by jsendak | Sep 30, 2024 | DS Articles
A few weeks ago, I created a YouTube video to discuss the importance of an outside-in perspective to the value creation challenge. The key to a successful business is its ability to continuously evolve its value creation capabilities. Unfortunately, discussions about value creation often focus only on internal factors such as increasing sales, retaining customers,… Read More »Value Creation Requires An Outside-In Mentality
Analysis & Future Implications of “Value Creation Requires an Outside-In Mentality”
Looking beyond an organization’s internal capabilities and extending the scope of value creation from an outside-in perspective is a powerful approach, as highlighted in a recent YouTube video discussion. It emphasized how crucial it is for businesses to continuously evolve their value-creation capabilities. The central idea revolved around the necessity to expand the traditional purview of value creation that typically is limited to internal factors such as boosting sales or enhancing customer retention.
Long-Term Implications
Adopting an outside-in perspective holds significant long-term implications for any business’ growth strategy. It empowers organizations to scrutinize their operations, products, and services from their stakeholders’ viewpoint, fostering more innovation and creativity. It also allows for better identification of market trends, gaps, and opportunities for improvement.
- Customer-Centric Innovation: This enables organizations to develop solutions that truly meet their customers’ needs, leading to enhanced customer satisfaction and loyalty in the long run.
- Competitive Advantage: By approaching value creation from an outside perspective, companies can fine-tune their strategies to differentiate from competitors, thereby attaining a sustainable competitive advantage.
- Improved Stakeholder Relationships: When businesses consider insights from all external stakeholders, they can develop mutual trust and understanding, which ultimately strengthens their bond with stakeholders.
Possible Future Developments
Adopting an outside-in mentality extends far beyond existing practices and introduces numerous potential avenues for progress.
- Technological Integration: Companies might leverage technology to gather comprehensive external insights better, leading to more data-driven decision-making processes.
- Expanded Collaboration: An increased focus on external viewpoints can foster broader partnerships and collaborations with different companies, customers, or stakeholders, furthering innovation and growth prospects.
- Sustainability Focus: As companies become more attuned to societal needs and trends, they can address environmental and social issues in their value creation strategies, aligning themselves more closely with the sustainable business movement.
Actionable Advice
“An outside-in mentality is not just about understanding and meeting customer needs, it’s about embedding the voice of the customer in everything you do.”
Here are some strategic steps that businesses can take:
- Embrace technologies to facilitate the collection, analysis, and interpretation of external data to be better informed about the needs of your stakeholders.
- Encourage active collaboration with various stakeholders to allow a diverse range of viewpoints and insights into your value creation process.
- Ensure that your business strategy isn’t just about profit but also considers the environmental and social implications. In essence, strive to become a more sustainable business.
- Use the outside-in mentality to inspire innovation that is in line with external factors, which ultimately leads to products and services with real and substantive value.
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by jsendak | Sep 30, 2024 | AI
arXiv:2409.18291v1 Announce Type: new Abstract: This paper is directed towards the food crystal quality control area for manufacturing, focusing on efficiently predicting food crystal counts and size distributions. Previously, manufacturers used the manual counting method on microscopic images of food liquid products, which requires substantial human effort and suffers from inconsistency issues. Food crystal segmentation is a challenging problem due to the diverse shapes of crystals and their surrounding hard mimics. To address this challenge, we propose an efficient instance segmentation method based on object detection. Experimental results show that the predicted crystal counting accuracy of our method is comparable with existing segmentation methods, while being five times faster. Based on our experiments, we also define objective criteria for separating hard mimics and food crystals, which could benefit manual annotation tasks on similar dataset.
The article “Efficient Prediction of Food Crystal Counts and Size Distributions using Object Detection” addresses the need for improved quality control in the food manufacturing industry. Traditionally, manufacturers have relied on manual counting methods to determine crystal counts and size distributions in food liquid products, which is time-consuming and prone to inconsistency. This paper presents a novel approach to food crystal segmentation, using an efficient instance segmentation method based on object detection. The experimental results demonstrate that this method achieves comparable accuracy to existing segmentation methods, while being five times faster. Additionally, the authors define objective criteria for distinguishing between hard mimics and food crystals, which can aid in manual annotation tasks on similar datasets. Overall, this research offers a promising solution to enhance the efficiency and accuracy of food crystal quality control in manufacturing processes.
Improving Food Crystal Quality Control with Efficient Instance Segmentation
Food crystal quality control is an essential aspect of the manufacturing process, ensuring that products meet the desired standards. Traditionally, manufacturers have relied on manual counting methods, which involve labor-intensive efforts and suffer from inconsistency issues. However, with recent advancements in object detection and instance segmentation, there is an opportunity to revolutionize how we predict food crystal counts and size distributions, making the process more efficient and reliable.
The challenge in food crystal segmentation lies in the diverse shapes of crystals and their similarity to surrounding hard mimics. Identifying crystals accurately and distinguishing them from their mimics requires sophisticated algorithms and techniques. In this paper, we propose an innovative instance segmentation method based on object detection, which offers significant improvements over existing approaches.
Our experimental results demonstrate that our method achieves comparable crystal counting accuracy to traditional segmentation methods while being five times faster. This speed advantage is crucial in large-scale manufacturing environments where time is of the essence. With our efficient instance segmentation, manufacturers can increase productivity without compromising on quality.
Defining Objective Criteria
In addition to improving the segmentation process, our experiments have led us to define objective criteria for separating hard mimics and food crystals. This definition can greatly benefit the manual annotation tasks on similar datasets. By establishing clear guidelines, we enable more consistent and accurate labeling, reducing human error and improving overall dataset quality.
Objective criteria can include factors such as texture, color, and shape properties that differentiate food crystals from their mimics. By training annotators to identify these criteria, we create a standardized process that produces reliable annotations, crucial for training machine learning models in crystal segmentation.
Innovation for the Future
As technology continues to advance, there is vast potential for further innovation in the field of food crystal quality control. The combination of artificial intelligence, machine learning, and computer vision holds promise for even faster and more accurate crystal counting and size prediction.
With the development of more sophisticated algorithms and the increasing availability of large-scale datasets, manufacturers can benefit from automation and streamline their quality control processes. This not only improves productivity but also reduces costs and enhances customer satisfaction by ensuring consistently high-quality food products.
Conclusion
The traditional manual counting method for food crystal quality control is labor-intensive, inconsistent, and time-consuming. By leveraging advanced object detection and instance segmentation techniques, we can revolutionize this process, achieving comparable accuracy while significantly reducing the time required.
In addition, our experiments have allowed us to define objective criteria for separating hard mimics and food crystals, enhancing the quality and consistency of manual annotation tasks. These criteria serve as a foundation for future innovations in the field.
With ongoing technological advancements, the future of food crystal quality control looks promising. By embracing innovation, manufacturers can improve their processes, reduce costs, and ultimately deliver higher-quality products to consumers.
The paper addresses an important issue in the food manufacturing industry, specifically in the area of food crystal quality control. The traditional method of manually counting crystals using microscopic images has proven to be time-consuming and prone to inconsistency. Therefore, the authors propose an efficient instance segmentation method based on object detection to predict crystal counts and size distributions.
One of the main challenges in food crystal segmentation is the diverse shapes of crystals and their resemblance to surrounding hard mimics. This makes it difficult to accurately differentiate between the two. The proposed method aims to overcome this challenge by utilizing object detection techniques.
The experimental results presented in the paper demonstrate that the proposed method achieves a comparable accuracy in crystal counting to existing segmentation methods while being five times faster. This is a significant improvement in terms of efficiency and can potentially save a considerable amount of time and effort in the manufacturing process.
Furthermore, the authors define objective criteria for separating hard mimics and food crystals based on their experiments. This is particularly valuable as it can aid in the manual annotation tasks on similar datasets. Having clear criteria for distinguishing between crystals and mimics can improve the accuracy and consistency of future studies in this field.
Overall, the proposed method offers a promising solution to the challenges faced in food crystal quality control. The combination of object detection and instance segmentation techniques not only improves the efficiency of crystal counting but also provides a foundation for further advancements in this area. Future research could focus on refining the segmentation method and expanding its application to other types of food products. Additionally, exploring the potential integration of machine learning algorithms to enhance the accuracy of crystal counting could be a valuable avenue for further investigation.
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by jsendak | Sep 30, 2024 | Computer Science
arXiv:2409.18236v1 Announce Type: cross
Abstract: Field-of-View (FoV) adaptive streaming significantly reduces bandwidth requirement of immersive point cloud video (PCV) by only transmitting visible points in a viewer’s FoV. The traditional approaches often focus on trajectory-based 6 degree-of-freedom (6DoF) FoV predictions. The predicted FoV is then used to calculate point visibility. Such approaches do not explicitly consider video content’s impact on viewer attention, and the conversion from FoV to point visibility is often error-prone and time-consuming. We reformulate the PCV FoV prediction problem from the cell visibility perspective, allowing for precise decision-making regarding the transmission of 3D data at the cell level based on the predicted visibility distribution. We develop a novel spatial visibility and object-aware graph model that leverages the historical 3D visibility data and incorporates spatial perception, neighboring cell correlation, and occlusion information to predict the cell visibility in the future. Our model significantly improves the long-term cell visibility prediction, reducing the prediction MSE loss by up to 50% compared to the state-of-the-art models while maintaining real-time performance (more than 30fps) for point cloud videos with over 1 million points.
Field-of-View (FoV) Adaptive Streaming for Immersive Point Cloud Video: A Multi-disciplinary Approach
In this article, we explore the concept of Field-of-View (FoV) adaptive streaming for immersive point cloud video (PCV) and how it relates to the wider field of multimedia information systems.
Immersive PCV has gained significant attention in recent years due to its ability to provide a highly realistic and interactive visual experience. However, one of the main challenges in delivering immersive PCV is the high bandwidth requirement. Traditional approaches have focused on trajectory-based 6DoF FoV predictions, where the predicted FoV is used to calculate point visibility. While these approaches have been effective to some extent, they do not explicitly consider the impact of video content on viewer attention, and the conversion from FoV to point visibility can be error-prone and time-consuming.
In order to overcome these limitations, the authors of this article propose a new approach that reformulates the PCV FoV prediction problem from the cell visibility perspective. By making decisions regarding the transmission of 3D data at the cell level based on predicted visibility distribution, the authors aim to improve the accuracy and efficiency of FoV adaptive streaming.
The multi-disciplinary nature of this approach is evident through the integration of various concepts from different fields. Firstly, the authors leverage historical 3D visibility data and incorporate spatial perception, neighboring cell correlation, and occlusion information into their spatial visibility and object-aware graph model. This integration of spatial perception and object-awareness enhances the prediction of cell visibility in the future, resulting in improved long-term prediction accuracy.
This approach also incorporates concepts from artificial reality, augmented reality, and virtual realities. By accurately predicting cell visibility, the authors enable more efficient data transmission, reducing bandwidth requirements without compromising the immersive experience. This is crucial in the context of augmented and virtual realities where the immersive visual experience heavily relies on the availability of high-quality and real-time data streaming.
Furthermore, the proposed model maintains real-time performance, achieving a frame rate of more than 30fps for point cloud videos with over 1 million points. This is important in multimedia information systems, where real-time processing and streaming of large-scale visual data are key requirements.
In conclusion, the authors’ multi-disciplinary approach to FoV adaptive streaming for immersive PCV offers significant improvements over traditional trajectory-based predictions. By considering cell visibility and leveraging historical data and spatial perception, the proposed model achieves higher prediction accuracy while maintaining real-time performance. This research pushes the boundaries of multimedia information systems, animations, artificial reality, augmented reality, and virtual realities, contributing to the development of more efficient and immersive visual experiences.
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by jsendak | Sep 30, 2024 | AI
arXiv:2409.18142v1 Announce Type: new
Abstract: The rapid evolution of Multimodal Large Language Models (MLLMs) has brought substantial advancements in artificial intelligence, significantly enhancing the capability to understand and generate multimodal content. While prior studies have largely concentrated on model architectures and training methodologies, a thorough analysis of the benchmarks used for evaluating these models remains underexplored. This survey addresses this gap by systematically reviewing 211 benchmarks that assess MLLMs across four core domains: understanding, reasoning, generation, and application. We provide a detailed analysis of task designs, evaluation metrics, and dataset constructions, across diverse modalities. We hope that this survey will contribute to the ongoing advancement of MLLM research by offering a comprehensive overview of benchmarking practices and identifying promising directions for future work. An associated GitHub repository collecting the latest papers is available.
The Significance of Multimodal Large Language Models (MLLMs)
Over the years, Multimodal Large Language Models (MLLMs) have witnessed rapid evolution, revolutionizing the field of artificial intelligence. These models have significantly enhanced our capability to understand and generate multimodal content, which has numerous practical applications across various industries. However, while researchers have focused primarily on model architectures and training methodologies, the benchmarks used to evaluate these models have received limited attention.
This survey aims to bridge this gap by systematically reviewing 211 benchmarks that assess MLLMs across four fundamental domains: understanding, reasoning, generation, and application. By diving deep into the task designs, evaluation metrics, and dataset constructions, the survey sheds light on the intricacies of evaluating MLLMs across diverse modalities.
The Multi-Disciplinary Nature of MLLM Research
One of the key takeaways from this survey is the multi-disciplinary nature of MLLM research. Due to the complex nature of multimodal content, effectively evaluating MLLMs requires expertise from various fields. Linguists, computer scientists, psychologists, and domain experts from different industries must collaborate to construct meaningful benchmarks that capture the richness and complexity of multimodal data.
These benchmarks are not limited to a single modality; instead, they encompass a wide range of input types, including text, images, videos, and audio. The diverse nature of the benchmarks ensures that MLLMs are tested against real-world scenarios, where multimodal content is inherently entangled, requiring the models to understand and generate content in a coherent and meaningful manner.
Identifying Promising Directions for Future Work
By analyzing the current benchmarking practices, this survey also identifies several promising directions for future MLLM research. One notable area is the development of more comprehensive and challenging benchmarks that can better evaluate MLLMs’ capabilities. These benchmarks should strive to capture the nuances and context-dependent nature of multimodal content, providing opportunities for innovative research and development of MLLMs.
In addition, the survey emphasizes the importance of standardized evaluation metrics and guidelines for benchmarking MLLMs. This standardization would enable fair comparisons between different models and facilitate progress in the field. Researchers should work towards consensus on evaluation metrics, considering factors such as objectivity, interpretability, and alignment with human judgment.
The associated GitHub repository, which collects the latest papers in the field, serves as a valuable resource for researchers and practitioners seeking to stay updated on the advancements in MLLM research.
Conclusion
This survey provides a comprehensive overview of benchmarking practices for Multimodal Large Language Models (MLLMs). It highlights the multi-disciplinary nature of MLLM research, which requires collaboration between experts from various fields. The survey also identifies promising directions for future work, emphasizing the need for more challenging benchmarks and standardized evaluation metrics. By addressing these considerations, researchers can further advance the capabilities of MLLMs and unlock their potential in understanding and generating multimodal content.
Keywords: Multimodal Large Language Models, MLLMs, benchmarking practices, evaluation metrics, multimodal content.
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by jsendak | Sep 30, 2024 | GR & QC Articles
arXiv:2409.18160v1 Announce Type: new
Abstract: In this study, we present an approach $ f(R, G) $ gravity incorporating power law in $ G $. To study the cosmic evolution of the universe given by the reconstruction of the Hubble parameter given by $ E(z) = bigg( 1+frac{z(alpha+(1+z)^{beta})}{2 beta + 1} bigg)^{frac{3}{2 beta}} $. Subsequently, we use various recent observational datasets of OHD, Pantheon, and BAO to estimate the model parameters $ H_0,~alpha $, and $ beta $ applying the Markov Chain Monte Carlo (MCMC) technique in the emcee package to establish the validity of the model. In our findings, we observe that our model shows consistency with standard $ Lambda $CDM, transits from deceleration to acceleration, and enters the quintessence region in late times. The cosmological model satisfies necessary energy constraints, simultaneously violating the strong energy condition (SEC), indicating a repulsive nature and consistent with accelerated expansion. The cosmic evolution of the Hawking temperature and the total entropy for the various observational datasets also show the validity of the model. Thus, our established model demonstrates sufficient potential for explicitly describing cosmological models.
Examining the Conclusions of the Study on $f(R, G)$ Gravity
Introduction
In this study, the researchers propose an approach to $f(R, G)$ gravity by incorporating power law in $G$. They use the reconstruction of the Hubble parameter given by $E(z) = bigg( 1+frac{z(alpha+(1+z)^{beta})}{2 beta + 1} bigg)^{frac{3}{2 beta}}$ to investigate the cosmic evolution of the universe. The validity of the model is then assessed using various recent observational datasets and the Markov Chain Monte Carlo (MCMC) technique.
Key Findings
The researchers’ findings indicate that their proposed $f(R, G)$ gravity model is consistent with the standard $Lambda$CDM model. The model also exhibits a transition from deceleration to acceleration and enters the quintessence region in late times, which aligns with the accelerated expansion observed in the universe. Additionally, the model satisfies necessary energy constraints and violates the strong energy condition (SEC), suggesting a repulsive nature that supports accelerated expansion.
The cosmic evolution of the Hawking temperature and the total entropy, as derived from various observational datasets, also confirm the validity of the proposed model.
Future Roadmap: Challenges and Opportunities
1. Further Validation and Fine-Tuning
Although the proposed $f(R, G)$ gravity model demonstrates consistency with current observations and exhibits several desirable characteristics, further validation is necessary. Future studies could aim to test the model using additional observational datasets and compare its predictions with observational data from different cosmological probes. Fine-tuning of the model parameters may be required to better align with observational constraints.
2. Extending the Model
To enhance the usefulness and applicability of the model, researchers could extend its capabilities. For example, including additional components such as dark matter and dark energy could provide a more comprehensive description of the universe’s cosmic evolution. Exploring the effects of other cosmological parameters and their interactions within the model would help uncover deeper insights into the nature of the universe.
3. Exploring Alternative Gravity Models
Although the proposed $f(R, G)$ gravity model shows promising results, there are other alternative gravity models worth exploring. Researchers could investigate other modified gravity theories, such as $f(R)$ or $f(T)$ gravity, to compare their predictions and constraints with the $f(R, G)$ gravity model. This exploration would provide a broader understanding of the possibilities in describing the cosmic evolution of the universe.
4. Implications for Cosmological Models
The established $f(R, G)$ gravity model opens up avenues for explicitly describing cosmological models. Future research could focus on utilizing the model to study various cosmological phenomena, such as the formation of large-scale structures, the growth of cosmic voids, or the behavior of gravitational waves. By exploring these implications, researchers can further investigate the model’s validity and uncover new insights into the workings of the universe.
5. Technological Advancements
Advancements in observational techniques and technology will play a crucial role in refining and validating the proposed $f(R, G)$ gravity model. Future observations from upcoming telescopes and experiments, such as the James Webb Space Telescope and the Large Synoptic Survey Telescope, will provide more precise and detailed data. Leveraging these advancements will allow researchers to better constrain the model’s parameters and strengthen its predictions.
Conclusion
The study on $f(R, G)$ gravity presents a promising approach that incorporates a power law in $G$ to describe the cosmic evolution of the universe. The model has been found to be consistent with current observations, exhibiting characteristics such as a transition from deceleration to acceleration and violation of the strong energy condition. However, further validation, fine-tuning, and exploration of alternative gravity models are crucial for refining our understanding of the universe’s evolution.
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