by jsendak | May 14, 2025 | DS Articles
This guide introduces data streaming from a data science perspective. We’ll explain what it is, why it matters, and how to use tools like Apache Kafka, Apache Flink, and PyFlink to build real-time pipelines.
The Importance of Data Streaming in Data Science
Data streaming is rapidly becoming a significant aspect of data science, transforming the way data is handled in real-time. This article explores its significance and potential future developments through the use of tools like Apache Kafka, Apache Flink, and PyFlink.
Understanding Data Streaming
Data streaming is essentially a mechanism wherein data is continuously processed as and when it arrives, rather than in batches. It allows for immediate insights into data that can be acted upon instantaneously. This is particularly useful in domains like financial services, healthcare, and logistics, where real-time data analysis could mean improved operational efficiency, patient health outcomes, and timely deliveries, respectively.
Long-term Implications and Future Developments in Data Streaming
As data volumes continue to grow exponentially, the importance of being able to process and analyze data in real-time without latency cannot be overstated. It signifies the shift towards more responsive, agile, and informed decision-making processes, thereby leading to insightful interactions, lower opportunity costs, and increased operational efficiencies.
The role of Apache Kafka, Apache Flink, and PyFlink
Apache Kafka, Apache Flink, and PyFlink are powerful tools that assist in real-time data streaming. They are built to handle massive volumes of data and perform complex processing tasks efficiently. Integration of these tools is likely to streamline data handling processes, contributing to the future advancements in data streaming.
Actionable Advice
- Invest in Learning: It is crucial to stay updated with real-time data streaming concepts and tools like Apache Kafka, Apache Flink, and PyFlink. In-depth understanding and practical skills in these areas are key to leveraging the advantages of real-time data processing.
- Infrastructure Upgrade: To effectively manage data streaming, adapt your data handling infrastructure accordingly. This includes setting up a reliable and scalable system that can handle higher volumes of data and offer faster processing times.
- Practical Implementation: Converting theoretical knowledge into practical solutions is key. Start with small projects that utilize data streaming to gain a hands-on understanding. Later, these can be scaled up to more complex projects.
In conclusion, the potential of data streaming is vast, and it’s high time businesses leverage these opportunities for real-time insights and more informed decision-making. With robust tools like Apache Kafka, Apache Flink, and PyFlink, this transformation can be conveniently achieved.
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by jsendak | May 14, 2025 | AI
arXiv:2505.07830v1 Announce Type: new
Abstract: A total of more than 3400 public shootings have occurred in the United States between 2016 and 2022. Among these, 25.1% of them took place in an educational institution, 29.4% at the workplace including office buildings, 19.6% in retail store locations, and 13.4% in restaurants and bars. During these critical scenarios, making the right decisions while evacuating can make the difference between life and death. However, emergency evacuation is intensely stressful, which along with the lack of verifiable real-time information may lead to fatal incorrect decisions. To tackle this problem, we developed a multi-route routing optimization algorithm that determines multiple optimal safe routes for each evacuee while accounting for available capacity along the route, thus reducing the threat of crowding and bottlenecking. Overall, our algorithm reduces the total casualties by 34.16% and 53.3%, compared to our previous routing algorithm without capacity constraints and an expert-advised routing strategy respectively. Further, our approach to reduce crowding resulted in an approximate 50% reduction in occupancy in key bottlenecking nodes compared to both of the other evacuation algorithms.
Expert Commentary: Multi-Disciplinary Approach to Emergency Evacuation
In the face of increasing public shootings in the United States, it is essential to develop effective strategies for emergency evacuation in high-risk locations such as educational institutions, workplaces, retail stores, and restaurants. The study mentioned in this article presents a novel multi-route routing optimization algorithm that not only determines multiple optimal safe routes for evacuees but also takes into account the available capacity along these routes, thus reducing the risk of overcrowding and bottlenecks.
This algorithm represents a significant advancement in the field of emergency management by combining principles from various disciplines such as computer science, operations research, and safety engineering. By integrating real-time information and capacity constraints into the decision-making process, the algorithm is able to provide tailored evacuation routes for each individual, ultimately leading to a substantial reduction in total casualties.
One of the key strengths of this approach is its ability to adapt to the dynamic nature of emergency situations, where unforeseen changes in the environment can impact the effectiveness of evacuation plans. By continuously optimizing routes based on updated information, the algorithm is able to respond in real-time to evolving threats and ensure the safety of evacuees.
Furthermore, the study highlights the importance of considering human behavior and psychology in the design of evacuation strategies. By acknowledging the intense stress and uncertainty that individuals experience during emergencies, the algorithm aims to alleviate some of this burden by providing clear and efficient routes for evacuation.
Looking ahead, the multi-disciplinary nature of this research opens up new possibilities for improving emergency response systems in various settings. By harnessing the power of technology, data analytics, and human-centric design principles, we can continue to enhance the safety and security of our communities in the face of escalating threats.
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by jsendak | May 13, 2025 | DS Articles
[This article was first published on
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Join our workshop on Using LLMs with ellmer, which is a part of our workshops for Ukraine series!
Here’s some more info:
Title: Using LLMs with ellmer
Date: Friday, June 13th, 18:00 – 20:00 CEST (Rome, Berlin, Paris timezone)
Speaker: Hadley Wickham is Chief Scientist at Posit PBC, winner of the 2019 COPSS award, and a member of the R Foundation. He builds tools (both computational and cognitive) to make data science easier, faster, and more fun. His work includes packages for data science (like the tidyverse, which includes ggplot2, dplyr, and tidyr)and principled software development (e.g. roxygen2, testthat, and pkgdown). He is also a writer, educator, and speaker promoting the use of R for data science. Learn more on his website, <http://hadley.nz>.
Description: Join us for an engaging, hands-on hackathon workshop where you’ll learn to use large language models (LLMs) from R with the ellmer (https://ellmer.tidyverse.org) package. In this 2-hour session, we’ll combine theory with practical exercises to help you create AI-driven solutions—no extensive preparation needed!
## What you’ll learn:
– A quick intro to LLMs: what they’re good at and where they struggle
– How to use ellmer with different model providers (OpenAI, Anthropic, Google Gemini, and others)
– Effective prompt design strategies and practical applications for your work
– Function calling: how to let LLMs use R functions for tasks they can’t handle well
– Extracting structured data from text, images, and video using LLMs
## What you’ll need:
– A laptop with R installed
– The development version of ellmer (`pak::pak(“tidyverse/ellmer”))`
– An account with either Claude (cheap) or Google Gemini (free).
Follow the instructions at <github.com/hadley/workshop-llm-hackathon> to get setup.
Minimal registration fee: 20 euro (or 20 USD or 800 UAH)
Please note that the registration confirmation is sent 1 day before the workshop to all registered participants rather than immediately after registration
How can I register?
- 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.
How can I sponsor a student?
- 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 list here. (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!
Using LLMs with ellmer workshop by Hadley Wickham was first posted on May 13, 2025 at 3:06 pm.
Continue reading: Using LLMs with ellmer workshop by Hadley Wickham
Analysis: The Future of LLMs with ellmer Workshops
In the ever-evolving field of data science, continuous learning and keeping up-to-date with the latest technologies and methodologies are of utmost importance. A recent announcement on R-bloggers.com discussed a fast-approaching online workshop on ‘Using LLMs with ellmer’ which undoubtedly caught the attention of many data science enthusiasts.
Implications and Future Developments
Large Language Models (LLMs), as introduced in this workshop, are a critical component in the realm of AI, capable of understanding and generating human-like text. Notably, the ellmer package enables these advanced AI capabilities to be integrated into the R environment. Ensuring that data scientists are adept in such tools has long-term implications for the speed, efficiency, and novel applications in data science.
Hadley Wickham, the speaker for this session, is a distinguished data scientist and prolific contributor to R packages, making the promise of future workshops held by him or speakers of a similar calibre, highly beneficial for learners. It’s quite plausible that the increased demand for these workshops could lead them to become a regular occurrence, facilitating upskilling in the R community.
In the future, we might see an expansion of topics, covering more R packages and advanced AI techniques. Furthermore, the flexible approach today’s workshop adopted towards payment (acceptable in different currencies and also by sponsoring a student) combined with its charitable cause, paints an encouraging picture of an inclusive learning community that values diversity and social responsibility. This could lead to increased accessibility in the future, as more and more professionals and students benefit from these affordable (or sponsored) learning opportunities.
Actionable Advice
- Stay Informed: Regularly check R-bloggers and similar resources for updates about forthcoming workshops and apply promptly. Remember that registration confirmations are sent out a day before the workshop.
- Prepare Adequately: Ensuring that the necessary prerequisites are met before the workshop (such as having R installed and setting up the ellmer package) allows for a more effective learning experience.
- Be Charitable: If able, consider sponsoring a student. This not only supports the learning of individuals unable to afford the fee, but additionally contributes towards addressing social implications in areas such as Ukraine.
- Take Part: Even if one is not an R user, such workshops, often held by industry experts, offer valuable insights which could be applied to data science work in general.
By utilizing such actionable advice, not only can individuals further their personal knowledge and skills, but the broader R, data science, and AI communities can continue to grow and evolve positively.
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by jsendak | May 13, 2025 | Computer Science
arXiv:2505.06685v1 Announce Type: new
Abstract: Emotion understanding in videos aims to accurately recognize and interpret individuals’ emotional states by integrating contextual, visual, textual, and auditory cues. While Large Multimodal Models (LMMs) have demonstrated significant progress in general vision-language (VL) tasks, their performance in emotion-specific scenarios remains limited. Moreover, fine-tuning LMMs on emotion-related tasks often leads to catastrophic forgetting, hindering their ability to generalize across diverse tasks. To address these challenges, we present Emotion-Qwen, a tailored multimodal framework designed to enhance both emotion understanding and general VL reasoning. Emotion-Qwen incorporates a sophisticated Hybrid Compressor based on the Mixture of Experts (MoE) paradigm, which dynamically routes inputs to balance emotion-specific and general-purpose processing. The model is pre-trained in a three-stage pipeline on large-scale general and emotional image datasets to support robust multimodal representations. Furthermore, we construct the Video Emotion Reasoning (VER) dataset, comprising more than 40K bilingual video clips with fine-grained descriptive annotations, to further enrich Emotion-Qwen’s emotional reasoning capability. Experimental results demonstrate that Emotion-Qwen achieves state-of-the-art performance on multiple emotion recognition benchmarks, while maintaining competitive results on general VL tasks. Code and models are available at https://anonymous.4open.science/r/Emotion-Qwen-Anonymous.
Expert Commentary:
Emotion understanding in videos is a complex task that requires the integration of various cues such as visual, textual, and auditory information. The development of Large Multimodal Models (LMMs) has shown promise in general vision-language tasks, but their performance in emotion-specific scenarios has been limited. The Emotion-Qwen framework presented in this article aims to address these challenges by incorporating a Hybrid Compressor based on the Mixture of Experts (MoE) paradigm.
The use of a MoE paradigm allows Emotion-Qwen to dynamically route inputs, balancing emotion-specific processing with general-purpose reasoning. This approach helps prevent catastrophic forgetting when fine-tuning LMMs on emotion-related tasks, enabling the model to generalize across diverse tasks. Additionally, the pre-training of Emotion-Qwen on large-scale general and emotional image datasets helps improve its multimodal representations.
One notable contribution of this work is the construction of the Video Emotion Reasoning (VER) dataset, which contains a large number of bilingual video clips with fine-grained descriptive annotations. This dataset enriches Emotion-Qwen’s emotional reasoning capabilities and enables the model to achieve state-of-the-art performance on multiple emotion recognition benchmarks.
From a multidisciplinary perspective, the Emotion-Qwen framework integrates concepts from computer vision, natural language processing, and machine learning to enable robust emotion understanding in videos. The model’s success in both emotion-specific and general VL tasks showcases the potential of multimodal approaches in the field of multimedia information systems.
Overall, the Emotion-Qwen framework represents a significant advancement in the field of emotion understanding in videos and demonstrates the importance of multi-disciplinary approaches in developing sophisticated AI models for complex tasks.
For more information and access to the code and models for Emotion-Qwen, visit the project’s page at https://anonymous.4open.science/r/Emotion-Qwen-Anonymous.
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by jsendak | May 13, 2025 | GR & QC Articles
arXiv:2505.06382v1 Announce Type: new
Abstract: We consider corrections to the Schwarzschild black hole metric arising from exotic long-range forces within quantum field theory frameworks. Specifically, we analyze two models: the Feinberg-Sucher potential for massless neutrinos and Ferrer-Nowakowski potentials for boson-mediated interactions at finite temperatures, yielding metric corrections with $r^{-5}$ and $r^{-3}$ dependencies. Using analytic expansions around the Schwarzschild photon sphere, we find that attractive potential corrections enhance gravitational lensing, enlarging the photon sphere and shadow radius, while repulsive potential corrections induce gravitational screening, reducing these observables. Our results clearly illustrate how different quantum-derived corrections can produce measurable deviations from standard Schwarzschild predictions, providing robust theoretical benchmarks for future astrophysical observations.
Conclusions
The study of corrections to the Schwarzschild black hole metric from exotic long-range forces within quantum field theory frameworks has revealed significant deviations from standard predictions. Analyzing models such as the Feinberg-Sucher and Ferrer-Nowakowski potentials has shown that attractive potential corrections enhance gravitational lensing effects, while repulsive potential corrections induce gravitational screening.
These results highlight the importance of considering quantum-derived corrections in understanding the behavior of black holes and the effects they have on observable phenomena such as the photon sphere and shadow radius. By providing robust theoretical benchmarks, this research paves the way for future astrophysical observations to test and further refine our understanding of black hole dynamics.
Future Roadmap
- Continue to refine models and simulations that incorporate quantum-derived corrections to the Schwarzschild black hole metric.
- Conduct observational studies to test the predictions of these corrections and compare them to standard Schwarzschild predictions.
- Explore the implications of these corrections for other astrophysical phenomena, such as gravitational wave detection and black hole mergers.
- Collaborate with experimentalists and observational astronomers to develop new methods for detecting and measuring the effects of quantum-derived corrections on black hole dynamics.
Potential Challenges
- Obtaining high-quality observational data to accurately test the predictions of quantum-derived corrections.
- Developing sophisticated modeling techniques to account for the complex interplay of exotic long-range forces in black hole environments.
- Securing funding and resources for large-scale observational campaigns and computational simulations.
Opportunities on the Horizon
- Advancing our understanding of the fundamental nature of black holes and their interactions with quantum fields.
- Opening up new avenues for exploring the boundary between classical and quantum physics in extreme gravitational environments.
- Contributing to the development of more accurate and comprehensive models for describing black hole dynamics in the universe.
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