Potential Future Trends in High-End Computing at NASA
High-end computing plays a crucial role in NASA’s missions, allowing scientists and researchers to advance our understanding of the universe. From exploring deep space to improving climate models, supercomputers enable projects that have far-reaching impacts on space exploration and life on Earth. As technology continues to advance, here are some potential future trends in high-end computing at NASA:
1. Advanced Simulation Techniques
NASA Ames has been using advanced simulation techniques to redesign the launch environment for the Artemis II mission, scheduled for 2025. By simulating the interactions between the rocket plume and the water-based sound suppression system, researchers were able to identify potential issues and make necessary adjustments. These simulations, run on the Aitken supercomputer, generated massive amounts of data, highlighting the need for more efficient data processing and analysis.
In the future, as computing power increases, NASA can expect to utilize more advanced simulation techniques, such as computational fluid dynamics and virtual reality simulations, to solve complex problems and improve mission planning. This will require supercomputers with higher processing capabilities and improved algorithms for data analysis.
2. Optimization for Fuel Efficiency
Another emerging trend in high-end computing at NASA is the optimization of aircraft designs for fuel efficiency. By fine-tuning the shape of wings, fuselages, and other structural components, researchers at NASA’s Ames Research Center aim to reduce air resistance and improve overall performance. The use of computational modeling software allows for hundreds of simulations to explore design possibilities.
In the future, the focus on fuel efficiency and sustainability in the aviation industry is likely to increase. NASA can continue to contribute to this trend by developing more sophisticated optimization algorithms and leveraging powerful supercomputers to run simulations quickly and accurately. This will enable researchers to identify the most efficient design configurations, leading to significant fuel savings and reduced emissions.
3. Artificial Intelligence in Weather and Climate Modeling
NASA and its partners are exploring the use of artificial intelligence (AI) techniques in weather and climate modeling. By training foundation models using large, unlabeled datasets, researchers can fine-tune results for different applications, such as weather prediction and climate projection. The Prithvi Weather-Climate foundation model, developed by NASA in collaboration with IBM Research, was pretrained using the newest NVIDIA A100 GPUs at the NASA Advanced Supercomputing facility.
In the future, AI will likely play a more prominent role in weather and climate modeling. Improvements in AI algorithms, coupled with increased computing power, will enable researchers to develop more accurate and efficient models. This will lead to better weather forecasts, improved climate projections, and a deeper understanding of complex atmospheric processes.
4. Integration of Simulation, Observation, and AI
Neutron stars, one of the densest objects in the universe, remain mysterious to scientists. To unravel their mysteries, researchers at NASA’s Goddard Space Flight Center are using a combination of simulation, observation, and AI. By applying deep neural networks to data obtained from observatories like the Fermi Gamma-ray Space Telescope and Neutron star Interior Composition Explorer, scientists can infer properties of neutron stars, such as their mass, radius, and magnetic field structure.
In the future, the integration of simulation, observation, and AI will continue to advance our understanding of cosmic objects and phenomena. Supercomputers will play a crucial role in processing and analyzing vast amounts of data from space observatories, allowing researchers to make significant discoveries and guide future scientific missions.
5. Advanced Visualization Techniques
The massive amount of data generated by NASA simulations and observations can be challenging to comprehend in its original form. The Scientific Visualization Studio (SVS), based at NASA Goddard, collaborates with scientists to create cinematic visualizations that turn data into insight. These visualizations provide a better understanding of complex phenomena, such as solar jets and atmospheric circulation.
In the future, as data sizes continue to grow, advanced visualization techniques will become increasingly important. Supercomputers with powerful data analysis and image-rendering capabilities will be essential for creating high-fidelity visualizations that help scientists and the general public visualize and comprehend complex scientific data.
Recommendations for the Industry
Based on the potential future trends in high-end computing at NASA, here are some recommendations for the industry:
Invest in Research and Development: Continued investment in research and development is crucial to push the boundaries of high-end computing. This includes funding for developing more powerful supercomputers, improving algorithms, and exploring new simulation techniques.
Collaborate with Industry and Academic Partners: Collaborations with industry and academic partners can bring together expertise and resources to tackle complex challenges in high-end computing. By fostering partnerships, NASA can leverage the latest advancements in computer hardware, software, and AI algorithms.
Enhance Data Storage and Processing Capabilities: As the volume of data continues to increase, the industry should focus on developing advanced data storage and processing technologies. This includes faster and more efficient storage solutions, as well as data analytics tools that can handle large-scale datasets.
Promote Data Visualization and Communication: Communicating scientific data to a broader audience is crucial for public engagement and understanding. Investing in advanced visualization techniques and tools can help scientists and educators present complex data in a more accessible and engaging way.
Support Education and Training: To keep up with the rapidly evolving field of high-end computing, it is essential to invest in education and training programs. This includes providing opportunities for researchers, students, and professionals to learn about the latest technologies and techniques in high-performance computing.
By following these recommendations, the industry can support the advancement of high-end computing and contribute to scientific discoveries and innovations with real-world applications.
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Need to learn how to create a volcano plot in R and visualize differential gene expression effectively?
Creating a volcano plot in R is essential for any researcher working with bioinformatics and RNA-Seq data. It allows you to easily identify which genes are upregulated or downregulated with significant changes between conditions. Imagine visualizing hundreds of genes on a simple, elegant plot and instantly spot those that stand out due to their statistical significance. That's the power of a volcano plot.
Key points
A volcano plot is a type of scatter plot used in genomics to visualize significant changes in gene expression, usually between different conditions (e.g., treated vs. untreated). It helps researchers easily identify the most important genes to study further.
To create a volcano plot, the log2 fold change is plotted on the x-axis, and the log10 p-value is plotted on the y-axis. Genes on the right are upregulated, while those on the left are downregulated. Genes farther from the center are more significant.
Typical cut-offs for volcano plots are a p-value less than 0.05 and a log2 fold change greater than 1, but these values vary. Adjusted p-values are often preferred to reduce false positives in the analysis.
Volcano plots can be created using tools like ggplot2, EnhancedVolcano in R, or Excel for simpler visualizations. EnhancedVolcano provides easy customization for publication-quality plots.
Volcano plots are used to quickly identify key genes in sequencing studies like RNA-Seq. They are more informative than standard scatter plots as they show changes in size and significance. Additionally, they can be made as models for educational purposes using materials like clay or paper mache.
Table of Contents
Volcanoplot in R is essential for anyone working with bioinformatics and RNA-Seq data. It helps you quickly see which genes are upregulated (increased expression) or downregulated (decreased) between different conditions. Imagine looking at hundreds of genes on a simple plot and immediately noticing which ones have significant changes—that's the power of a volcano plot.
Volcano Plots in R
Volcano plots are widely used in bioinformatics fields to show differential gene expression. It will explain volcano plots, why they are essential in gene expression analysis, and how they help researchers see significant changes in their data.
What is a Volcano Plot?
A volcano plot is a type of scatter plot that shows statistical significance (usually the negative log10 of the p-value) against fold change (log2 fold change) of gene expression. It helps researchers quickly find differentially expressed genes that are either upregulated or downregulated.
Why Use Volcano Plots?
Volcano plots are very helpful for finding key genes in RNA-Seq or proteomics experiments. By plotting fold change and statistical significance, researchers can see which genes have important changes, making it easier to focus on the most interesting ones. Creating a volcano plot in R is a great way to see significant changes in gene expression, which helps find essential genes in bioinformatics research.
Feature
Volcano Plot Benefits
Visualization Type
Scatter plot showing changes in gene expression
Key Metrics Displayed
Log2 fold change vs. -log10 p-value
Upregulated/Downregulated Genes
Quickly identifies which genes are more or less active between conditions
Quick Identification
Enables researchers to spot significant genes at a glance
Data Interpretation
Makes it simple to understand large datasets of gene activity
Implications and Future Developments Surrounding Volcano Plots in Bioinformatics
The value of plotting significant gene changes using volcano plots in R is ever-growing with the increased use of bioinformatics in health and disease research. Given such utility, this article discusses the long-term implications and possible future developments with a focus on the bioinformatics field and beyond.
Long-term Implications
Facilitation of Precision Medicine: As more information is discovered about expression changes in genes under different conditions, this could bolster the development and implementation of precision medicines, targeted to individual genomic profiles.
Accelerated Medical Research: With the ability to easily identify which genes are significantly up or downregulated, medical researchers can meet critical research objectives faster, accelerating the path to new treatments and therapies.
Enhanced Data Interpretation & Accessibility: As stated in the text, Volcano plots help in understanding large datasets of gene activity, providing an accessible path to data interpretation for a broader set of scientists, not just those specialized in genomics.
Future Developments
Given these long-term implications and the increasing dependence on data visualization in interpreting complex gene expression profiles, we can anticipate several advances in the use of Volcano plots.
Advanced Software Implementation: As bioinformatics continues to develop, we could expect enhanced software applications that further simplify the creation of volcano plots and other visualizations, with more customization options dedicated to presenting genomic data.
Integration with Machine Learning: Combining the interpretive power of machine learning with the clarity of volcano plots, it would be much simpler to classify and predict patterns of gene expression under different experimental conditions.
Virtual and Augmented Reality Models: To further enhance visualization and data interpretation, we might see future development of VR and AR models for volcano plots and other similar data visualization strategies.
Actionable Advice
For bioinformatics researchers, data analysts and others utilizing R for data interpretation in genomics:
Invest time in mastering ggplot2, EnhancedVolcano, and other similar data visualization tools in R. These tools increase efficiency and enhance the interpretation of complex genomic data.
Stay abreast of new software developments that could provide easier, more customizable methods for creating volcano plots.
Consider advancing your skills in machine learning techniques that can supplement data visualization for pattern recognition and prediction.
Be open to emerging strategies for data visualization and interpretation like virtual and augmented reality, as they could provide further breakthroughs in understanding gene expression data.
arXiv:2411.05285v1 Announce Type: new Abstract: The ever-improving quality of LLMs has fueled the growth of a diverse range of downstream tasks, leading to an increased demand for AI automation and a burgeoning interest in developing foundation model (FM)-based autonomous agents. As AI agent systems tackle more complex tasks and evolve, they involve a wider range of stakeholders, including agent users, agentic system developers and deployers, and AI model developers. These systems also integrate multiple components such as AI agent workflows, RAG pipelines, prompt management, agent capabilities, and observability features. In this case, obtaining reliable outputs and answers from these agents remains challenging, necessitating a dependable execution process and end-to-end observability solutions. To build reliable AI agents and LLM applications, it is essential to shift towards designing AgentOps platforms that ensure observability and traceability across the entire development-to-production life-cycle. To this end, we conducted a rapid review and identified relevant AgentOps tools from the agentic ecosystem. Based on this review, we provide an overview of the essential features of AgentOps and propose a comprehensive overview of observability data/traceable artifacts across the agent production life-cycle. Our findings provide a systematic overview of the current AgentOps landscape, emphasizing the critical role of observability/traceability in enhancing the reliability of autonomous agent systems.
The article “AgentOps: Observability and Traceability for Reliable AI Agent Systems” explores the growing demand for AI automation and the development of foundation model (FM)-based autonomous agents. As AI agent systems become more complex and involve various stakeholders, including users, developers, and model creators, the need for reliable outputs and answers from these agents becomes challenging. The article suggests that to build reliable AI agents and LLM applications, there is a need to shift towards designing AgentOps platforms that ensure observability and traceability throughout the development-to-production life-cycle. The authors conducted a rapid review and identified relevant AgentOps tools, providing an overview of their essential features and proposing a comprehensive overview of observability data and traceable artifacts. The findings emphasize the critical role of observability and traceability in enhancing the reliability of autonomous agent systems.
The Importance of Observability and Traceability in Building Reliable AI Agent Systems
The ever-improving quality of Language Model Models (LLMs) has led to a wide range of downstream tasks and an increased demand for AI automation. As a result, there is a growing interest in developing foundation model (FM)-based autonomous agents. With the evolution of AI agent systems, the involvement of various stakeholders and the integration of multiple components, obtaining reliable outputs and answers from these agents remains a challenge. This necessitates the need for a dependable execution process and end-to-end observability solutions.
To ensure the reliability of AI agents and LLM applications, a shift towards designing AgentOps platforms is crucial. These platforms provide observability and traceability across the entire development-to-production life-cycle. In order to understand the current AgentOps landscape, we conducted a rapid review and identified relevant tools from the agentic ecosystem. Based on our findings, we propose a comprehensive overview of observability data and traceable artifacts across the agent production life-cycle.
What is AgentOps?
AgentOps refers to the set of tools, practices, and methodologies employed in the development and operation of autonomous agent systems. It encompasses various aspects such as agent workflows, RAG pipelines, prompt management, agent capabilities, and observability features. By ensuring observability and traceability, AgentOps aims to enhance the reliability of AI agents and provide actionable insights into their behavior and performance.
The Role of Observability
Observability plays a critical role in AgentOps by providing visibility into the inner workings of AI agents. It allows developers and deployers to monitor and understand the agent’s behavior, performance, and any potential issues or anomalies. With observability, stakeholders can gain insights into how agents make decisions, identify bottlenecks in the workflow, and optimize performance.
“Observability provides visibility into the inner workings of AI agents, allowing developers and deployers to monitor and understand their behavior, performance, and any potential issues or anomalies.”
The Importance of Traceability
Traceability in AgentOps refers to the ability to trace the artifacts and inputs that contribute to the agent’s outputs. By tracing back the sources of information, prompts, and training data, stakeholders can ensure the accountability and transparency of the AI agent system. Traceability also aids in debugging and troubleshooting, as it allows developers to identify the root causes of errors or biased outputs.
Proposed Overview of Observability and Traceability Data
Based on our rapid review of AgentOps tools, we propose an overview of the essential features that ensure observability and traceability across the agent production life-cycle:
Agent Workflows: Tools that provide visibility into the steps and processes involved in agent operation, including data ingestion, preprocessing, training, and inference.
RAG Pipelines: Solutions that enable the monitoring of the Retrieval-Augmented Generation (RAG) process, which combines information retrieval and language generation tasks.
Prompt Management: Tools for managing and analyzing prompts used to guide the behavior of AI agents, ensuring transparency and fairness in responses.
Agent Capabilities: Features that allow developers and end-users to evaluate and understand the agent’s capabilities, limitations, and performance metrics.
Observability Features: Metrics, logs, and visualizations that provide insights into the agent’s behavior, performance, and potential issues.
Traceable Artifacts: Mechanisms for tracing the sources and inputs that contribute to the agent’s outputs, including training data, prompts, and intermediate artifacts.
Conclusion
In order to build reliable AI agents and LLM applications, the integration of observability and traceability is paramount. AgentOps platforms that provide end-to-end visibility and traceability across the agent production life-cycle enable stakeholders to understand and enhance the reliability of autonomous agent systems. By leveraging observability data and traceable artifacts, we can ensure transparency, accountability, and continuous improvement in AI agent development and deployment.
The paper discusses the growing demand for AI automation and the development of foundation model (FM)-based autonomous agents. As AI agents tackle more complex tasks, there is a need for reliable outputs and answers from these agents. This necessitates a dependable execution process and end-to-end observability solutions.
The authors argue that to build reliable AI agents and LLM (Language Model) applications, it is crucial to shift towards designing AgentOps platforms that ensure observability and traceability throughout the development-to-production life-cycle. AgentOps platforms would allow stakeholders, including agent users, agentic system developers and deployers, and AI model developers, to have visibility into the inner workings of the AI agent systems.
The authors conducted a rapid review and identified relevant AgentOps tools from the agentic ecosystem. They provide an overview of the essential features of AgentOps and propose a comprehensive overview of observability data/traceable artifacts across the agent production life-cycle. This systematic overview of the current AgentOps landscape highlights the critical role of observability/traceability in enhancing the reliability of autonomous agent systems.
Overall, this paper addresses an important aspect of AI agent development and highlights the need for observability and traceability in ensuring the reliability of these systems. By providing an overview of existing AgentOps tools and emphasizing their importance, the authors contribute to the ongoing research and development in this area.
Looking ahead, it would be interesting to see further research on specific techniques and methodologies to enhance observability and traceability in AI agent systems. Additionally, as AI agents become more advanced and autonomous, there may be a need to address ethical considerations and potential biases that could arise in their decision-making processes. Exploring how observability and traceability can be utilized to mitigate these concerns would be a valuable direction for future research. Read the original article
arXiv:2410.19459v1 Announce Type: new
Abstract: Neural Radiance Fields (NeRF) have revolutionized the field of 3D visual representation by enabling highly realistic and detailed scene reconstructions from a sparse set of images. NeRF uses a volumetric functional representation that maps 3D points to their corresponding colors and opacities, allowing for photorealistic view synthesis from arbitrary viewpoints. Despite its advancements, the efficient streaming of NeRF content remains a significant challenge due to the large amount of data involved. This paper investigates the rate-distortion performance of two NeRF streaming strategies: pixel-based and neural network (NN) parameter-based streaming. While in the former, images are coded and then transmitted throughout the network, in the latter, the respective NeRF model parameters are coded and transmitted instead. This work also highlights the trade-offs in complexity and performance, demonstrating that the NN parameter-based strategy generally offers superior efficiency, making it suitable for one-to-many streaming scenarios.
Neural Radiance Fields (NeRF) Streaming Strategies: A Closer Look
Neural Radiance Fields (NeRF) have revolutionized the field of 3D visual representation by enabling highly realistic and detailed scene reconstructions from a sparse set of images. This breakthrough has paved the way for photorealistic view synthesis from arbitrary viewpoints, opening up new possibilities in various domains such as virtual reality, augmented reality, and multimedia information systems.
However, one significant challenge that researchers and practitioners face is the efficient streaming of NeRF content. The large amount of data involved in representing these highly detailed scenes poses a daunting task in terms of transmission and rendering in real-time scenarios. To address this challenge, a recent paper investigates the rate-distortion performance of two NeRF streaming strategies: pixel-based and neural network (NN) parameter-based streaming.
The first strategy, pixel-based streaming, involves coding and transmitting images throughout the network. This approach allows for more straightforward encoding and decoding but requires a large amount of data to be transmitted, leading to potential bandwidth limitations and increased latency.
On the other hand, the second strategy, NN parameter-based streaming, focuses on coding and transmitting the respective NeRF model parameters instead of the images themselves. This approach offers a more efficient alternative as it reduces the amount of data that needs to be transmitted. By leveraging the learned parameters of the neural network, the reconstruction process can be performed on the receiver’s end, resulting in higher efficiency and lower bandwidth requirements.
The paper’s findings highlight the trade-offs between complexity and performance when comparing the two streaming strategies. In general, the NN parameter-based strategy offers superior efficiency and reduced data transmission requirements, making it particularly suitable for one-to-many streaming scenarios. This finding is crucial in the context of multimedia information systems, animations, artificial reality, augmented reality, and virtual realities, where real-time rendering and transmission of complex scenes are essential.
The multi-disciplinary nature of the concepts explored in this work is evident. It combines techniques from computer graphics, machine learning, image and video coding, and multimedia systems to address the challenges of streaming NeRF content efficiently. By leveraging neural network architectures and understanding the interplay between the volumetric representation of scenes and data transmission, researchers can further enhance the realism and accessibility of complex 3D visualizations.
In conclusion, the study of streaming strategies for Neural Radiance Fields (NeRF) opens up exciting possibilities in the field of multimedia information systems, animations, artificial reality, augmented reality, and virtual realities. The findings of this paper shed light on the trade-offs and efficiencies of different approaches, allowing for improved real-time rendering and transmission of highly detailed 3D scenes. As researchers continue to delve into the multi-disciplinary aspects of this field, we can expect further advancements in the quality and accessibility of virtual visual experiences. Read the original article
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Join our workshop onVisualizing Variance with Sankey diagrams/Riverplots using R: An Illustration with Longitudinal Multi-level Modeling, which is a part of our workshops for Ukraine series!
Here’s some more info:
Title: Visualizing Variance with Sankey diagrams/Riverplots using R: An Illustration with Longitudinal Multi-level Modeling
Date: Thursday, November 26th, 18:00 – 20:00 CET (Rome, Berlin, Paris timezone)
Speaker: Daniel P. Moriarity, PhD is a clinical psychologist with a particular interest in immunopsychiatry, psychiatric phenotyping, and methods reform in biological psychiatry. He currently works as a Postdoctoral Fellow in the UCLA Laboratory for Stress Assessment and Research with Dr. George Slavich. Starting January 2025, he will join the University of Pennsylvania’s Psychology Department as an Assistant Professor of Clinical Psychology.
Description: This workshop will illustrate how to create Sankey diagrams/Riverplots with a focus on longitudinal multilevel modeling to separately visualize between-person and within-person variance. However, the technique can be applied to many other visualizations of different sources of variance (e.g., different variables, random vs. fixed effects). Data + code templates will be provided to follow along with.
Minimal registration fee: 20 euro (or 20 USD or 800 UAH)
Please note that the registration confirmation email will be sent 1 day before the workshop.
Save your donation receipt (after the donation is processed, there is an option to enter your email address on the website to which the donation receipt is sent)
Fill in the registration form, attaching a screenshot of a donation receipt (please attach the screenshot of the donation receipt that was emailed to you rather than the page you see after donation).
If you are not personally interested in attending, you can also contribute by sponsoring a participation of a student, who will then be able to participate for free. If you choose to sponsor a student, all proceeds will also go directly to organisations working in Ukraine. You can either sponsor a particular student or you can leave it up to us so that we can allocate the sponsored place to students who have signed up for the waiting list.
Save your donation receipt (after the donation is processed, there is an option to enter your email address on the website to which the donation receipt is sent)
Fill in the sponsorship form, attaching the screenshot of the donation receipt (please attach the screenshot of the donation receipt that was emailed to you rather than the page you see after the donation). You can indicate whether you want to sponsor a particular student or we can allocate this spot ourselves to the students from the waiting list. You can also indicate whether you prefer us to prioritize students from developing countries when assigning place(s) that you sponsored.
If you are a university student and cannot afford the registration fee, you can also sign up for the waiting listhere. (Note that you are not guaranteed to participate by signing up for the waiting list).
You can also find more information about this workshop series, a schedule of our future workshops as well as a list of our past workshops which you can get the recordings & materials here.
Looking forward to seeing you during the workshop!
Long-Term Implications and Future Developments of Sankey Diagrams/Riverplots using R Workshop
The announcement and detailed description of an educational workshop titled “Visualizing Variance with Sankey diagrams/Riverplots using R: An Illustration with Longitudinal Multi-level Modeling” indicates the increasing importance of data visualization as well as the application of R programming in the field of data science. This workshop has long-term implications not only for participants but for the wider community of data scientists and analysts.
Future Impact on Data Visualization and R Programming
The focus of the workshop on demonstrating how to create Sankey diagrams/Riverplots using R for visualizing variance in longitudinal multilevel modeling marks a growing trend towards the application of R for data analysis and visualization. As illustrated by the speaker’s profile, such workshops are increasingly bridging the gap between complex statistical analysis and clinical psychology. This trend may lead to a future where data science plays an even more integral role in psychiatry and medical research.
Implications on Educational Workshops and Philanthropy
The workshop also sets a pattern for future initiatives where education and philanthropy intersect. The unique registration process, which involves making a donation, demonstrates a commitment to supporting humanitarian causes. This innovative method could pave the way for similar charitable/scholarship-based initiatives, where the participation fee doubles as a donation to a good cause. Also, the model of sponsoring students’ participation could lead to an increase in opportunities and accessibility of such workshops for students who cannot afford the registration fee.
Actionable Advice
Expand Knowledge Base: Data scientists, analysts, and psychologists should consider participating in or promoting such workshops. These workshops provide an opportunity to expand one’s knowledge base and learn how to use R for visualizing complex statistical data.
Incorporate Philanthropic Initiatives: Anyone organizing educational workshops or courses should consider integrating philanthropic initiatives into their registration process. The model wherein participation fees are donated to a cause not only fosters a sense of social responsibility but also provides a novel way to support humanitarian causes.
Promote Student Sponsorship: Businesses and individuals should consider sponsoring students who cannot afford the fees of such valuable workshops. This not only aids in the dissemination of knowledge but also helps in fostering a culture of learning and inclusivity.
