by jsendak | Apr 30, 2025 | Computer Science
arXiv:2504.18799v1 Announce Type: new
Abstract: Multimodal music emotion recognition (MMER) is an emerging discipline in music information retrieval that has experienced a surge in interest in recent years. This survey provides a comprehensive overview of the current state-of-the-art in MMER. Discussing the different approaches and techniques used in this field, the paper introduces a four-stage MMER framework, including multimodal data selection, feature extraction, feature processing, and final emotion prediction. The survey further reveals significant advancements in deep learning methods and the increasing importance of feature fusion techniques. Despite these advancements, challenges such as the need for large annotated datasets, datasets with more modalities, and real-time processing capabilities remain. This paper also contributes to the field by identifying critical gaps in current research and suggesting potential directions for future research. The gaps underscore the importance of developing robust, scalable, a interpretable models for MMER, with implications for applications in music recommendation systems, therapeutic tools, and entertainment.
Expert Commentary: Multimodal Music Emotion Recognition in the Context of Multimedia Information Systems and Virtual Realities
Music holds great emotional power, and understanding and predicting the emotions it evokes is a fascinating and important area of research. The emerging discipline of Multimodal Music Emotion Recognition (MMER) aims to leverage multiple modalities such as audio, lyrics, gestures, and physiological signals to recognize and predict the emotional content of music. This survey paper provides a comprehensive overview of the current state-of-the-art in MMER, shedding light on the various approaches and techniques used in this field.
The field of MMER intersects with several other domains, making it a truly multi-disciplinary subject. Multimedia Information Systems, for instance, play a significant role in MMER by providing the infrastructure and tools to handle and analyze large volumes of multimodal music data. The techniques discussed in this survey, such as feature extraction and processing, are fundamental to extracting relevant information from music and its associated modalities. These techniques are shared with other fields, such as Speech and Image Processing, highlighting the cross-pollination of knowledge and methodologies.
Furthermore, Animations, Artificial Reality, Augmented Reality, and Virtual Realities are all related to MMER. These technologies offer new ways to experience and interact with music, providing additional modalities for MMER. For example, in Virtual Reality environments, users can be fully immersed in a musical experience and their physiological signals and gestures can be captured, enhancing the multimodal data available for emotion recognition. By incorporating these technologies, MMER can have practical applications in areas such as interactive entertainment, virtual music therapy, and even music recommendation systems that can generate personalized playlists based on the user’s emotional state.
The survey paper highlights the advancements in deep learning methods in MMER. Deep learning algorithms, such as Convolutional Neural Networks (CNNs) and Recurrent Neural Networks (RNNs), have shown remarkable performance in various domains, and their application in MMER has yielded promising results. Deep learning allows for the automatic extraction of relevant features from music and other modalities, reducing the need for manual feature engineering. However, it is important to mention that large annotated datasets are still required to train these models effectively, and creating such datasets can be a laborious and resource-intensive task.
The paper also emphasizes the increasing importance of feature fusion techniques in MMER. As the field progresses, researchers are moving towards combining information from multiple modalities to improve emotion recognition accuracy. Fusion techniques such as early fusion, late fusion, and hybrid fusion are discussed in the paper, each with its advantages and trade-offs. The choice of fusion technique depends on the specific requirements of the application and the available data. This trend towards multimodal fusion reflects the realization that a holistic understanding of music emotions requires the integration of information from different sources.
Despite the advancements in MMER, several challenges still need to be addressed. The need for large annotated datasets that cover a wide range of music genres, emotions, and demographic diversity is one significant challenge. Building such datasets is crucial for developing robust and generalizable MMER models. Additionally, the field would benefit from datasets with more modalities, including visual and physiological signals, as they can provide richer information for emotion recognition. Furthermore, real-time processing capabilities are essential for practical applications of MMER, such as interactive music systems. Developing efficient and scalable algorithms to handle real-time multimodal music data is a direction that future research should aim to pursue.
In conclusion, this survey paper provides a comprehensive overview of MMER, its current state-of-the-art, and potential avenues for future research. The multi-disciplinary nature of MMER, with its connections to Multimedia Information Systems, Animations, Artificial Reality, Augmented Reality, and Virtual Realities, opens up exciting possibilities for understanding and harnessing the emotional power of music.
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by jsendak | Apr 30, 2025 | AI
arXiv:2504.18572v1 Announce Type: new
Abstract: Large Language Models have demonstrated remarkable capabilities in natural language processing, yet their decision-making processes often lack transparency. This opaqueness raises significant concerns regarding trust, bias, and model performance. To address these issues, understanding and evaluating the interpretability of LLMs is crucial. This paper introduces a standardised benchmarking technique, Benchmarking the Explainability of Large Language Models, designed to evaluate the explainability of large language models.
Introduction
Large Language Models (LLMs) have revolutionized natural language processing with their impressive capabilities. They are capable of understanding, generating, and translating text with remarkable accuracy. However, the lack of transparency in their decision-making processes raises concerns about trust, bias, and model performance. To address these issues, it is crucial to understand and evaluate the interpretability of LLMs.
Importance of Explainability
Explainability refers to the ability to understand and interpret the decision-making process of a machine learning model. As LLMs are deployed in various real-world applications, such as chatbots, customer service, and content generation, it becomes essential to ensure transparency and accountability.
One major concern with LLMs is the potential bias present in their outputs. Without a clear understanding of how these models arrive at their decisions, it becomes challenging to identify and rectify any biases that may exist. Additionally, the ability to explain model decisions helps in building trust and acceptance among users and stakeholders.
Benchmarking the Explainability of LLMs
This paper introduces a standardized benchmarking technique called Benchmarking the Explainability of Large Language Models. This technique aims to evaluate the explainability of LLMs and provide a common framework for comparing different models.
The benchmarking technique involves measuring the model’s ability to provide meaningful explanations for its decisions. This can be done through various methods, such as generating saliency maps that highlight important words or phrases in the input text, providing step-by-step reasoning for the output, or generating counterfactual explanations to understand how the model’s output would change with different inputs.
By benchmarking the explainability of LLMs, researchers and practitioners can gain insights into the strengths and weaknesses of different models and develop strategies to improve the interpretability of these models.
Multi-Disciplinary Nature of Explainability
The concept of explainability in LLMs is multi-disciplinary, involving expertise from various fields. Linguists and language experts can contribute insights into the quality of generated explanations and identify linguistic patterns that contribute to explainability.
From a machine learning perspective, researchers can develop techniques to extract and visualize important information from LLMs, making the decision-making process more interpretable. Additionally, experts in ethics and fairness can provide guidance on identifying and mitigating biases in LLMs.
The collaboration between these disciplines is crucial to achieving meaningful progress in evaluating and enhancing the explainability of LLMs.
The Future of Explainability in LLMs
As LLMs continue to evolve and become more powerful, the need for explainability becomes increasingly important. Future research in this field should focus on developing more sophisticated and comprehensive benchmarking techniques that cover a wide range of interpretability aspects.
Furthermore, efforts should be made to improve the transparency of LLMs by incorporating explainability as a core component during the model training process. This would enable models to provide meaningful explanations by default, increasing trust and reducing bias.
With advancements in explainability, LLMs have the potential to become more trustworthy and reliable in a wide range of real-world applications. However, it is essential to address the challenges associated with explainability to ensure that these models are accountable and fair.
Conclusion
The lack of transparency in the decision-making processes of Large Language Models raises concerns regarding trust, bias, and model performance. To address these concerns, it is crucial to evaluate and enhance the explainability of these models. The introduction of the standardized benchmarking technique, Benchmarking the Explainability of Large Language Models, provides a common framework for evaluating and comparing the explainability of LLMs. This multi-disciplinary effort involving linguists, machine learning researchers, and ethics experts is essential for advancing the field of explainability in LLMs. The future of LLMs lies in their ability to provide meaningful explanations, improving trust, and reducing bias.
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by jsendak | Apr 30, 2025 | GR & QC Articles
arXiv:2504.18607v1 Announce Type: new
Abstract: This study presents new spherically symmetric and dynamical wormhole solutions supported by ordinary matter modeled as an anisotropic fluid, exhibiting a traversable nature. To achieve this goal, we adopt different approaches to obtain both evolving static and genuinely dynamical solutions, such as imposing a viable condition on the Ricci scalar, considering an anisotropic equation of state, and choosing a suitable energy density profile. For each derived shape function, we analyze the corresponding $2D$ and $3D$ embedding diagrams and verify their compatibility with the weak energy condition through density plots. The equilibrium conditions are also explored graphically to assess the stability of the obtained solutions, which are shown to be stable within the analyzed framework. Additionally, we investigate the complexity factor associated with each configuration, examining its dependence on both temporal evolution and the coupling parameter $lambda$ of the $f(R,T)$ theory.
In this study, new spherically symmetric and dynamical wormhole solutions are presented, which are supported by ordinary matter modeled as an anisotropic fluid. These wormholes are found to be traversable, meaning that they could potentially be used for interstellar travel.
To achieve these solutions, different approaches are adopted. First, a viable condition on the Ricci scalar is imposed. Then, an anisotropic equation of state is considered, and a suitable energy density profile is chosen.
The derived shape functions are analyzed in both 2D and 3D embedding diagrams. The density plots of these diagrams verify their compatibility with the weak energy condition, which is an important requirement for traversable wormholes.
The stability of the obtained solutions is also explored. Graphical analysis of the equilibrium conditions shows that these solutions are stable within the analyzed framework. This is an encouraging result, as stability is crucial for the practical implementation of wormholes.
Furthermore, the complexity factor associated with each configuration is investigated. The dependence of this factor on both temporal evolution and the coupling parameter λ of the f(R,T) theory is examined. This analysis provides insights into the behavior and properties of the wormhole solutions.
Future Roadmap
The findings of this study open up several opportunities for future research and development in the field of wormhole physics. Here is a potential roadmap for readers interested in exploring these opportunities:
- Further investigate the stability of the obtained wormhole solutions by considering perturbations and analyzing their effects. This can provide a more comprehensive understanding of the long-term behavior and viability of these wormholes.
- Explore the implications of the anisotropic equation of state and energy density profile on the physical properties of the wormholes. This can help refine the modeling of the wormhole matter and potentially lead to the discovery of new phenomena.
- Investigate the possibility of constructing wormholes with different shapes and geometries. The current study focuses on spherically symmetric solutions, but there may be other configurations that can exhibit traversable properties.
- Examine the effects of different matter models on the stability and traversability of wormholes. This can involve considering different types of fluids or even exotic matter, which could lead to new insights and potential breakthroughs.
- Extend the analysis to higher-dimensional wormholes. The current study focuses on 2D and 3D embedding diagrams, but there may be interesting and novel properties that emerge in higher dimensions.
- Consider the implications of the obtained wormhole solutions for practical applications, such as interstellar travel. This can involve studying the energy requirements, potential constraints, and engineering challenges associated with utilizing these wormholes.
Overall, the study presents exciting possibilities for the exploration of wormholes and their potential utilization for space travel. Future research in this field can contribute to our understanding of fundamental physics and open up new frontiers for human exploration.
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by jsendak | Apr 30, 2025 | Computer Science
Analysis: Asymmetric numeral systems (ANS) have gained significant attention in recent years due to their high compression efficiency and low computational complexity. This article presents several algorithms for generating tables for ANS, with a focus on optimizing the discrepancy and entropy loss.
Discrepancy refers to how well the generated tables distribute the probability mass across different symbols. Lower discrepancy values indicate better distribution, leading to more efficient compression. The article claims that the presented algorithms are optimal in terms of discrepancy, which is a significant achievement in ANS research.
The optimization of entropy loss is another crucial aspect discussed in the article. Entropy loss refers to the difference between the theoretical entropy of a data source and the compressed representation using ANS. Minimizing entropy loss is essential to ensure that the compressed data retains as much information as possible.
The article also introduces improved theoretical bounds for entropy loss in tabled ANS. These bounds provide a better understanding of the expected compression performance and can guide future research in optimizing ANS algorithms.
In addition to the theoretical analysis, the article includes a brief empirical evaluation of the stream variant of ANS. Empirical evaluations are crucial to validate the theoretical claims and assess the performance of the proposed algorithms in practice.
Expert Insights:
The presented algorithms for generating tables in ANS are indeed a significant contribution to the field. Optimizing discrepancy and entropy loss is a crucial step in improving the compression efficiency of ANS. By providing algorithms that are proven to be optimal in terms of discrepancy, the article enables researchers and practitioners to achieve state-of-the-art compression performance.
The improved theoretical bounds for entropy loss also enhance our understanding of ANS and its limitations. These bounds can guide future research in developing new algorithms or refining existing ones to further minimize entropy loss and improve compression performance.
The empirical evaluation of the stream variant of ANS complements the theoretical analysis by demonstrating the real-world performance of the proposed algorithms. This evaluation allows us to assess the practical impact of the algorithms and provides insights into their suitability for different types of data sources.
Overall, this article contributes to the advancement of ANS by presenting optimized algorithms for table generation and offering improved theoretical bounds for entropy loss. The combination of theoretical analysis and empirical evaluation strengthens the credibility of the findings and sets a foundation for future research in ANS compression.
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by jsendak | Apr 30, 2025 | Art
Potential Future Trends in Art: A Look at Ivana Ivković’s Work
Ivana Ivković, a Serbian artist born in 1979, has garnered attention for her unique approach to art. Her multidisciplinary practice, which includes drawing, photography, installation, and delegated performance, revolves around the themes of vulnerability, resistance, power, trust, foreshadowing, and collapse. Ivković’s work transcends time, and in moments of upheaval, she believes that art watches and waits. In this article, we will explore the potential future trends related to these themes and make predictions and recommendations for the industry.
1. Embracing Vulnerability
One future trend we can expect to see is a greater emphasis on embracing vulnerability in art. Ivković’s work highlights the power of vulnerability as a means of connecting with the audience on a deep emotional level. As society becomes more open to discussions surrounding mental health and emotional well-being, artists will likely use vulnerability as a tool to convey authenticity and raw emotions. This trend will pave the way for more intimate and personal art experiences that resonate with a broader audience.
2. Exploring Resistance
Ivković’s exploration of resistance raises important questions about societal norms, power structures, and individual agency. In the future, we can expect artists to continue pushing the boundaries and challenging the status quo through their work. Art will serve as a platform for dissent and resistance, amplifying voices that challenge injustice and inequality. This trend will encourage artists to be more politically engaged and actively participate in shaping public discourse.
3. Incorporating Technology
The use of technology in art is already prevalent, but we can expect it to become even more integrated in the future. Ivković’s multidisciplinary practice hints at the potential of combining different mediums and technologies to create immersive and interactive experiences. Virtual reality, augmented reality, and artificial intelligence will play a significant role in shaping the future of art. Artists will leverage these tools to push the boundaries of creativity and enhance the viewer’s engagement.
4. Collaboration and Delegated Performance
Ivković often incorporates delegated performance in her work, bringing together different individuals to create a collective experience. This approach fosters collaboration and blurs the lines between the artist and the audience. In the future, we can expect more artists to adopt similar collaborative techniques, embracing the power of collective creativity. This trend will fundamentally transform the traditional artist-audience dynamic, encouraging participation and co-creation.
Predictions
Based on these key points, we can make the following predictions for the future trends in the art industry:
- Art will become more emotionally impactful by embracing vulnerability and exploring personal narratives.
- Artists will continue to challenge the status quo and use their work as a means of resistance against injustice and inequality.
- Technology will play a crucial role in revolutionizing the art experience through virtual reality, augmented reality, and artificial intelligence.
- Collaboration and delegated performance will become more prevalent, blurring the boundaries between the artist and the audience.
Recommendations
Considering these potential future trends, it is important for artists and industry professionals to adapt and embrace change. To thrive in this evolving landscape, the following recommendations are suggested:
- Artists should strive for authenticity and rawness in their work, embracing vulnerability as a means of connecting with their audience on a deeper level.
- Art organizations and institutions should actively support artists who challenge societal norms and promote inclusivity and diverse perspectives.
- Investments in technology and digital platforms should be prioritized to enhance the art experience and reach a wider audience.
- Collaboration should be encouraged, fostering a sense of community and collective creativity.
In conclusion, Ivana Ivković’s work serves as a glimpse into the potential future trends in the art industry. Embracing vulnerability, exploring resistance, incorporating technology, and fostering collaboration are key themes that will shape the future of art. By understanding and adapting to these trends, artists and industry professionals can thrive in an ever-evolving landscape.
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