by jsendak | Jan 18, 2024 | Computer Science
Analysis: Development of EtherCAT Master for Medical Robotics
The use of sensors and actuators in robotic systems has been steadily increasing, allowing for the integration of more advanced features and capabilities. However, this increase in complexity poses challenges, especially in fields such as medical robotics where safety and determinism are of paramount importance.
In this paper, the authors address this issue by reporting the development of an EtherCAT master as part of a software framework for a spine surgery robot. EtherCAT (Ethernet for Control Automation Technology) is a real-time industrial Ethernet communication protocol that enables fast and deterministic data exchange between components in a distributed control system.
One of the key aspects of this research is the use of an open-source EtherCAT master running on a real-time preemptive Linux operating system. This combination allows for precise periodicity and execution timing, crucial requirements for the successful operation of a medical robot. By implementing a multi-axis controller using this framework, the researchers aim to ensure the safety and accuracy of the spine surgery robot.
The real-time performance of the system is evaluated in terms of periodicity, jitter, and execution time in the first prototype of the spine surgery robot. Periodicity refers to the accuracy of the timing for executing specific tasks at regular intervals. Jitter, on the other hand, measures the variability or inconsistency in the execution timing. These metrics provide insights into the system’s ability to consistently perform tasks within the required time constraints, which is crucial for medical robotics applications.
The development of an EtherCAT master for medical robotics holds great promise for enhancing the safety and precision of surgical procedures. Such advancements can assist surgeons in performing complex procedures with greater accuracy and control. By leveraging real-time preemptive Linux and open-source software, this research also promotes collaborative and accessible development in the field of medical robotics.
Looking ahead, there are several potential directions for further research and development in this area. Firstly, the evaluation of the system’s real-time performance in a clinical setting using human subjects would provide valuable insights into its reliability and safety. Additionally, the integration of advanced sensing technologies, such as computer vision and haptics, could further enhance the capabilities of the spine surgery robot.
In conclusion, the development of an EtherCAT master as part of a software framework for spine surgery robot presents an important step towards ensuring safety and determinism in medical robotics. By leveraging open-source software and real-time preemptive Linux, this research offers a promising solution for addressing the complexity and timing requirements of medical robotic systems. With further advancements and validations, such systems have the potential to revolutionize surgical procedures and improve patient outcomes.
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by jsendak | Jan 18, 2024 | Computer Science
Speech-driven 3D facial animation is challenging due to the scarcity of
large-scale visual-audio datasets despite extensive research. Most prior works,
typically focused on learning regression models on a small dataset using the
method of least squares, encounter difficulties generating diverse lip
movements from speech and require substantial effort in refining the generated
outputs. To address these issues, we propose a speech-driven 3D facial
animation with a diffusion model (SAiD), a lightweight Transformer-based U-Net
with a cross-modality alignment bias between audio and visual to enhance lip
synchronization. Moreover, we introduce BlendVOCA, a benchmark dataset of pairs
of speech audio and parameters of a blendshape facial model, to address the
scarcity of public resources. Our experimental results demonstrate that the
proposed approach achieves comparable or superior performance in lip
synchronization to baselines, ensures more diverse lip movements, and
streamlines the animation editing process.
Speech-Driven 3D Facial Animation: Enhancing Lip Synchronization
In the field of multimedia information systems, animations play a crucial role in creating engaging and realistic virtual experiences. One aspect that contributes to the realism of animations is the synchronization of facial movements, particularly lip movements, with speech. This synchronization is challenging due to the scarcity of large-scale visual-audio datasets and the limitations of previous regression models.
The article introduces a novel approach called speech-driven 3D facial animation with a diffusion model (SAiD). SAiD utilizes a lightweight Transformer-based U-Net with a cross-modality alignment bias between audio and visual data. This approach enhances lip synchronization by effectively mapping speech audio to facial movements.
The multidisciplinary nature of this work is evident in the integration of techniques from computer vision, natural language processing, and machine learning. The use of a Transformer-based model allows for capturing complex dependencies between audio and visual features, while the diffusion model enables the generation of diverse lip movements.
To evaluate the proposed approach, the researchers introduce BlendVOCA, a benchmark dataset consisting of pairs of speech audio and parameters of a blendshape facial model. This dataset addresses the scarcity of publicly available resources for training and testing speech-driven facial animation systems.
The experimental results demonstrate that SAiD achieves comparable or even superior performance in lip synchronization when compared to baseline methods. Additionally, SAiD ensures more diverse lip movements, which is essential for creating realistic animations. The proposed approach also streamlines the animation editing process, saving significant effort in refining the generated outputs.
From a holistic perspective, this research contributes to the broader field of multimedia information systems. It addresses the challenges related to speech-driven 3D facial animation, which is crucial for applications such as virtual reality and augmented reality. By enabling more accurate and diverse lip synchronization, SAiD enhances the immersive experience of these technologies.
Overall, this article signifies the significance of advancements in animations, artificial reality, augmented reality, and virtual realities. The proposed approach and dataset pave the way for more sophisticated and realistic multimedia experiences, bridging the gap between audio and visual modalities in virtual environments.
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by jsendak | Jan 18, 2024 | Computer Science
This research introduces a new approach to style transfer that focuses specifically on curve-based design sketches. Traditional neural style transfer methods often struggle to handle binary sketch transformations, but this new framework successfully addresses these challenges.
One of the key contributions of this research is the use of parametric shape-editing rules. By incorporating these rules into the style transfer process, the framework can better preserve the important features and characteristics of the original design sketch while still applying the desired style.
Another important aspect of this framework is the efficient curve-to-pixel conversion techniques. Converting curve-based sketches into pixel-based representations can be a complex task, but by developing efficient conversion techniques, this research enables smoother and more accurate style transfer.
The fine-tuning of VGG19 on ImageNet-Sketch is another significant aspect of this study. By training the VGG19 model on a dataset specifically designed for sketches, the researchers enhance its ability to extract style features from curve-based imagery. This fine-tuned model then serves as a feature pyramid network, allowing for more precise style extraction.
Overall, this research opens up new possibilities for style transfer in the field of product design. By combining intuitive curve-based imagery with rule-based editing, designers can now more effectively articulate their design concepts and explore different styles within their sketches.
Next Steps
While this research presents a promising framework for curve-based style transfer in product design, there are several avenues for future exploration and improvement.
Firstly, further development of the parametric shape-editing rules could enhance the flexibility and control that designers have over the style transfer process. By refining these rules and making them more customizable, designers can have even greater creative freedom in expressing their design concepts.
Additionally, more research could be done on optimizing the curve-to-pixel conversion techniques. Improving the efficiency and accuracy of this conversion process would result in more visually appealing and faithful style transfers.
Furthermore, exploring different pre-trained models and datasets for fine-tuning could also lead to improvements in style extraction. By experimenting with different architectures or training on larger and more diverse sketch datasets, researchers could potentially achieve even better results in capturing and transferring various design styles.
In conclusion, the presented framework is a valuable contribution to the field of style transfer in product design. It addresses the challenges specific to curve-based sketches and offers opportunities for designers to enhance their design articulation. Future research can build upon this foundation to further advance the capabilities and applications of style transfer in design.
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by jsendak | Jan 18, 2024 | Computer Science
An Expert Commentary on Agent Strategic Behavior in Online Marketplaces
This article explores the challenges posed by strategic agents in online marketplaces and proposes a practical matching policy to optimize performance in such environments. The problem arises when agents in online platforms, such as ridesharing and freelancing platforms, have different levels of compatibility with different types of jobs.
The conventional wisdom suggests that it is more efficient to reserve more flexible agents for jobs, as they can fulfill any task. However, this creates an incentive for agents to pretend to be more specialized in order to increase their chances of being matched with a job that suits them well. This behavior results in a loss of matches and inefficiencies in the system.
The authors of this article model the allocation of jobs to agents as a matching queue and investigate the equilibrium performance of various matching policies when agents strategically report their own types. The findings reveal that reserving flexibility without considering strategic behavior can backfire, leading to extremely poor performance compared to a policy that randomly dispatches jobs to agents.
To address this challenge and strike a balance between matching efficiency and agents’ strategic considerations, the authors propose a new policy called “flexibility reservation with fallback.” This policy takes into account both agent flexibility and specialization but also incorporates a fallback mechanism to prevent agents from manipulating their reported types dishonestly. The authors demonstrate that this policy exhibits robust performance under strategic behavior.
This research has important implications for managers and service platform operators. It highlights the need to consider agent strategic behavior when designing matching policies in online platforms. Ignoring strategic behavior can lead to significant inefficiencies and loss of matches. The proposed flexibility reservation with fallback policy offers a practical solution that is easy to implement in practice due to its parameter-free nature. It provides a robust performance guarantee while balancing the needs of both the platform and its agents.
The article also provides a real-world example of how this policy has been implemented in the driver destination product of major ridesharing platforms. This demonstrates the feasibility and effectiveness of the proposed policy in improving matching efficiency and addressing strategic behavior in online marketplaces.
In conclusion, this article makes a valuable contribution by addressing the challenges posed by agent strategic behavior in online marketplaces. The proposed flexibility reservation with fallback policy offers a practical solution to optimize matching efficiency while considering agents’ strategic considerations. It provides managers and platform operators with insights and guidelines to design effective matching policies that can improve performance in online platforms.+
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by jsendak | Jan 17, 2024 | Computer Science
In recent years, communication compression techniques have become increasingly important in overcoming the communication bottleneck in distributed learning. These techniques help to reduce the amount of data that needs to be transmitted between nodes, improving the efficiency of distributed training. While unbiased compressors have been extensively studied in the literature, biased compressors have received much less attention.
In this work, the authors investigate three classes of biased compression operators, two of which are novel, and examine their performance in the context of stochastic gradient descent and distributed stochastic gradient descent. The key finding of this study is that biased compressors can achieve linear convergence rates in both single node and distributed settings.
The authors provide a theoretical analysis of a distributed compressed SGD method with an error feedback mechanism. They establish that this method has an ergodic convergence rate that can be bounded by a term involving the compression parameter $delta$, the smoothness constant $L$, the strong convexity constant $mu$, as well as the stochastic gradient noise $C$ and the gradient variance $D$. This result provides a theoretical justification for the effectiveness of biased compressors in distributed learning scenarios.
In addition to the theoretical analysis, the authors also conduct experiments using synthetic and empirical distributions of communicated gradients. These experiments shed light on why and to what extent biased compressors outperform their unbiased counterparts. The results highlight the potential benefits of using biased compressors in practical applications.
Finally, the authors propose several new biased compressors that offer both theoretical guarantees and promising practical performance. These new compressors could potentially be adopted in distributed learning systems to further improve convergence rates and reduce communication overhead.
In summary, this work contributes to the understanding of biased compression operators in distributed learning. The findings suggest that biased compressors can lead to improved convergence rates, making them an attractive option for reducing communication overhead in distributed training. The proposed theoretical analysis and new compressors provide valuable insights and practical solutions for optimizing distributed learning systems.
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by jsendak | Jan 17, 2024 | Computer Science
Integrating deep learning and causal discovery has increased the
interpretability of Temporal Action Segmentation (TAS) tasks. However,
frame-level causal relationships exist many complicated noises outside the
segment-level, making it infeasible to directly express macro action semantics.
Thus, we propose Causal Abstraction Segmentation Refiner (CASR), which can
refine TAS results from various models by enhancing video causality in
marginalizing frame-level casual relationships. Specifically, we define the
equivalent frame-level casual model and segment-level causal model, so that the
causal adjacency matrix constructed from marginalized frame-level causal
relationships has the ability to represent the segmnet-level causal
relationships. CASR works out by reducing the difference in the causal
adjacency matrix between we constructed and pre-segmentation results of
backbone models. In addition, we propose a novel evaluation metric Causal Edit
Distance (CED) to evaluate the causal interpretability. Extensive experimental
results on mainstream datasets indicate that CASR significantly surpasses
existing various methods in action segmentation performance, as well as in
causal explainability and generalization.
Enhancing Temporal Action Segmentation with Causal Abstraction Segmentation Refiner (CASR)
In recent years, the integration of deep learning and causal discovery has greatly improved the interpretability of Temporal Action Segmentation (TAS) tasks. However, a significant challenge remains in expressing macro action semantics due to the presence of frame-level causal relationships with complicated noises outside the segment-level.
To address this challenge, we propose a novel framework called Causal Abstraction Segmentation Refiner (CASR). CASR aims to refine TAS results from various models by enhancing video causality in marginalizing frame-level causal relationships. By defining equivalent frame-level causal models and segment-level causal models, CASR constructs a causal adjacency matrix that represents the segment-level causal relationships.
The key idea behind CASR is to minimize the difference between the causal adjacency matrix constructed from marginalized frame-level causal relationships and the pre-segmentation results of backbone models. This refinement process ensures that the refined TAS results capture a more accurate representation of the underlying causal relationships within the video.
In addition to introducing CASR, we also propose a new evaluation metric called Causal Edit Distance (CED) to assess the causal interpretability of TAS results. CED provides a quantitative measure of how well the refined TAS results align with the ground truth causal structure of the video.
The multi-disciplinary nature of CASR is evident in its integration of concepts from deep learning, causal discovery, and multimedia information systems. By combining these fields, CASR provides a comprehensive approach to enhancing TAS performance and interpretability.
In the broader field of multimedia information systems, TAS plays a crucial role in applications such as video surveillance, human-computer interaction, and content analysis. The ability to accurately segment and interpret actions within a video can improve tasks such as activity recognition, event detection, and anomaly detection.
Furthermore, CASR’s approach to enhancing video causality has implications for other areas of multimedia technology, such as animations, artificial reality, augmented reality, and virtual realities. By refining TAS results and improving causal interpretability, CASR can contribute to the development of more realistic and immersive multimedia experiences.
Key Takeaways:
- The integration of deep learning and causal discovery has improved the interpretability of Temporal Action Segmentation (TAS) tasks.
- CASR is a framework that refines TAS results by enhancing video causality through marginalizing frame-level causal relationships.
- CASR constructs a causal adjacency matrix to represent segment-level causal relationships.
- The difference between the constructed causal adjacency matrix and pre-segmentation results is minimized to refine TAS results.
- CED is a new evaluation metric introduced by CASR to assess causal interpretability.
- CASR’s multi-disciplinary nature relates to the wider field of multimedia information systems, as it can improve applications like video surveillance and content analysis.
- CASR’s enhancement of video causality has implications for animations, artificial reality, augmented reality, and virtual realities.
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