectivity, phase dynamics, oscillation, and more. By focusing on these areas, researchers are able to gain a deeper understanding of brain activity and its relation to various cognitive processes.
Future Trends in MEG Research
Looking ahead, MEG research is poised to make significant advancements across three key aspects.
- Disease Treatment and Practical Applications: The potential of MEG as a diagnostic and therapeutic tool for various neurological and psychiatric disorders is yet to be fully realized. Future research in this area will likely focus on utilizing MEG to improve disease treatment and enhance practical applications in healthcare settings.
- Experimental Foundations and Technical Advancements: Pushing the boundaries of MEG technology is crucial for further advancements in the field. Researchers will continue to explore and refine experimental techniques, such as combining MEG with other instruments, to expand its applications and improve data collection and analysis.
- Fundamental and Advanced Human Cognition: Understanding the complexities of human cognition is a fundamental goal of neuroscientific research. MEG offers unique insights into brain dynamics, and future studies will likely delve deeper into unraveling the intricacies of cognitive processes, both at the basic and advanced levels.
The future of MEG research holds immense potential, and the integration of MEG with other instruments will be crucial in diversifying research methodologies within this field. By collaborating and leveraging the strengths of different techniques, researchers can uncover new insights into the functioning of the human brain.
Overall, this bibliometric analysis provides a comprehensive overview of international MEG research trends, showcasing its growth, current hotspots, and future directions. As the field continues to evolve, researchers and practitioners in the MEG community can utilize these findings to shape their own contributions and drive the field forward.
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by jsendak | Aug 20, 2024 | Computer Science
arXiv:2408.08544v1 Announce Type: cross
Abstract: Sign language serves as the primary meaning of communication for the deaf-mute community. Different from spoken language, it commonly conveys information by the collaboration of manual features, i.e., hand gestures and body movements, and non-manual features, i.e., facial expressions and mouth cues. To facilitate communication between the deaf-mute and hearing people, a series of sign language understanding (SLU) tasks have been studied in recent years, including isolated/continuous sign language recognition (ISLR/CSLR), gloss-free sign language translation (GF-SLT) and sign language retrieval (SL-RT). Sign language recognition and translation aims to understand the semantic meaning conveyed by sign languages from gloss-level and sentence-level, respectively. In contrast, SL-RT focuses on retrieving sign videos or corresponding texts from a closed-set under the query-by-example search paradigm. These tasks investigate sign language topics from diverse perspectives and raise challenges in learning effective representation of sign language videos. To advance the development of sign language understanding, exploring a generalized model that is applicable across various SLU tasks is a profound research direction.
Advances in Sign Language Understanding: A Multi-disciplinary Perspective
Sign language serves as the primary means of communication for the deaf-mute community, conveying information through a combination of manual and non-manual features such as hand gestures, body movements, facial expressions, and mouth cues. In recent years, there has been a growing interest in developing sign language understanding (SLU) systems to facilitate communication between the deaf-mute and hearing individuals.
The Multi-disciplinary Nature of Sign Language Understanding
Sign language understanding involves multiple disciplines, including linguistics, computer vision, machine learning, and multimedia information systems. Linguistics provides insights into the structure and grammar of sign languages, helping researchers design effective representations for capturing the semantic meaning conveyed by sign languages.
Computer vision and machine learning techniques are essential for analyzing the visual features of sign language videos. These techniques enable the extraction of hand gestures, body movements, and facial expressions from video sequences, which are then used for recognition, translation, or retrieval tasks. Additionally, these disciplines contribute to the development of computer vision algorithms capable of understanding sign language in real-time or near real-time scenarios.
Multimedia information systems play a crucial role in sign language understanding, providing platforms for creating, storing, and retrieving sign language videos. These systems also enable the integration of additional multimedia modalities, such as text or audio, to enhance the comprehension of sign language content. Furthermore, multimedia information systems enable the creation of sign language databases, which are essential for training and evaluating SLU models.
Sign Language Understanding Tasks
Several sign language understanding tasks have been studied in recent years, each addressing different aspects of sign language communication:
- Isolated/Continuous Sign Language Recognition (ISLR/CSLR): These tasks focus on recognizing hand gestures and body movements in isolated signs or continuous sign sequences. By analyzing the visual features extracted from sign language videos, ISLR and CSLR aim to understand the meaning conveyed by individual signs or complete sentences.
- Gloss-free Sign Language Translation (GF-SLT): Unlike traditional sign language translation, which maps individual signs to spoken language words, GF-SLT aims to directly translate sign language videos into the target language without relying on gloss-level annotations. This task requires the development of advanced machine learning models capable of handling the structural complexity of sign languages.
- Sign Language Retrieval (SL-RT): SL-RT focuses on retrieving sign videos or corresponding texts from a closed-set based on examples provided by the user. This task enables efficient access to sign language content, allowing individuals to search for specific signs or sentences in sign language databases.
Challenges and Future Directions
Developing a generalized model that is applicable across various sign language understanding tasks poses significant challenges. One key challenge is designing effective representations that capture the rich semantic information present in sign language videos. This requires incorporating both manual and non-manual features, as well as considering the temporal dynamics of sign language.
Another challenge is the lack of large-scale annotated sign language datasets. Training deep learning models for sign language understanding often requires vast amounts of labeled data. However, the creation of such datasets is time-consuming and requires expert annotation. Addressing this challenge requires innovative solutions, such as leveraging weakly supervised or unsupervised learning methods for sign language understanding.
In conclusion, sign language understanding is a multi-disciplinary field that combines knowledge from linguistics, computer vision, machine learning, and multimedia information systems. Advancing the state-of-the-art in sign language understanding requires collaboration and contributions from these diverse disciplines. By addressing the challenges and exploring new directions, we can pave the way for improved communication and inclusivity for the deaf-mute community.
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by jsendak | Aug 20, 2024 | Computer Science
The recent wave of foundation models has revolutionized the field of computer vision, and the segment anything model (SAM) has emerged as a particularly noteworthy advancement. SAM has not only showcased remarkable zero-shot generalization, but its applications have transcended traditional paradigms in computer vision, extending to image segmentation, multi-modal segmentation, and even the video domain.
While existing surveys have delved into SAM’s applications in image processing, there is a noticeable absence of a comprehensive review in the video domain. To bridge this gap, this work conducts a systematic review on SAM for videos in the era of foundation models. By focusing on its recent advances and discussing its applications in various tasks, this review sheds light on the opportunities for developing foundation models in the video domain.
Background: SAM and Video-related Research Domains
In order to provide readers with a clear understanding of SAM and its relevance to the video domain, this review starts with a brief introduction to the background of SAM and its applications in video-related research domains. By doing so, readers can grasp the context and significance of SAM in the broader field of computer vision.
Taxonomy of SAM Methods in Video Domain
In order to provide a structured analysis of SAM methods in the video domain, this review categorizes existing methods into three key areas: video understanding, video generation, and video editing. By organizing the methods into these categories, a clear framework is established to analyze and summarize the advantages and limitations of each approach. This taxonomy serves as a valuable resource for researchers and practitioners seeking to navigate the landscape of SAM methods in the video domain.
Comparative Analysis and Benchmarks
In order to assess the performance of SAM-based methods in comparison to the current state-of-the-art, this review provides a comprehensive analysis of comparative results on representative benchmarks. By evaluating the performance of SAM-based methods against existing approaches, readers gain insights into the strengths and weaknesses of SAM in the video domain. This comparative analysis contributes to the development of benchmarks and establishes a baseline for future research and advancements.
Challenges and Future Research Directions
While SAM has shown immense promise and achieved impressive results in the video domain, there are still challenges that need to be addressed. This review discusses the challenges faced by current research in SAM for videos and outlines several future research directions. By pinpointing the existing gaps and envisioning future possibilities, this review acts as a catalyst for further advancements and innovation in the field.
In conclusion, this systematic review of SAM for videos in the era of foundation models addresses a notable gap in the existing literature. By providing a comprehensive analysis of SAM’s applications, comparative results, and future research directions, this review serves as a valuable resource for researchers, practitioners, and enthusiasts interested in the intersection of computer vision, foundation models, and the video domain.
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by jsendak | Aug 17, 2024 | Computer Science
arXiv:2408.07791v1 Announce Type: new
Abstract: We demonstrate the efficiencies and explanatory abilities of extensions to the common tools of Autoencoders and LLM interpreters, in the novel context of comparing different cultural approaches to the same international news event. We develop a new Convolutional-Recurrent Variational Autoencoder (CRVAE) model that extends the modalities of previous CVAE models, by using fully-connected latent layers to embed in parallel the CNN encodings of video frames, together with the LSTM encodings of their related text derived from audio. We incorporate the model within a larger system that includes frame-caption alignment, latent space vector clustering, and a novel LLM-based cluster interpreter. We measure, tune, and apply this system to the task of summarizing a video into three to five thematic clusters, with each theme described by ten LLM-produced phrases. We apply this system to two news topics, COVID-19 and the Winter Olympics, and five other topics are in progress.
Extending Autoencoders and LLM Interpreters for Cross-Cultural News Analysis
In this study, the researchers showcase the effectiveness of advanced techniques in the field of multimedia information systems for comparing different cultural approaches to international news events. By building on the concepts of Autoencoders and LLM interpreters, the authors propose a Convolutional-Recurrent Variational Autoencoder (CRVAE) model that enhances the capabilities of previous models.
The novelty of the CRVAE model lies in its ability to incorporate both video frames and related textual information derived from audio. By using fully-connected latent layers, the model generates embeddings of CNN encodings for video frames and LSTM encodings for textual content simultaneously.
The authors integrate this model into a larger system that encompasses frame-caption alignment, latent space vector clustering, and a unique LLM-based cluster interpreter. This comprehensive system aims to summarize videos into three to five clusters based on different themes, with each theme described by ten phrases generated by the LLM.
The researchers have successfully applied this system to two news topics, namely COVID-19 and the Winter Olympics, with promising results. They are currently working on extending their analysis to five other topics, which demonstrates the potential of their approach for studying cross-cultural perspectives on various news events.
The content of this article underscores the multi-disciplinary nature of multimedia information systems. It brings together concepts from areas such as video processing (CNN), natural language processing (LSTM), latent space analysis, and cluster interpretation. By combining these techniques, the researchers are able to analyze news events from different cultural perspectives efficiently and provide valuable insights.
This article is particularly relevant to the fields of animations, artificial reality, augmented reality, and virtual realities. The advancements in multimedia information systems showcased here can be leveraged to enhance the immersive experiences offered by these technologies. For example, by analyzing cross-cultural news events, developers can create more contextually relevant and culturally sensitive animations or virtual reality experiences. Furthermore, the integration of audio, video, and textual information in the CRVAE model aligns with the goal of creating more realistic and interactive artificial and augmented reality environments.
In conclusion, the study presented in this article demonstrates the power of extending Autoencoders and LLM interpreters for analyzing cross-cultural news events. Its multidisciplinary approach brings together various concepts from multimedia information systems and is highly relevant to the wider fields of animations, artificial reality, augmented reality, and virtual realities. This research opens up exciting possibilities for developing more immersive and culturally aware multimedia experiences.
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by jsendak | Aug 17, 2024 | Computer Science
Enhancing Interpersonal Emotion Regulation on Online Platforms
Interpersonal communication has become a vital aspect of how people manage their emotions, particularly in the digital age. Social media and online content consumption have been found to play a significant role in regulating emotions and seeking support for rest and recovery. However, these platforms were not originally designed with emotion regulation in mind, which limits their effectiveness in this regard. To address this issue, a new approach is proposed to enhance Interpersonal Emotion Regulation (IER) on online platforms through content recommendation.
The objective of this approach is to empower users to regulate their emotions while actively or passively engaging in online platforms. This is achieved by crafting media content that aligns with IER strategies, particularly empathic responding. By incorporating empathic recommendations into the content recommendation system, users are given a more personalized experience that aids in their emotional regulation efforts.
This proposed recommendation system aims to blend both system-initiated and user-initiated emotion regulation, creating an environment that allows for real-time IER practices on digital media platforms. By leveraging user activity and preferences, the system can generate empathic recommendations that are tailored to individual needs and preferences, resulting in a more effective emotion regulation experience.
Evaluating the Efficacy
To assess the effectiveness of this approach, a mixed-method research design is utilized. The research design includes the analysis of text-based social media data and a user survey. By collecting 37.5K instances of user posts and interactions on Reddit over a year, researchers have been able to gain insights into how users engage with digital media platforms for emotion regulation.
The collected data is used to design a Contextual Multi-Armed Bandits (CMAB) based recommendation system. This system utilizes features from user activity and preferences to generate empathic recommendations. Through experimentation, it has been found that these empathic recommendations are preferred by users over widely accepted emotion regulation strategies such as distraction and avoidance.
The Role of Digital Applications
Digital applications have played a crucial role in facilitating the process of digital emotion regulation. The widespread recognition of digital media applications for Digital Emotion Regulation (DER) has paved the way for advancements in this field. The proposed recommendation system builds upon this recognition and aims to further enhance the effectiveness of digital applications in supporting emotion regulation.
By leveraging the power of digital platforms and incorporating empathic recommendations, users can have a more personalized and supportive experience. This not only benefits individuals in managing their emotions but also has potential implications for mental health and well-being at a broader societal level.
In conclusion, the proposed approach to enhance Interpersonal Emotion Regulation (IER) on online platforms through content recommendation holds great promise. By incorporating empathic recommendations into the recommendation system, users can have a more effective and personalized emotion regulation experience. Further research and development in this area will likely yield valuable insights and innovations, ultimately enabling users to better manage their emotions in the digital realm.
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by jsendak | Aug 16, 2024 | Computer Science
arXiv:2408.07349v1 Announce Type: cross
Abstract: The increasing prevalence of retinal diseases poses a significant challenge to the healthcare system, as the demand for ophthalmologists surpasses the available workforce. This imbalance creates a bottleneck in diagnosis and treatment, potentially delaying critical care. Traditional methods of generating medical reports from retinal images rely on manual interpretation, which is time-consuming and prone to errors, further straining ophthalmologists’ limited resources. This thesis investigates the potential of Artificial Intelligence (AI) to automate medical report generation for retinal images. AI can quickly analyze large volumes of image data, identifying subtle patterns essential for accurate diagnosis. By automating this process, AI systems can greatly enhance the efficiency of retinal disease diagnosis, reducing doctors’ workloads and enabling them to focus on more complex cases. The proposed AI-based methods address key challenges in automated report generation: (1) Improved methods for medical keyword representation enhance the system’s ability to capture nuances in medical terminology; (2) A multi-modal deep learning approach captures interactions between textual keywords and retinal images, resulting in more comprehensive medical reports; (3) Techniques to enhance the interpretability of the AI-based report generation system, fostering trust and acceptance in clinical practice. These methods are rigorously evaluated using various metrics and achieve state-of-the-art performance. This thesis demonstrates AI’s potential to revolutionize retinal disease diagnosis by automating medical report generation, ultimately improving clinical efficiency, diagnostic accuracy, and patient care. [https://github.com/Jhhuangkay/DeepOpht-Medical-Report-Generation-for-Retinal-Images-via-Deep-Models-and-Visual-Explanation]
The Role of Artificial Intelligence in Automating Medical Report Generation for Retinal Images
The increasing prevalence of retinal diseases presents a significant challenge to the healthcare system, as the demand for ophthalmologists exceeds the available workforce. This creates a bottleneck in diagnosis and treatment, leading to potential delays in critical care. In this context, the use of Artificial Intelligence (AI) shows promise in automating medical report generation for retinal images, thereby improving the efficiency of diagnosis and reducing the workload of doctors.
One of the primary advantages of AI is its ability to quickly analyze large volumes of image data and identify subtle patterns that are essential for accurate diagnosis. By automating the process of medical report generation, AI systems can significantly enhance the efficiency of diagnosing retinal diseases. This automation enables doctors to focus on more complex cases and allocate their limited resources more effectively.
This thesis explores the potential of AI in revolutionizing retinal disease diagnosis by automating medical report generation. The proposed AI-based methods address several key challenges in this regard:
- Improved methods for medical keyword representation: By enhancing the system’s ability to capture nuances in medical terminology, these methods improve the accuracy of AI-generated medical reports. This is crucial in ensuring that the reports accurately reflect the nuances and complexities of retinal diseases.
- Multi-modal deep learning approach: This approach captures interactions between textual keywords and retinal images, resulting in more comprehensive medical reports. By considering both the visual information from the retinal images and the textual information from medical keywords, the AI system can generate more accurate and informative reports.
- Techniques to enhance interpretability: It is essential for AI-based report generation systems to be transparent and interpretable in a clinical setting. This fosters trust and acceptance among clinicians, enabling them to understand and validate the generated reports. By incorporating techniques for visual explanation, the proposed methods enhance the interpretability of the AI system.
The evaluation of these AI-based methods using various metrics demonstrates their state-of-the-art performance. By leveraging AI, retinal disease diagnosis can be transformed, leading to improved clinical efficiency, diagnostic accuracy, and patient care.
The Multidisciplinary Nature and Relation to Multimedia Information Systems
The concept of automating medical report generation for retinal images through AI is a prime example of the multidisciplinary nature of multimedia information systems. This field combines aspects of computer science, medical imaging, and artificial intelligence to develop solutions that efficiently handle and process multimedia data, such as images and videos.
Multimedia information systems have evolved to meet the increasing demand for efficient management and analysis of diverse types of data. In the case of retinal images, AI-based systems leverage deep learning techniques to extract relevant features and patterns, enabling accurate diagnosis and automated report generation.
Additionally, the integration of AI with retinal image analysis aligns with developments in the broader fields of artificial reality, augmented reality, and virtual realities. These fields aim to create immersive and interactive experiences by combining virtual and real-world elements.
The application of AI in retinal disease diagnosis can contribute to the development of augmented reality systems, where AI-generated medical reports are overlaid directly onto the retinal images. This would provide ophthalmologists with real-time, context-specific information during diagnosis and treatment, enhancing their decision-making process.
In summary, the use of AI in automating medical report generation for retinal images has the potential to revolutionize retinal disease diagnosis. By addressing key challenges and leveraging multidisciplinary concepts from multimedia information systems, artificial reality, augmented reality, and virtual realities, AI systems can enhance clinical efficiency, diagnostic accuracy, and patient care in ophthalmology.
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