We had the pleasure of sitting down with Kirsten Bulsink, a data scientist at the Dutch National Institute for Public Health and the Environment (RIVM). Our discussion covered her journey from pandemic response to R-package development and how the Netherlands eScience Center played a part in creating a crucial part of tooling at RIVM. Her story demonstrates the importance of collaborative work in research.
Q: Can you tell us about your background and current role at RIVM?
A: I’ve been working at RIVM for a little over three years now. My background is in psychology, with a master’s in neuroscience. During my Research Master’s, I discovered my passion for data analysis and finding answers through data. This led me to pursue a minor in data science.
I started working at RIVM during the COVID-19 pandemic. Initially, it was a chaotic time, with researchers working overtime to analyze and report data quickly. When I joined, there was already a semi-automatic data pipeline in place, but we still had to tackle complex challenges, like calculating vaccination rates with data from a selected group (because of opt-out).
As our team grew to about 9 to 10 people, we started organizing workshops to reflect on our processes. We asked ourselves what worked well and what we’d do differently if we could start over. This reflection led to the development of new tools and approaches.
“…we started organizing workshops to reflect on our processes. We asked ourselves what worked well and what we’d do differently if we could start over. This reflection led to the development of new tools and approaches.”
Before the pandemic, processes and methods differed for different infectious diseases. As a result, researchers at RIVM had to perform many actions manually, and these processes could differ per infectious disease. The pandemic necessitated more knowledge sharing and collaboration. We started standardizing and automating data transformation and reporting for infectious diseases.
Q: We understand that you and your colleague participated in the R-packaging workshop organized by the eScience Center. Can you tell us about that experience and the R-package your team developed?
Yes, that’s correct. One of my colleagues actually took the R-packaging workshop offered by the eScience Center before I did. Later, I also had the opportunity to take the same course.
The package, which now serves as a core tool for epidemiological pipelines at RIVM, provides functionality for loading, cleaning, and reporting data, with various checks in place. It also includes functions to create graphs in RIVM colors and style.
For example, during the COVID-19 pandemic, we used analysis methods to process data on positive cases, calculate the number of cases over time, and generate reports. Now, we use the package for monitoring and reporting on various infectious diseases like sexually transmitted infections and respiratory infections, not just COVID-19.
Participants of the RIVM-eScience Center hackathon
How did the R-packaging workshop help professionalize your package?
After joining the workshop at the Netherlands eScience Center, I organized a session for my team to share what I had learned. While my colleagues had already done a great job, the workshop helped us improve consistency in managing dependencies. We also enhanced our documentation. The package improvements made it easier for others to use the package. Installation became smoother, and users no longer had to figure out why they needed to install extra packages.
“The package improvements made it easier for others to use the package. Installation became smoother, and users no longer had to figure out why they needed to install extra packages.”
Later on, I also took the Python software development course offered by the eScience Center, which was really eye-opening. I learned about tools like linters, virtual environments, testing, coverage, and CI/CD pipelines. This knowledge made us realize we needed to implement these practices in our R-package as well.
Q: What led to the decision to organize hackathons for further package development, and how did the eScience Center get involved?
After gaining all this knowledge from the eScience Center courses, we felt ready to take our package to the next level. We decided to organize hackathons to focus on implementing best practices and improving our package structure.
Our first main goal was to internally demonstrate that we had a high-quality product, especially since many analyses of infectious disease data rely on this package. Our second goal was to share our methodology with external parties like the GGD (Municipal Health Services), even if we couldn’t share the actual data.
We reached out to the eScience Center training team for support, and they connected us with Pablo Rodríguez Sánchez (one of the eScience Center’s Research Software Engineers (RSEs) and main author of the R-packaging course, ed.) to consult during our hackathon. This collaboration was very valuable in guiding our efforts and providing expert insights.
Q: What were the outcomes of the hackathons?
We had two hackathons. In the first one, we focused on testing and documentation. We increased our test coverage and improved our package documentation, including creating a vignette with examples.
The second hackathon was about splitting our large package into smaller, more manageable ones. We also worked on establishing a workflow for potentially publishing the package on GitHub while keeping our main development on RIVM’s internal GitLab.
Pablo provided a fresh perspective and helped us confirm that we were on the right track. His expertise was particularly valuable in the second hackathon when we were making decisions about package structure and workflow.
“Pablo Rodríguez-Sánchez, Research Software Engineer (RSE) at the Netherlands eScience Center, provided a fresh perspective and helped us confirm that we were on the right track. His expertise was particularly valuable in the second hackathon when we were making decisions about package structure and workflow.”
Q: How has this experience changed your team’s way of working?
In the past year, we’ve started to work much more like a software development team. We now use a Kanban board for project management and have implemented CI/CD pipelines, which have made our development process much smoother. The package split has made everything more manageable, and it’s easier to see where we need certain tests or improvements.
Q: What’s next for your package and team?
We’re planning to release some of our packages in GitHub in the next couple of months, which will allow external users to download and use them. We’re also focusing on internal knowledge sharing and running workshops about our tooling.
We value having the eScience Center as a sparring partner for tackling these technical challenges.
In my current role I now have a nice combination of technical skills and advisory tasks. We advise and make other people at RIVM enthusiastic about our tools. Our recent experience in developing this R package has been invaluable.
The Netherlands eScience Center would like to thank Kirsten for her time for the interview . We look forward to continuing our collaboration. If you want to learn more about collaborating with the eScience Center or are interested in our training programme, please visit Training & Workshops — eScience Center. If you are interested in receiving consulting like Kirsten did, you may be interested in our Fellowship Programme.
From Pandemic Crisis to R-Package Implementation: Lessons from RIVM
Amidst the chaos of the pandemic, the Dutch National Institute for Public Health and the Environment (RIVM) began implementing R-packages to improve data processing and reporting. The process revealed the importance of collaboration, standardization, and continual learning for efficient research and data analysis.
Upgrade in Research Practices
Kirsten Bulsink, a data scientist at RIVM, detailed the transformative journey her team undertook when choosing to embrace R-packages in their work processes. From being heavily reliant on manual data processing for different infectious diseases, RIVM shifted towards standardizing and automating data transformation and reporting for infectious diseases amidst the trying times of the pandemic.
The Power of Collaborative Learning
The team sought assistance from the Netherlands eScience Center in learning how to pack and unpack scripts in R. The workshop enabled them to create packages that simplify data loading, cleaning, and reporting, and also aesthetic elements such as creating graphs in RIVM colors.
“The package improvements made it easier for others to use the package. Installation became smoother, and users no longer had to figure out why they needed to install extra packages.”
Investing in Hackathons for Package Development
As a further initiative, the RIVM team underwent hackathons aimed at improving the structure and usability of their R-packages. Goals were set to demonstrate the quality of their products, and to share their methodology with external parties even without sharing actual data.
Fruitful Outcomes and Future Directions
The hackathons resulted in improved package documentation, increased testing, and the division of the large package into smaller, more manageable ones. The team has also began working more like a software development team, implementing project management tools, and CI/CD pipelines for a smoother development process.
Plans are underway for public release of some RIVM packages using GitHub. Channels are also being established for continual internal knowledge sharing and running workshops about their tooling. Overall, the investment in R-package development marked a transformative step towards efficient, standardized data handling for RIVM.
The Key Takeaways and Actionable Advice
The willingness to innovate and embrace new tools such as R-packages can greatly improve the efficiency of data analysis. Thus, institutions should prioritize technological upskilling.
Collaborative learning, both internal and external, is essential for maximizing the benefits of innovative tools. Hosting workshops and hackathons can be an excellent way to foster such collaborative learning environments.
Sharing knowledge and tools with a broader community can further improve institutional standing and spur industry-wide advancement. The use of public platforms like GitHub can be a powerful vehicle for achieving this.
Embracing a software development mindset can help data science teams better manage their projects and improve their productivity.
To harness the power of R-packages and similar tools, institutions should foster learning and collaboration, embrace change, and share their knowledge with the wider community.
arXiv:2411.10523v1 Announce Type: new
Abstract: We analyze the semiclassical Schwarzschild geometry in the Boulware quantum state in the framework of two-dimensional (2D) dilaton gravity. The classical model is defined by the spherical reduction of Einstein’s gravity sourced with conformal scalar fields. The expectation value of the stress-energy tensor in the Boulware state is singular at the classical horizon of the Schwarzschild spacetime, but when backreaction effects are considered, previous results have shown that the 2D geometry is horizonless and described by a non-symmetric wormhole with a curvature singularity on the other side of the throat. In this work we show that reversing the sign of the central charge of the conformal matter removes the curvature singularity of the 2D backreacted geometry, which happens to be horizonless and asymptotically flat. This result is consistent with a similar analysis recently performed for the CGHS model. We also argue the physical significance of negative central charges in conformal anomalies from a four-dimensional perspective.
Future Roadmap: Challenges and Opportunities
Introduction
In this article, we examine the conclusions drawn from analyzing the semiclassical Schwarzschild geometry in the Boulware quantum state within the framework of two-dimensional (2D) dilaton gravity. The classical model is defined by the spherical reduction of Einstein’s gravity sourced with conformal scalar fields. The expectation value of the stress-energy tensor in the Boulware state is found to be singular at the classical horizon of the Schwarzschild spacetime. However, considering the backreaction effects, previous studies have shown that the 2D geometry becomes horizonless, transforming into a non-symmetric wormhole with a curvature singularity on the other side of the throat. This work presents a new insight into this phenomenon by demonstrating that reversing the sign of the central charge of the conformal matter eliminates the curvature singularity and results in a horizonless and asymptotically flat 2D backreacted geometry. This finding aligns with a similar analysis performed for the CGHS model.
Roadmap
Understanding the Boulware Quantum State
Readers should start by familiarizing themselves with the concept of the Boulware quantum state and its implications in the semiclassical Schwarzschild geometry. This state exhibits a singularity at the classical horizon, requiring further investigation to explore its behavior under backreaction effects.
Exploring Backreaction Effects
Next, readers should delve into the examination of backreaction effects on the 2D dilaton gravity model. Analyze the previous results that demonstrate the transformation of the horizon into a wormhole with a curvature singularity on the other side of the throat. This offers a unique perspective on the nature of the backreacted geometry.
Significance of Negative Central Charges
Consider the implications of reversing the sign of the central charge of the conformal matter. This key finding removes the curvature singularity and results in a horizonless and asymptotically flat 2D backreacted geometry. Relate this result to the recent analysis conducted for the CGHS model, which provides further support for the consistency and significance of negative central charges in conformal anomalies.
Physical Significance from a Four-Dimensional Perspective
Finally, readers should evaluate the physical significance of negative central charges in conformal anomalies from a four-dimensional perspective. Reflect on the implications and potential applications of this understanding beyond the 2D dilaton gravity framework.
Challenges and Opportunities
Challenges:
Further research is needed to explore the broader implications of the newfound understanding of the curvature singularity and horizonless nature of the 2D backreacted geometry.
Investigating the compatibility of these findings with other quantum gravity models and theories.
Addressing potential limitations and assumptions of the 2D dilaton gravity framework and exploring its validity in higher dimensions.
Opportunities:
Probing the connection between the reversal of the central charge sign and the elimination of curvature singularities, potentially leading to new insights into the nature of wormholes.
Exploring the implications of this research in other fields, such as black hole physics, quantum field theory, and quantum gravity.
Investigating the potential applications in areas like information theory, holography, and cosmology.
Conclusion
This roadmap provides readers with an outline of the future research directions and potential challenges and opportunities in understanding the semiclassical Schwarzschild geometry within the framework of 2D dilaton gravity. By considering the backreaction effects and reversing the sign of the central charge, researchers have discovered a horizonless and asymptotically flat geometry, removing the curvature singularity. Further investigation is required to fully comprehend the physical significance of these findings and their applicability in other quantum gravity models and theories.
arXiv:2411.10060v1 Announce Type: new
Abstract: Multimodal emotion recognition in conversation (MER) aims to accurately identify emotions in conversational utterances by integrating multimodal information. Previous methods usually treat multimodal information as equal quality and employ symmetric architectures to conduct multimodal fusion. However, in reality, the quality of different modalities usually varies considerably, and utilizing a symmetric architecture is difficult to accurately recognize conversational emotions when dealing with uneven modal information. Furthermore, fusing multi-modality information in a single granularity may fail to adequately integrate modal information, exacerbating the inaccuracy in emotion recognition. In this paper, we propose a novel Cross-Modality Augmented Transformer with Hierarchical Variational Distillation, called CMATH, which consists of two major components, i.e., Multimodal Interaction Fusion and Hierarchical Variational Distillation. The former is comprised of two submodules, including Modality Reconstruction and Cross-Modality Augmented Transformer (CMA-Transformer), where Modality Reconstruction focuses on obtaining high-quality compressed representation of each modality, and CMA-Transformer adopts an asymmetric fusion strategy which treats one modality as the central modality and takes others as auxiliary modalities. The latter first designs a variational fusion network to fuse the fine-grained representations learned by CMA- Transformer into a coarse-grained representations. Then, it introduces a hierarchical distillation framework to maintain the consistency between modality representations with different granularities. Experiments on the IEMOCAP and MELD datasets demonstrate that our proposed model outperforms previous state-of-the-art baselines. Implementation codes can be available at https://github.com/ cjw-MER/CMATH.
Analysis of the Content
In this article, the authors discuss the challenges and limitations of previous methods in multimodal emotion recognition in conversation (MER) and propose a novel approach called Cross-Modality Augmented Transformer with Hierarchical Variational Distillation (CMATH). The authors highlight the importance of considering the varying quality of different modalities and the need for an asymmetric fusion strategy to accurately recognize conversational emotions.
The concept of multimodal emotion recognition is highly relevant to the field of multimedia information systems. Multimodal information, which includes textual, visual, and auditory cues, is widely used in various multimedia applications such as video summarization, emotion detection in videos, and human-computer interaction. By accurately identifying emotions in conversational utterances, multimedia information systems can provide more personalized and interactive experiences.
CMATH addresses the limitations of previous methods by introducing two major components: Multimodal Interaction Fusion and Hierarchical Variational Distillation. The Modality Reconstruction submodule focuses on obtaining high-quality compressed representations of each modality, taking into account the varying quality of different modalities. The Cross-Modality Augmented Transformer (CMA-Transformer) submodule adopts an asymmetric fusion strategy, treating one modality as the central modality and others as auxiliary modalities. This approach allows for more accurate emotion recognition by leveraging the strengths of each modality.
The Hierarchical Variational Distillation component of CMATH further improves the fusion of multimodal information by designing a variational fusion network and a hierarchical distillation framework. The variational fusion network combines the fine-grained representations learned by the CMA-Transformer into a coarse-grained representation. This intermediate representation helps maintain consistency between different modalities with different granularities, ensuring a more accurate recognition of conversational emotions.
Expert Insights
The proposed CMATH model demonstrates the multi-disciplinary nature of the concepts discussed in the article. It combines techniques from natural language processing, computer vision, and machine learning to address the challenges in multimodal emotion recognition. This interdisciplinary approach is crucial for developing effective models that can accurately interpret and understand human emotions in conversational contexts.
Furthermore, the concept of CMATH aligns with the broader field of augmented reality, virtual reality, and artificial reality. Emotion recognition plays a crucial role in creating immersive and realistic virtual environments, where the system can respond appropriately to the user’s emotions and enhance the user’s overall experience. By accurately integrating multimodal information, such as facial expressions, speech intonations, and textual cues, CMATH can contribute to the advancement of emotion-aware virtual and augmented reality systems.
In conclusion, the authors’ proposed CMATH model addresses the challenges and limitations of previous methods in multimodal emotion recognition in conversation. The asymmetric fusion strategy and hierarchical variational distillation framework offer a robust solution for accurately recognizing conversational emotions. This research contributes to the wider field of multimedia information systems and has implications for augmented and virtual realities by enabling more immersive and emotionally responsive environments.
Potential Future Trends in Space Radiation Measurement
In a groundbreaking mission, two female figures named Helga and Zohar have successfully completed a trip around the moon to measure the levels of space radiation outside the low Earth orbit. This landmark achievement opens up exciting possibilities and potential future trends in the field of space radiation measurement.
Advancements in Radiation Detection Technology
One of the key areas that will undoubtedly see significant advancements is radiation detection technology. With the success of Helga and Zohar’s mission, scientists will likely focus on developing more advanced and compact radiation detectors specifically designed for space exploration. These detectors may feature improved sensitivity and accuracy, allowing for better measurements of space radiation levels. This trend is crucial for ensuring the safety of future manned missions, as astronauts can be exposed to potentially harmful levels of radiation in deep space.
Increased Space Radiation Research
Helga and Zohar’s mission has shed light on the importance of further research into space radiation. Scientists will likely invest more resources into studying the effects of space radiation on both human health and spacecraft systems. This increased focus on research will lead to a better understanding of the long-term effects of space radiation, helping to develop effective shielding strategies and countermeasures.
Development of Radiation Shielding Technologies
As space exploration continues to expand, the need for effective radiation shielding technologies becomes paramount. The data collected by Helga and Zohar will pave the way for the development of innovative shielding materials and strategies that can minimize the risks of space radiation exposure. These developments may include lightweight and reliable shielding materials, advanced electromagnetic shielding techniques, and innovative spacecraft designs that prioritize radiation protection.
Integration of Artificial Intelligence
Artificial Intelligence (AI) is transforming various industries, and space radiation measurement is no exception. AI algorithms can aid in analyzing large datasets collected during space missions, helping scientists extract valuable insights and trends. In the future, AI can potentially be used to predict space radiation levels based on various factors and models. With AI integration, space agencies and researchers can make more informed decisions regarding mission planning and astronaut safety.
Predictions for the Future
Based on the above trends, it is possible to make some predictions regarding the future of space radiation measurement:
Space radiation detectors will become smaller, more portable, and highly accurate.
Extensive research will be conducted on the long-term effects of space radiation.
Innovative radiation shielding technologies will be developed and implemented in spacecraft.
Artificial Intelligence will play a crucial role in analyzing and predicting space radiation levels.
Recommendations for the Industry
Considering the potential future trends in space radiation measurement, it is important for the industry to prioritize the following recommendations:
Invest in research and development to advance radiation detection technology specifically designed for space exploration.
Allocate resources for extensive research on the effects of space radiation on human health and spacecraft systems.
Foster collaboration between scientists, engineers, and material experts to accelerate the development of innovative radiation shielding technologies.
Integrate AI capabilities into space radiation measurement systems for efficient data analysis and prediction.
Establish international standards and guidelines for space radiation safety to ensure consistency and best practices among space agencies.
With the success of the Helga and Zohar mission, the future of space radiation measurement looks promising. By investing in advanced technology, extensive research, and innovative shielding strategies, the industry can ensure the safety of future manned missions and pave the way for further space exploration.
References:
Nature. Published online: 15 November 2024; doi:10.1038/d41586-024-03744-0
arXiv:2411.08148v1 Announce Type: new
Abstract: Pioneering advancements in artificial intelligence, especially in genAI, have enabled significant possibilities for content creation, but also led to widespread misinformation and false content. The growing sophistication and realism of deepfakes is raising concerns about privacy invasion, identity theft, and has societal, business impacts, including reputational damage and financial loss. Many deepfake detectors have been developed to tackle this problem. Nevertheless, as for every AI model, the deepfake detectors face the wrath of lack of considerable generalization to unseen scenarios and cross-domain deepfakes. Besides, adversarial robustness is another critical challenge, as detectors drastically underperform to the slightest imperceptible change. Most state-of-the-art detectors are trained on static datasets and lack the ability to adapt to emerging deepfake attack trends. These three crucial challenges though hold paramount importance for reliability in practise, particularly in the deepfake domain, are also the problems with any other AI application. This paper proposes an adversarial meta-learning algorithm using task-specific adaptive sample synthesis and consistency regularization, in a refinement phase. By focussing on the classifier’s strengths and weaknesses, it boosts both robustness and generalization of the model. Additionally, the paper introduces a hierarchical multi-agent retrieval-augmented generation workflow with a sample synthesis module to dynamically adapt the model to new data trends by generating custom deepfake samples. The paper further presents a framework integrating the meta-learning algorithm with the hierarchical multi-agent workflow, offering a holistic solution for enhancing generalization, robustness, and adaptability. Experimental results demonstrate the model’s consistent performance across various datasets, outperforming the models in comparison.
Expert Commentary: Advancements in deepfake detection and the need for generalization and robustness
Artificial intelligence has made significant advancements in the field of deepfake detection, but it has also brought about new challenges. This paper highlights three crucial challenges faced by deepfake detectors – lack of generalization to unseen scenarios and cross-domain deepfakes, adversarial robustness, and the inability to adapt to emerging attack trends. These challenges are not unique to the deepfake domain but exist in other AI applications as well.
The lack of generalization to unseen scenarios and cross-domain deepfakes is a significant concern. AI models trained on specific datasets often struggle to perform well on real-world scenarios that they have not encountered during training. This is because deepfakes are continually evolving and becoming more sophisticated, making it challenging for detectors to keep up. The proposed adversarial meta-learning algorithm addresses this issue by focusing on the strengths and weaknesses of the classifier and refining it to improve both robustness and generalization.
Adversarial robustness is another critical challenge. Deepfake detectors often fail to detect slight imperceptible changes in deepfakes, which can be exploited by attackers. Adversarial attacks aim to deceive detectors by introducing subtle modifications to the deepfake. The proposed algorithm tackles this challenge by incorporating consistency regularization, which helps the detector react consistently to adversarial changes, making it more robust.
Furthermore, the paper introduces a hierarchical multi-agent retrieval-augmented generation workflow. This workflow, combined with a sample synthesis module, allows the model to dynamically adapt to new data trends by generating custom deepfake samples. This addresses the challenge of adapting to emerging attack trends and ensures that the model stays up-to-date with the latest deepfake techniques.
The integration of the meta-learning algorithm with the hierarchical multi-agent workflow offers a holistic solution for enhancing generalization, robustness, and adaptability. By combining these techniques, the proposed framework demonstrates consistent performance across various datasets, surpassing other models in comparison.
This research highlights the multi-disciplinary nature of deepfake detection. It involves advancements in artificial intelligence, specifically genAI, and draws upon concepts from computer vision, machine learning, and adversarial attacks. The proposed framework provides valuable insights and solutions not only for the deepfake domain but also for other AI applications facing similar challenges.
In conclusion, while deepfake detection has come a long way, there is still much work to be done to improve generalization, robustness, and adaptability. The proposed framework presented in this paper offers a promising approach to tackle these challenges and lays the foundation for further advancements in deepfake detection and other AI applications.
