by jsendak | Jul 30, 2024 | Computer Science
Expert Commentary: Enhancing Spiking Neural Networks with Learnable Delays and Dynamic Pruning
Spiking Neural Networks (SNNs) have become increasingly popular in the field of neuromorphic computing due to their closer resemblance to biological neural networks. In this article, the authors present a model that incorporates two key enhancements – learnable synaptic delays and dynamic pruning – to improve the efficiency and biological realism of SNNs for temporal data processing.
Learnable Synaptic Delays using Dilated Convolution with Learnable Spacings (DCLS)
Synaptic delays play a crucial role in information processing in the brain, allowing for the sequential propagation of signals. The authors introduce a novel approach called Dilated Convolution with Learnable Spacings (DCLS) to incorporate learnable delays in their SNN model. By training the model on the Raw Heidelberg Digits keyword spotting benchmark using Backpropagation Through Time, they demonstrate that the network learns to utilize specific delays to improve its performance on temporal data tasks.
This approach has important implications for real-world applications that involve processing time-varying data, such as speech or video processing. By enabling SNNs to learn and adapt their synaptic delays, the model becomes more capable of capturing the spatio-temporal patterns present in the data, leading to improved accuracy and robustness.
Dynamic Pruning with DEEP R and RigL
To ensure optimal connectivity throughout training, the authors introduce a dynamic pruning strategy that combines DEEP R for connection removal and RigL for connection reintroduction. Pruning refers to the selective removal of connections in a neural network, reducing its computational and memory requirements while maintaining its performance. By dynamically pruning and rewiring the network, the model adapts to the task at hand and achieves a more efficient representation of the data.
This pruning strategy is particularly valuable in the context of SNNs, as it allows for the creation of networks with optimal connectivity, mimicking the sparse and selective connectivity observed in biological neural networks. By reducing the number of connections, the model becomes more biologically plausible and potentially more efficient in terms of energy consumption.
Enforcing Dale’s Principle for Excitation and Inhibition
Dale’s Principle states that individual neurons are either exclusively excitatory or inhibitory, but not both. By incorporating this principle into their SNN model, the authors align their model closer to biological neural networks, enhancing its biological realism. This constraint ensures that the network exhibits clear spatio-temporal patterns of excitation and inhibition after training.
The results of this research are significant as they shed light on the spatio-temporal dynamics in SNNs and demonstrate the robustness of the emerging patterns to both pruning and rewiring processes. This finding provides a solid foundation for future work in the field of neuromorphic computing and opens up exciting possibilities for developing efficient and biologically realistic SNN models for various applications.
In conclusion, the integration of learnable synaptic delays, dynamic pruning, and biological constraints presented in this article is a significant step towards enhancing the efficacy and biological realism of SNNs for temporal data processing. These advancements contribute to the development of more efficient and adaptive neuromorphic computing systems that can better process and understand time-varying information.
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by jsendak | Jul 26, 2024 | Computer Science
arXiv:2407.17911v1 Announce Type: new
Abstract: Diffusion models revolutionize image generation by leveraging natural language to guide the creation of multimedia content. Despite significant advancements in such generative models, challenges persist in depicting detailed human-object interactions, especially regarding pose and object placement accuracy. We introduce a training-free method named Reasoning and Correcting Diffusion (ReCorD) to address these challenges. Our model couples Latent Diffusion Models with Visual Language Models to refine the generation process, ensuring precise depictions of HOIs. We propose an interaction-aware reasoning module to improve the interpretation of the interaction, along with an interaction correcting module to refine the output image for more precise HOI generation delicately. Through a meticulous process of pose selection and object positioning, ReCorD achieves superior fidelity in generated images while efficiently reducing computational requirements. We conduct comprehensive experiments on three benchmarks to demonstrate the significant progress in solving text-to-image generation tasks, showcasing ReCorD’s ability to render complex interactions accurately by outperforming existing methods in HOI classification score, as well as FID and Verb CLIP-Score. Project website is available at https://alberthkyhky.github.io/ReCorD/ .
Analysis: Reasoning and Correcting Diffusion (ReCorD) in Multimedia Image Generation
In the field of multimedia information systems, the generation of realistic and detailed images has been an ongoing challenge. This is particularly true when it comes to human-object interactions (HOIs), where accurately depicting the pose and placement of objects in relation to humans is crucial for creating immersive and authentic visuals.
However, recent advancements in generative models, especially those leveraging natural language input, have shown promise in improving image generation. The article introduces a novel training-free method called Reasoning and Correcting Diffusion (ReCorD), which aims to address the challenges in generating accurate HOIs by combining Latent Diffusion Models with Visual Language Models.
One of the key contributions of ReCorD is the incorporation of an interaction-aware reasoning module. By considering the context and semantics of the input text description, this module enhances the understanding of the intended interaction between humans and objects. This is crucial for generating images that accurately depict the desired pose and object placement.
Furthermore, ReCorD also introduces an interaction correcting module, which refines the output image to ensure precision in HOI generation. This fine-tuning process takes into account intricate details of human-object interactions, resulting in images with superior fidelity. Moreover, by carefully selecting poses and positioning objects, ReCorD manages to reduce the computational requirements without compromising the quality of the generated images.
What makes ReCorD particularly interesting is its multi-disciplinary nature. It combines techniques from computer vision, natural language processing, and generative modeling to address the challenges in HOI generation. By integrating these diverse disciplines, ReCorD pushes the boundaries of text-to-image synthesis and demonstrates the potential of combining different approaches to achieve more accurate and realistic images.
In the wider field of multimedia information systems, ReCorD aligns with the research on image generation, which has seen significant progress in recent years. The use of diffusion models and the incorporation of natural language guidance further strengthen the connection to multimedia information systems, as these techniques allow for semantic understanding and context-aware generation of visuals.
In addition, ReCorD’s focus on human-object interactions and accurate depiction of poses and object placements highlights its relevance to animations, artificial reality, augmented reality, and virtual realities. These technologies rely on realistic visuals to create immersive experiences, and ReCorD’s advancements in image generation can potentially enhance the quality and authenticity of such virtual environments.
In conclusion, ReCorD presents an innovative approach to generating images that accurately depict human-object interactions. By leveraging the strengths of diffusion models and visual language models, as well as incorporating reasoning and correcting modules, ReCorD achieves superior fidelity in generated images. The multi-disciplinary nature of ReCorD aligns it with the wider field of multimedia information systems and its relevance to various technologies like animations, artificial reality, augmented reality, and virtual realities.
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by jsendak | Jul 26, 2024 | Computer Science
Maintaining patient safety and the safety of healthcare workers (HCWs) in hospitals and clinics highly depends on following the proper protocol for donning and taking off personal protective equipment (PPE).
As an expert commentator, I completely agree with the importance of ensuring that HCWs adhere to correct procedures when it comes to using PPE. During the ongoing COVID-19 pandemic, we have seen the critical role that PPE plays in preventing the spread of infection and protecting the frontline healthcare workers.
However, it is crucial to note that donning and doffing PPE can be a complex and cognitively demanding process. HCWs often face challenges in following the correct sequence and may inadvertently miss a step, which can significantly increase the risk of contamination or infection.
The Centers for Disease Control and Prevention (CDC) guidelines for correct PPE use provide an essential framework for HCWs to follow.
The CDC guidelines are based on scientific evidence and best practices, ensuring that HCWs have the necessary knowledge and guidance to protect themselves and their patients. These guidelines emphasize the importance of proper hand hygiene, using the appropriate PPE for specific tasks, and the correct sequence for donning and doffing.
A real-time object detection system, coupled with unique sequencing algorithms, offers a promising solution to enhance the donning and doffing process.
By implementing a real-time object detection system, healthcare settings can provide HCWs with immediate feedback during the process of putting on and removing PPE. This feedback can help identify any missed steps or errors, allowing HCWs to correct them promptly.
Additionally, the use of unique sequencing algorithms ensures that the correct order of donning and doffing is maintained. This is crucial to prevent cross-contamination and ensure the proper protection of HCWs and patients.
The deployment of tiny machine learning (yolov4-tiny) in embedded system architecture is a game-changer for healthcare settings.
The use of tiny machine learning in embedded systems makes this solution feasible and cost-effective for different healthcare settings. These embedded systems can be integrated into existing infrastructure or wearable devices, providing real-time alerts and feedback to HCWs without the need for external resources or extensive training.
Overall, the combination of real-time object detection, unique sequencing algorithms, and tiny machine learning offers a promising approach to improving donning and doffing procedures in healthcare settings. By ensuring that HCWs follow the proper protocol, we can enhance patient safety and protect the well-being of our healthcare workforce.
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by jsendak | Jul 25, 2024 | Computer Science
arXiv:2407.17205v1 Announce Type: new
Abstract: The technology to capture, create, and use three-dimensional (3D) models has become increasingly accessible in recent years.
With increasing numbers of use cases for 3D models and collections of rapidly increasing size, better methods to analyze the content of 3D models are required.
While previously proposed 3D model collections for research purposes exist, these often contain only untextured geometry and are typically designed for a specific application, which limits their use in quantitative evaluations of modern 3D model analysis methods.
In this paper, we introduce the Sketchfab 3D Creative Commons Collection (S3D3C), a new 3D model research collection consisting of 40,802 creative commons licensed models downloaded from the 3D model platform Sketchfab.
By including popular freely available models with a wide variety of technical properties, such as textures, materials, and animations, we enable its use in the evaluation of state-of-the-art geometry-based and view-based 3D model analysis and retrieval techniques.
Expert Commentary: The Advancements in 3D Model Analysis and Retrieval Techniques
Over the past few years, the accessibility of technology to capture, create, and use three-dimensional (3D) models has significantly improved. This has led to a vast increase in the use cases for 3D models, resulting in collections of rapidly growing size. However, as the size and complexity of these collections increase, so does the need for better methods to analyze their content. In this regard, the Sketchfab 3D Creative Commons Collection (S3D3C) introduced in this paper presents a promising solution.
The S3D3C collection consists of 40,802 creative commons licensed models that were downloaded from the popular 3D model platform, Sketchfab. Unlike previously proposed research collections, S3D3C incorporates a wide variety of technical properties found in real-world 3D models, such as textures, materials, and animations. This inclusion of popular freely available models with diverse characteristics allows for a more comprehensive evaluation of state-of-the-art geometry-based and view-based 3D model analysis and retrieval techniques.
One key aspect that sets S3D3C apart is its multi-disciplinary nature. The collection encompasses not only the field of multimedia information systems but also overlaps with concepts in animations, artificial reality, augmented reality, and virtual realities. The broad range of technical properties found in the models allows researchers from various disciplines to explore and evaluate their methods, fostering collaboration and innovation.
From a multimedia information systems perspective, the S3D3C collection provides a realistic and practical dataset for testing and benchmarking the performance of 3D model analysis algorithms. Researchers in this field can leverage this collection to improve techniques for extracting meaningful information from complex 3D models, such as object recognition, shape analysis, and semantic understanding. Moreover, the dataset’s diversity can help identify challenges and limitations faced by existing methods, pushing researchers to develop more robust and efficient solutions.
The inclusion of animation and interactive elements within the models in S3D3C opens up new possibilities for research in animations and virtual realities. Researchers can now investigate techniques for analyzing and manipulating animated 3D models, advancing fields like character animation, motion capture, and virtual reality experiences. The availability of a standardized dataset allows for fair comparisons between different approaches, fostering healthy competition and driving innovation in these areas.
The advancements in 3D model analysis and retrieval techniques facilitated by the S3D3C collection have significant implications in fields like computer graphics, computer vision, and human-computer interaction. Improved methods for analyzing and understanding 3D models can revolutionize industries ranging from gaming and entertainment to architectural design and virtual prototyping. The research conducted using this collection can pave the way for more immersive virtual experiences, better content creation tools, and more intelligent systems capable of understanding and interacting with the 3D world.
In conclusion, the introduction of the Sketchfab 3D Creative Commons Collection (S3D3C) fills an important gap in the research community when it comes to benchmarking and evaluating 3D model analysis and retrieval techniques. The multi-disciplinary nature of the collection, its inclusion of diverse technical properties, and the vast number of models make it a valuable resource for researchers from a wide range of fields. The future of 3D modeling and analysis looks promising, with the S3D3C collection serving as a catalyst for innovation and collaboration.
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by jsendak | Jul 25, 2024 | Computer Science
Improving the Sustainability of Microservices: A Rapid Review
Microservices have become increasingly popular in the software industry due to their scalability, maintainability, and agility. However, as the industry focuses more on environmental sustainability, there is a growing demand for improving the sustainability of microservice systems. This article presents a rapid review of 22 peer-reviewed studies that explore architectural tactics to enhance the environmental sustainability of microservices.
Sustainability Aspects: Energy Efficiency, Carbon Efficiency, and Resource Efficiency
The review identifies three key sustainability aspects: energy efficiency, carbon efficiency, and resource efficiency. Resource efficiency appears to be the most extensively studied aspect, highlighting the importance of optimizing resource utilization in microservice systems. On the other hand, energy efficiency and carbon efficiency are still in the early stages of research, pointing towards the need for further investigation and development in these areas.
Categorization by Context: Serverless Platforms, Decentralized Networks, and More
To provide actionable insights, the reviewed studies categorize the identified architectural tactics according to their context. This allows practitioners to identify tactics that are applicable in specific settings. Some of the categorized contexts include serverless platforms, decentralized networks, and others. By aligning the tactics with the appropriate context, organizations can effectively implement sustainability improvements in their microservice systems.
Evidence of Optimization: Measurement Units, Statistical Methods, and Experimental Setup
To ensure the relevance and effectiveness of the identified tactics, the studies present evidence of optimization after implementing these tactics. This evidence includes measurement units and statistical methods used to quantify improvements in energy efficiency, carbon efficiency, and resource efficiency. Moreover, the experiments conducted are described to provide valuable guidance for future studies and industrial practitioners.
Insufficiencies and Areas for Further Research
While the rapid review presents valuable insights, it also acknowledges the insufficiencies in the current research landscape. There is a need for more comprehensive studies and experiments to fully understand and enhance the sustainability of microservice systems. By addressing these insufficiencies, researchers and industry professionals can pave the way for more sustainable software architectures.
Conclusion
In conclusion, this rapid review highlights the importance of improving the sustainability of microservice systems. By synthesizing findings from peer-reviewed studies, it provides actionable tactics categorized by sustainability aspects and context. The evidence of optimization presented in the studies adds credibility to these tactics. However, further research is necessary to fully explore the energy efficiency and carbon efficiency of microservices. With continuous efforts, the industry can integrate sustainability into microservice architectures and contribute to a more environmentally responsible future.
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by jsendak | Jul 24, 2024 | Computer Science
arXiv:2407.16307v1 Announce Type: new
Abstract: Multimodal contrastive learning (MCL) has shown remarkable advances in zero-shot classification by learning from millions of image-caption pairs crawled from the Internet. However, this reliance poses privacy risks, as hackers may unauthorizedly exploit image-text data for model training, potentially including personal and privacy-sensitive information. Recent works propose generating unlearnable examples by adding imperceptible perturbations to training images to build shortcuts for protection. However, they are designed for unimodal classification, which remains largely unexplored in MCL. We first explore this context by evaluating the performance of existing methods on image-caption pairs, and they do not generalize effectively to multimodal data and exhibit limited impact to build shortcuts due to the lack of labels and the dispersion of pairs in MCL. In this paper, we propose Multi-step Error Minimization (MEM), a novel optimization process for generating multimodal unlearnable examples. It extends the Error-Minimization (EM) framework to optimize both image noise and an additional text trigger, thereby enlarging the optimized space and effectively misleading the model to learn the shortcut between the noise features and the text trigger. Specifically, we adopt projected gradient descent to solve the noise minimization problem and use HotFlip to approximate the gradient and replace words to find the optimal text trigger. Extensive experiments demonstrate the effectiveness of MEM, with post-protection retrieval results nearly half of random guessing, and its high transferability across different models. Our code is available on the https://github.com/thinwayliu/Multimodal-Unlearnable-Examples
Commentary: Multimodal Unlearnable Examples for Privacy Protection in Zero-Shot Classification
In the field of multimedia information systems, the concept of multimodal contrastive learning (MCL) has been gaining traction for its remarkable advancements in zero-shot classification. By leveraging millions of image-caption pairs sourced from the Internet, MCL algorithms have demonstrated their ability to learn from diverse sets of data. However, this heavy reliance on internet-crawled image-text pairs also poses significant privacy risks. Unscrupulous hackers could exploit the image-text data to train models, potentially accessing personal and privacy-sensitive information.
Recognizing the need for privacy protection in MCL, recent works have proposed the use of imperceptible perturbations added to training images. These perturbations aim to create unlearnable examples that confuse unauthorized model training. However, these existing methods are primarily designed for unimodal classification tasks and their effectiveness in the context of MCL remains largely unexplored.
In this paper, the authors address this gap by proposing a novel optimization process called Multi-step Error Minimization (MEM) for generating unlearnable examples in multimodal data. MEM extends the Error-Minimization (EM) framework by optimizing both the image noise and an additional text trigger. By doing so, MEM effectively misleads the model into learning a shortcut between the noise features and the text trigger, making the examples unlearnable.
The approach outlined in MEM consists of two main steps. Firstly, projected gradient descent is utilized to solve the noise minimization problem. This ensures that the added noise remains imperceptible to human observers while achieving the desired effect. Secondly, the authors employ the HotFlip technique to approximate the gradient and replace words in the text trigger. This allows for the identification of an optimal text trigger that maximizes the effectiveness of the unlearnable example.
Extensive experiments conducted by the authors demonstrate the efficacy of MEM in privacy protection. The post-protection retrieval results show a significant reduction in performance compared to random guessing, indicating that the unlearnable examples effectively confuse unauthorized model training. Furthermore, the high transferability of MEM across different models highlights its potential for widespread application.
Overall, this research makes valuable contributions to the field of multimedia information systems by addressing the important issue of privacy protection in MCL. By introducing the concept of multimodal unlearnable examples and proposing the MEM optimization process, the authors provide a novel and effective approach to safeguarding personal and privacy-sensitive information. This work exemplifies the multi-disciplinary nature of the field, drawing from concepts in artificial reality, augmented reality, and virtual realities to create practical solutions for real-world problems.
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