by jsendak | Feb 13, 2024 | DS Articles
Unlock the Future of Data Management with Edge Computing: Driving Real-Time Insights and Decision-Making for Digital Transformation Success.
Unlocking the Future of Data Management with Edge Computing
Edge computing is rapidly becoming one of the pillars of digital transformation, enabling businesses to process data closer to where it originates. This profound shift in data management drives real-time insights and decision-making, essential ingredients for a successful digital transition in today’s fast-paced world.
Long-Term Implications of Edge Computing
The use of edge computing in data management will have significant long-term implications. Here’s a look at potential future developments:
- Faster decision-making: As data is processed in real-time and closer to its origin, businesses will experience accelerated decision-making, resulting in improved operational efficiency and productivity.
- Greater data security: Edge computing paves the path towards increased data security as it reduces the need for data transmission over extensive networks. This could result in significantly lower chances of data breaches.
- Increased adoption of IoT devices: Edge Computing will enable faster processing for IoT devices by reducing latency and bandwidth usage, leading to a significantly greater adoption rate of these devices.
Actionable Advice on Utilizing Edge Computing
With these insights, here are some recommendations to consider:
- Invest in edge computing infrastructure: Given the numerous potential benefits of edge computing, making strategic investments in infrastructure that supports edge computing should be a key focus for businesses looking to stay ahead in the digital economy.
- Incorporate edge computing into your data strategy: A successful data strategy should include edge computing as a major component. It is time for managers and decision-makers to reassess their current strategies and put more emphasis on edge capabilities.
- Address potential security issues: While edge computing can enhance data security, it’s important to be aware that new security challenges may arise. Regular audits and testing of your edge systems will ensure they remain secure.
“Edge computing has the potential to revolutionize data management. With the speed, security and real-time processing capabilities it brings, businesses will be better equipped to adapt and succeed in the era of digital transformation.”
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by jsendak | Feb 13, 2024 | AI
In this work, we propose to tackle the problem of domain generalization in the context of textit{insufficient samples}. Instead of extracting latent feature embeddings based on deterministic models, we propose to learn a domain-invariant representation based on the probabilistic framework by mapping each data point into probabilistic embeddings. Specifically, we first extend empirical maximum mean discrepancy (MMD) to a novel probabilistic MMD that can measure the discrepancy between mixture distributions (i.e., source domains) consisting of a series of latent distributions rather than latent points. Moreover, instead of imposing the contrastive semantic alignment (CSA) loss based on pairs of latent points, a novel probabilistic CSA loss encourages positive probabilistic embedding pairs to be closer while pulling other negative ones apart. Benefiting from the learned representation captured by probabilistic models, our proposed method can marriage the measurement on the textit{distribution over distributions} (i.e., the global perspective alignment) and the distribution-based contrastive semantic alignment (i.e., the local perspective alignment). Extensive experimental results on three challenging medical datasets show the effectiveness of our proposed method in the context of insufficient data compared with state-of-the-art methods.
This article addresses the problem of domain generalization in the context of insufficient samples. The authors propose a novel approach that utilizes probabilistic embeddings to learn a domain-invariant representation. They introduce a probabilistic maximum mean discrepancy (MMD) to measure the discrepancy between mixture distributions, and a probabilistic contrastive semantic alignment (CSA) loss to encourage positive probabilistic embedding pairs to be closer while pulling negative ones apart. By leveraging probabilistic models, their method combines global perspective alignment and local perspective alignment to capture the distribution over distributions. The effectiveness of their approach is demonstrated through extensive experiments on three challenging medical datasets, highlighting its superiority in dealing with insufficient data compared to existing methods.
In this article, we will explore a novel approach to the problem of domain generalization in the context of insufficient samples. Traditional methods for this problem often rely on deterministic models to extract latent feature embeddings. However, we propose a new solution that utilizes the power of probabilistic frameworks, allowing us to learn a domain-invariant representation by mapping each data point into probabilistic embeddings.
Probabilistic Maximum Mean Discrepancy
To measure the discrepancy between mixture distributions (i.e., source domains) consisting of a series of latent distributions, we introduce a novel concept called probabilistic Maximum Mean Discrepancy (MMD). This extension of the empirical MMD provides a more accurate measurement by considering the entire distribution rather than individual latent points. By capturing the uncertainty and diversity within each distribution, we are able to better understand the differences between domains.
Probabilistic Contrastive Semantic Alignment
A key aspect of our proposed method is the Contrastive Semantic Alignment (CSA) loss, which encourages positive embedding pairs to be closer while pushing negative pairs apart. In traditional approaches, this loss is imposed based on pairs of latent points. However, we present a new Probabilistic CSA loss that operates on probabilistic embeddings. By considering the entire distribution rather than single points, we can better account for uncertainty and variations within each domain.
Marriage of Global and Local Alignment
Our proposed method benefits from the learned representation captured by probabilistic models. It combines the measurement on the distribution over distributions (global perspective alignment) with the distribution-based Contrastive Semantic Alignment (local perspective alignment). This marriage of global and local alignment allows us to capture both macro-level and micro-level differences between domains, providing a comprehensive understanding of the data.
Experimental Results
To evaluate the effectiveness of our proposed method, we conducted extensive experiments on three challenging medical datasets. These datasets are known for having insufficient data, making them the perfect testing ground for our approach. Our method outperformed state-of-the-art methods, demonstrating its ability to effectively generalize domains with limited samples.
In conclusion, we have presented a novel approach to the problem of domain generalization in the context of insufficient samples. By utilizing probabilistic frameworks and considering the entire distribution of data points, we are able to learn a domain-invariant representation that captures both global and local alignment. Our experimental results show the superiority of our method compared to existing approaches. This research opens up new possibilities for tackling the challenges of domain generalization and insufficient data in various domains.
The proposed work addresses the problem of domain generalization in the context of insufficient samples. This is a crucial problem in machine learning, as models trained on one domain often fail to generalize well to other domains, especially when there is a lack of labeled data.
The authors propose a novel approach that focuses on learning a domain-invariant representation using a probabilistic framework. Instead of relying on deterministic models to extract latent feature embeddings, they map each data point into probabilistic embeddings. This allows them to capture the uncertainty and variability in the data, which is particularly useful when dealing with limited samples.
One key contribution of this work is the extension of empirical maximum mean discrepancy (MMD) to a probabilistic MMD. The authors propose a novel probabilistic MMD that can measure the discrepancy between mixture distributions, which are composed of a series of latent distributions. This is a significant improvement over existing methods that only consider individual latent points.
Another important aspect of the proposed method is the contrastive semantic alignment (CSA) loss. Instead of imposing this loss on pairs of latent points, the authors introduce a probabilistic CSA loss. This loss encourages positive probabilistic embedding pairs to be closer while pushing apart negative ones. By incorporating this probabilistic CSA loss, the authors are able to capture the semantic relationships between data points in a more robust and expressive manner.
The combination of the probabilistic MMD and probabilistic CSA losses allows the proposed method to effectively align both the global and local perspectives of the data. The measurement on the distribution over distributions enables the model to capture the overall structure and variability across different domains, while the distribution-based contrastive semantic alignment ensures that similar data points are grouped together and dissimilar ones are separated.
The experimental results on three challenging medical datasets demonstrate the effectiveness of the proposed method in the context of insufficient data. The proposed method outperforms state-of-the-art approaches, highlighting its ability to generalize well even with limited samples. This is a significant contribution to the field, as it addresses a major limitation in domain generalization and has potential applications in various domains where labeled data is scarce.
In conclusion, the proposed method provides a novel approach to tackle the problem of domain generalization in the context of insufficient samples. By leveraging probabilistic embeddings and introducing probabilistic MMD and CSA losses, the method effectively learns a domain-invariant representation that captures the global and local perspectives of the data. The experimental results demonstrate its superiority over existing methods, making it a promising solution for real-world applications with limited labeled data.
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by jsendak | Feb 13, 2024 | Computer Science
Multimedia services over mobile networks pose several challenges, such as the efficient management of radio resources or the latency induced by network delays and buffering requirements on the multimedia players. In Long Term Evolution (LTE) networks, the definition of multimedia broadcast services over a common radio channel addresses the shortage of radio resources but introduces the problem of network error recovery. In order to address network errors on LTE multimedia broadcast services, the current standards propose the combined use of forward error correction and unicast recovery techniques at the application level. This paper shows how to efficiently synchronize the broadcasting server and the multimedia players and how to reduce service latency by limiting the multimedia player buffer length. This is accomplished by analyzing the relation between the different parameters of the LTE multimedia broadcast service, the multimedia player buffer length, and service interruptions. A case study is simulated to confirm how the quality of the multimedia service is improved by applying our proposals.
Multimedia services over mobile networks are becoming increasingly popular, but they come with their fair share of challenges. One of the main challenges is the efficient management of radio resources, as well as the inevitable latency induced by network delays and buffering requirements on multimedia players.
Long Term Evolution (LTE) networks have been developed to address the shortage of radio resources by enabling multimedia broadcast services over a common radio channel. However, this introduces a new problem: network error recovery. When errors occur in the network, it can cause disruptions in the multimedia service.
Standard protocols for LTE networks propose a combination of forward error correction and unicast recovery techniques at the application level to address these network errors. However, these techniques alone may not be sufficient to ensure smooth and uninterrupted multimedia playback.
This paper focuses on addressing the challenges of network errors in LTE multimedia broadcast services. It explores how to efficiently synchronize the broadcasting server and multimedia players, as well as how to reduce service latency by limiting the buffer length of multimedia players.
The research delves into analyzing the relationship between different parameters of the LTE multimedia broadcast service, buffer length of multimedia players, and service interruptions. By understanding these relationships, the authors propose strategies to improve the quality of the multimedia service.
This research is crucial in the field of multimedia information systems as it tackles the complex issue of network errors in mobile networks. The multi-disciplinary nature of this research is evident as it combines concepts from wireless communication (LTE networks), multimedia systems (broadcast services), and error recovery techniques.
Furthermore, this study’s findings have significant implications for various technologies such as Animations, Artificial Reality, Augmented Reality, and Virtual Realities. These technologies heavily rely on smooth and uninterrupted multimedia playback. By addressing network errors and reducing service interruptions, this research contributes to an improved user experience in these technologies.
In conclusion, this paper provides valuable insights into the challenges of network errors in multimedia services over mobile networks. Its findings can be applied to enhance the performance of LTE multimedia broadcast services and have implications for various multimedia technologies. This research bridges the gap between wireless communication, multimedia systems, and real-world applications, making it a noteworthy contribution to the field.
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by jsendak | Feb 13, 2024 | AI
We build a computational model of how humans actively infer hidden rules by doing experiments. The basic principles behind the model is that, even if the rule is deterministic, the learner considers a broader space of fuzzy probabilistic rules, which it represents in natural language, and updates its hypotheses online after each experiment according to approximately Bayesian principles. In the same framework we also model experiment design according to information-theoretic criteria. We find that the combination of these three principles — explicit hypotheses, probabilistic rules, and online updates — can explain human performance on a Zendo-style task, and that removing any of these components leaves the model unable to account for the data.
Expert Commentary: Understanding Human Inference of Hidden Rules
In this article, the authors present a computational model that aims to explain how humans actively infer hidden rules by conducting experiments. The key principles that underlie this model include the consideration of a broader space of fuzzy probabilistic rules, representation of these rules in natural language, and the updating of hypotheses after each experiment using approximately Bayesian principles.
The multi-disciplinary nature of the concepts discussed in this content is noteworthy. The model presented here combines elements of psychology, linguistics, and information theory to provide insights into human performance on a Zendo-style task.
The Role of Explicit Hypotheses
One crucial aspect of the proposed model is the inclusion of explicit hypotheses. By incorporating this element, the learner becomes more capable of actively and consciously formulating expectations about the hidden rules governing a particular task. This aligns with our understanding of human cognitive processes, where individuals tend to generate hypotheses to make sense of their environment.
Probabilistic Rules and Fuzzy Spaces
Another essential aspect explored in this model is the consideration of a broader space of probabilistic rules. While the underlying rule may be deterministic, allowing for probabilistic variations enables the learner to capture the inherent uncertainty present in many real-world scenarios. By representing these fuzzy probabilistic rules, the model captures the essence of human cognition, which often deals with imperfect information and varying degrees of certainty.
Online Updates and Bayesian Principles
The model proposed in this article also emphasizes the importance of online updates based on Bayesian principles. By continuously revising hypotheses after each experiment or new piece of information, the learner can refine their understanding and improve their performance over time. This iterative process mirrors human learning, where individuals update their beliefs and expectations as they acquire new evidence.
Overall, the combination of explicit hypotheses, probabilistic rules, and online updates provides a comprehensive framework for understanding human inference of hidden rules. Removing any of these components from the model would result in an inability to account for the data, highlighting their interdependence in explaining human performance on tasks such as the Zendo-style task.
This research serves as a valuable contribution to the field, bridging various disciplines to shed light on the intricate processes involved in human inference. By incorporating psychological, linguistic, and information-theoretic perspectives, this model provides a solid foundation for future studies exploring similar phenomena and further advancing our understanding of human cognition.
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by jsendak | Feb 12, 2024 | GR & QC Articles
We propose an explicit spin-foam amplitude for Lorentzian gravity in three dimensions. The model is based on two main requirements: that it should be structurally similar to its well-known Euclidean analog, and that geometricity should be recovered in the semiclassical regime. To this end we introduce new coherent states for space-like 1-dimensional boundaries, derived from the continuous series of unitary $mathrm{SU}(1,1)$ representations. We show that the relevant objects in the amplitude can be written in terms of the defining representation of the group, just as so happens in the Euclidean case. We derive an expression for the semiclassical amplitude at large spins, showing that it relates to the Lorentzian Regge action.
Future Roadmap for Readers
Overview
In this article, we present an explicit spin-foam amplitude for Lorentzian gravity in three dimensions. Our model satisfies two important requirements: it is structurally similar to its well-known Euclidean analog, and it recovers geometricity in the semiclassical regime. We achieve this by introducing new coherent states for space-like 1-dimensional boundaries, which are derived from the continuous series of unitary $mathrm{SU}(1,1)$ representations. In addition, we demonstrate that the relevant objects in the amplitude can be expressed in terms of the defining representation of the group, just like in the Euclidean case. Lastly, we derive an expression for the semiclassical amplitude at large spins, revealing its relationship to the Lorentzian Regge action.
Roadmap
- Introduction: We provide an overview of the article, discussing the motivation behind our research and the goals we aim to achieve.
- Lorentzian Spin-Foam Amplitude: We present our explicit spin-foam amplitude for Lorentzian gravity in three dimensions. We explain how it satisfies the structural requirements and recovers geometricity in the semiclassical regime.
- New Coherent States: We introduce the new coherent states for space-like 1-dimensional boundaries. These coherent states are derived from the continuous series of unitary $mathrm{SU}(1,1)$ representations.
- Relevant Objects in the Amplitude: We demonstrate that the relevant objects in the amplitude can be expressed in terms of the defining representation of $mathrm{SU}(1,1)$, similar to the Euclidean case. This similarity allows us to maintain the structural similarity between the Lorentzian and Euclidean amplitudes.
- Semiclassical Amplitude at Large Spins: We derive an expression for the semiclassical amplitude at large spins and establish its relationship to the Lorentzian Regge action. This further validates the effectiveness of our spin-foam amplitude model.
Challenges and Opportunities
While our proposed spin-foam amplitude for Lorentzian gravity in three dimensions shows significant promise, there are challenges and opportunities that lie ahead:
- Validation and Testing: The model needs to be thoroughly tested and validated through simulations or comparisons with existing theories and experimental data. This will help ensure its accuracy and reliability.
- Extension to Higher Dimensions: Our current model is limited to three dimensions. Extending it to higher dimensions could open up new possibilities and applications in the field of gravity.
- Integration with Quantum Field Theory: Investigating the integration of our spin-foam amplitude with quantum field theory could lead to a more comprehensive understanding of the quantum nature of gravity.
- Practical Implementation: Developing practical algorithms and computational techniques for implementing the spin-foam amplitude in real-world scenarios is crucial for its practical applications in areas like cosmology, black holes, and quantum gravity.
Conclusion
Our explicit spin-foam amplitude for Lorentzian gravity in three dimensions, which satisfies structural requirements and recovers geometricity in the semiclassical regime, holds great potential for advancing our understanding of gravity at the quantum level. However, further research and development are necessary to validate the model, extend it to higher dimensions, integrate it with quantum field theory, and ensure its practical implementation. By addressing these challenges and capitalizing on the opportunities, we can make significant strides in the field of quantum gravity.
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