by jsendak | Feb 23, 2024 | Computer Science
arXiv:2402.14326v1 Announce Type: new
Abstract: Offloading computing to edge servers is a promising solution to support growing video understanding applications at resource-constrained IoT devices. Recent efforts have been made to enhance the scalability of such systems by reducing inference costs on edge servers. However, existing research is not directly applicable to pixel-level vision tasks such as video semantic segmentation (VSS), partly due to the fluctuating VSS accuracy and segment bitrate caused by the dynamic video content. In response, we present Penance, a new edge inference cost reduction framework. By exploiting softmax outputs of VSS models and the prediction mechanism of H.264/AVC codecs, Penance optimizes model selection and compression settings to minimize the inference cost while meeting the required accuracy within the available bandwidth constraints. We implement Penance in a commercial IoT device with only CPUs. Experimental results show that Penance consumes a negligible 6.8% more computation resources than the optimal strategy while satisfying accuracy and bandwidth constraints with a low failure rate.
Analysis of Penance: Edge Inference Cost Reduction Framework
In this article, the authors introduce Penance, a new framework for reducing edge inference costs in video semantic segmentation (VSS) tasks. With the growing demand for video understanding applications on resource-constrained IoT devices, offloading computing to edge servers has become a promising solution. However, existing research is not directly applicable to pixel-level vision tasks like VSS, mainly due to the dynamic nature of video content, which leads to fluctuating accuracy and segment bitrate.
Penance addresses this challenge by leveraging the softmax outputs of VSS models and the prediction mechanism of H.264/AVC codecs. By optimizing model selection and compression settings, Penance aims to minimize the inference cost while meeting the required accuracy within the available bandwidth constraints. It is worth noting that Penance is implemented on a commercial IoT device with only CPUs, making it accessible to a wide range of devices.
The multi-disciplinary nature of this work is evident in its integration of computer vision (specifically VSS), video codecs (H.264/AVC), and edge computing. It combines knowledge from these diverse domains to develop a novel solution that addresses the specific challenges faced in edge inference for VSS.
When considering the wider field of multimedia information systems, Penance contributes to the efficiency and scalability of video understanding applications on IoT devices. By reducing inference costs at the edge, it enables resource-constrained devices to perform complex vision tasks like semantic segmentation without relying heavily on cloud resources. This can lead to improved response times, reduced latency, and increased privacy.
Furthermore, Penance has relevance to various aspects of multimedia technologies such as animations, artificial reality, augmented reality, and virtual realities. These technologies often involve real-time video processing and analysis, where efficient edge inference is crucial for a seamless and immersive user experience. By optimizing inference costs, Penance can support the delivery of rich multimedia content in these applications without compromising on performance.
In conclusion, Penance is an innovative framework that addresses the challenges of edge inference for video semantic segmentation tasks. Its integration of various technologies and its impact on the wider field of multimedia information systems, animations, artificial reality, augmented reality, and virtual realities make it a significant contribution to the advancement of edge computing in the context of video understanding applications.
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by jsendak | Feb 23, 2024 | AI
arXiv:2402.14083v1 Announce Type: new
Abstract: While Transformers have enabled tremendous progress in various application settings, such architectures still lag behind traditional symbolic planners for solving complex decision making tasks. In this work, we demonstrate how to train Transformers to solve complex planning tasks and present Searchformer, a Transformer model that optimally solves previously unseen Sokoban puzzles 93.7% of the time, while using up to 26.8% fewer search steps than standard $A^*$ search. Searchformer is an encoder-decoder Transformer model trained to predict the search dynamics of $A^*$. This model is then fine-tuned via expert iterations to perform fewer search steps than $A^*$ search while still generating an optimal plan. In our training method, $A^*$’s search dynamics are expressed as a token sequence outlining when task states are added and removed into the search tree during symbolic planning. In our ablation studies on maze navigation, we find that Searchformer significantly outperforms baselines that predict the optimal plan directly with a 5-10$times$ smaller model size and a 10$times$ smaller training dataset. We also demonstrate how Searchformer scales to larger and more complex decision making tasks like Sokoban with improved percentage of solved tasks and shortened search dynamics.
Transformers in Complex Decision Making Tasks
In recent years, Transformers have gained popularity and achieved remarkable success in various application settings. However, when it comes to complex decision-making tasks, traditional symbolic planners still outperform Transformer architectures. This article introduces a novel approach to training Transformers for solving complex planning tasks, demonstrating the potential for these architectures to bridge the gap and excel in this domain.
Introducing Searchformer
The authors present Searchformer, a Transformer model specifically designed to solve previously unseen Sokoban puzzles. Impressively, Searchformer achieves optimal solutions 93.7% of the time while employing up to 26.8% fewer search steps than the standard $A^*$ search algorithm.
To achieve this, Searchformer is constructed as an encoder-decoder Transformer model that is initially trained to predict the search dynamics of $A^*$, a widely-used symbolic planning algorithm. This pre-training phase allows Searchformer to gain an understanding of the underlying search process. Subsequently, the model undergoes fine-tuning through expert iterations, aiming to generate optimal plans while minimizing the number of search steps required.
The Training Method
The training method employed in this work involves expressing $A^*$’s search dynamics as a token sequence that outlines the addition and removal of task states in the search tree during symbolic planning. By framing the training in this way, Searchformer learns to effectively predict the optimal plan with fewer search steps. The results of ablation studies on maze navigation demonstrate the superiority of Searchformer over baselines that directly predict the optimal plan.
Multi-disciplinary Nature
This research showcases the multi-disciplinary nature of the concepts involved. By combining ideas from natural language processing and symbolic planning, the authors have created a Transformer architecture that excels in complex decision-making tasks. This highlights the importance of integrating knowledge from different domains to push the boundaries of what Transformers can achieve.
Scaling to Larger Tasks
Another notable aspect of Searchformer is its ability to scale to larger and more complex decision-making tasks like Sokoban. The model exhibits improved percentages of solved tasks and shorter search dynamics, further emphasizing the potential of Transformers in this domain. With its capability to handle larger problems, Searchformer opens up avenues for applying Transformer-based approaches to a wide range of complex planning applications.
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by jsendak | Feb 23, 2024 | GR & QC Articles
arXiv:2402.13360v1 Announce Type: new
Abstract: This study explores the behavior of compact stars within the framework of $f(R,L_m,T)$ gravity, focusing on the functional form $f(R,L_m,T) = R + alpha TL_m$. The modified Tolman-Oppenheimer-Volkoff (TOV) equations are derived and numerically solved for several values of the free parameter $alpha$ by considering both quark and hadronic matter — described by realistic equations of state (EoSs). Furthermore, the stellar structure equations are adapted for two different choices of the matter Lagrangian density (namely, $L_m= p$ and $L_m= -rho$), laying the groundwork for our numerical analysis. As expected, we recover the traditional TOV equations in General Relativity (GR) when $alpha rightarrow 0$. Remarkably, we found that the two choices for $L_m$ have appreciably different effects on the mass-radius diagrams. Results showcase the impact of $alpha$ on compact star properties, while final remarks summarize key findings and discuss implications, including compatibility with observational data from NGC 6397’s neutron star. Overall, this research enhances comprehension of $f(R,L_m,T)$ gravity’s effects on compact star internal structures, offering insights for future investigations.
This study examines the behavior of compact stars within the framework of $f(R,L_m,T)$ gravity, focusing specifically on the functional form $f(R,L_m,T) = R + alpha TL_m$. The modified Tolman-Oppenheimer-Volkoff (TOV) equations are derived and numerically solved for different values of the parameter $alpha$, considering both quark and hadronic matter with realistic equations of state. The stellar structure equations are adapted for two choices of the matter Lagrangian density, laying the foundation for the numerical analysis.
When $alpha$ approaches zero, the traditional TOV equations in General Relativity (GR) are recovered. However, it was discovered that the two choices for $L_m$ have significantly different effects on the mass-radius diagrams. This highlights the impact of $alpha$ on the properties of compact stars. The study concludes by summarizing the key findings and discussing their implications, including their compatibility with observational data from NGC 6397’s neutron star.
Overall, this research enhances our understanding of the effects of $f(R,L_m,T)$ gravity on the internal structures of compact stars. It provides insights that can contribute to future investigations in this field.
Roadmap for Future Investigations
To further explore the implications and potential applications of $f(R,L_m,T)$ gravity on compact stars, several avenues of research can be pursued:
1. Expansion to Other Functional Forms
While this study focuses on the specific functional form $f(R,L_m,T) = R + alpha TL_m$, there is potential for investigation into other functional forms. Different choices for $f(R,L_m,T)$ may yield interesting and diverse results, expanding our understanding of compact star behavior.
2. Exploration of Different Equations of State
Currently, the study considers realistic equations of state for both quark and hadronic matter. However, there is room for exploration of other equations of state. By incorporating different equations of state, we can gain a more comprehensive understanding of the behavior of compact stars under $f(R,L_m,T)$ gravity.
3. Inclusion of Additional Parameters
Expanding the analysis to include additional parameters beyond $alpha$ can provide a more nuanced understanding of the effects of $f(R,L_m,T)$ gravity on compact stars. By investigating how different parameters interact with each other and impact the properties of compact stars, we can uncover new insights into the behavior of these celestial objects.
4. Comparison with Observational Data
While this study discusses the compatibility of the findings with observational data from NGC 6397’s neutron star, it is important to expand this comparison to a wider range of observational data. By comparing the theoretical predictions with a larger dataset, we can validate the conclusions drawn and identify any discrepancies or areas for further investigation.
Challenges and Opportunities
Potential Challenges:
- Obtaining accurate and comprehensive observational data on compact stars for comparison with theoretical predictions can be challenging due to their extreme conditions and limited visibility.
- Numerically solving the modified TOV equations for various parameter values and choices of matter Lagrangian density may require significant computational resources and optimization.
- Exploring different functional forms and equations of state can lead to complex analyses, requiring careful interpretation and validation of results.
Potential Opportunities:
- The advancements in observational techniques and instruments provide opportunities for obtaining more precise data on compact stars, enabling more accurate validation of theoretical models.
- Ongoing advancements in computational power and numerical techniques allow for more efficient and faster solution of the modified TOV equations, facilitating the exploration of a broader parameter space.
- The diverse range of functional forms and equations of state available for investigation provides ample opportunities for uncovering novel insights into the behavior and properties of compact stars.
By addressing these challenges and capitalizing on the opportunities, future investigations into the effects of $f(R,L_m,T)$ gravity on compact star internal structures can continue to push the boundaries of our understanding and pave the way for further advancements in the field.
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by jsendak | Feb 23, 2024 | Computer Science
Existing approaches to Theory of Mind (ToM) in Artificial Intelligence (AI) have predominantly focused on prompted or cue-based ToM. While this has yielded useful insights into how AI systems can infer and reason about the mental states of others, there is a growing consensus that this approach may limit the development of Artificial Social Intelligence (ASI).
In this article, we propose an alternative perspective by introducing the concept of spontaneous ToM. Spontaneous ToM refers to the ability of AI systems to reason about others’ mental states using unintentional and possibly uncontrollable cognitive functions. By grounding social reasoning in these natural, automatic processes, AI systems can achieve a more human-like understanding of the minds of others.
The Limitations of Prompted ToM
Prompted ToM, as commonly explored in AI research, involves explicitly providing cues or prompts to an AI system to elicit inferences about the mental states of others. While this approach has proven valuable, it presents certain limitations.
Firstly, prompted ToM relies heavily on external cues, which may not always be available in real-world social interactions. This limits the generalizability and applicability of prompted ToM models. Spontaneous ToM, on the other hand, leverages cognitive functions that operate automatically and can be applied in a broader range of situations.
Secondly, prompted ToM may neglect the important role of unconscious mental processes in social reasoning. By exclusively focusing on explicit prompts, AI systems miss out on the rich and nuanced information that can be gleaned from spontaneous cognitive processes. Incorporating spontaneous ToM allows for a more comprehensive understanding of others’ mental states.
A Principled Approach to AI ToM
We advocate for a principled approach to studying and developing AI ToM, which involves considering both prompted and spontaneous ToM. By combining these two forms of social reasoning, AI systems can exhibit a robust and generalized ASI.
Principled AI ToM would require research efforts to explore the cognitive mechanisms underlying spontaneous ToM. Understanding these innate processes can help AI systems mimic human-like social reasoning and enhance their ability to predict and respond to the mental states of others.
Furthermore, developing AI systems with spontaneous ToM could have significant implications for various applications. For instance, in human-robot interaction, AI systems with spontaneous ToM could anticipate the intentions and needs of users more effectively, leading to more seamless and intuitive interactions.
The Future of ASI
The integration of spontaneous ToM into AI systems marks an exciting direction for the field of ASI. As researchers delve further into understanding the cognitive processes involved in spontaneous social reasoning, we can expect significant advancements in the development of AI systems that are genuinely capable of understanding and interacting with humans in a social context.
However, challenges lie ahead. Unintentional cognitive functions can be challenging to model, as they are often implicit and difficult to define explicitly. Overcoming these challenges will require interdisciplinary collaborations between computer science, cognitive science, neuroscience, and related disciplines.
In summary, by moving beyond prompted ToM and embracing spontaneous social reasoning, AI researchers can unlock the full potential of ASI. As we continue to investigate the intricacies of human cognition, we can lay the groundwork for AI systems that possess a genuine understanding of others’ mental states.
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by jsendak | Feb 23, 2024 | Art
