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Analysis

This paper investigates the dynamic pathways of a geometric phase transition in an active matter system. It focuses on the transition between different cluster morphologies (slab and droplet) in a 2D active lattice gas undergoing motility-induced phase separation. The study uses forward flux sampling to generate transition trajectories and reveals that the transition pathways are dependent on the Peclet number, highlighting the role of non-equilibrium fluctuations. The findings are relevant for understanding active matter systems more broadly.
Reference

The droplet-to-slab transition always follows a similar mechanism to its equilibrium counterpart, but the reverse (slab-to-droplet) transition depends on rare non-equilibrium fluctuations.

Analysis

This paper is significant because it uses genetic programming, an AI technique, to automatically discover new numerical methods for solving neutron transport problems. Traditional methods often struggle with the complexity of these problems. The paper's success in finding a superior accelerator, outperforming classical techniques, highlights the potential of AI in computational physics and numerical analysis. It also pays homage to a prominent researcher in the field.
Reference

The discovered accelerator, featuring second differences and cross-product terms, achieved over 75 percent success rate in improving convergence compared to raw sequences.

Analysis

This paper provides valuable implementation details and theoretical foundations for OpenPBR, a standardized physically based rendering (PBR) shader. It's crucial for developers and artists seeking interoperability in material authoring and rendering across various visual effects (VFX), animation, and design visualization workflows. The focus on physical accuracy and standardization is a key contribution.
Reference

The paper offers 'deeper insight into the model's development and more detailed implementation guidance, including code examples and mathematical derivations.'

Research#llm📝 BlogAnalyzed: Dec 24, 2025 13:35

LLM-Powered Horse Racing Prediction

Published:Dec 24, 2025 01:21
1 min read
Zenn LLM

Analysis

This article discusses using LLMs for horse racing prediction. It mentions structuring data like odds, AI predictions, and qualitative data in Markdown format for LLM input. The data is sourced from the internet and pre-processed. The article also references a research lab (Nislab) and an Advent calendar, suggesting a research or project context. The brief excerpt focuses on data preparation and input methods for the LLM, hinting at a practical application of AI in sports analysis. Further details about the prompt are mentioned but truncated.
Reference

"Horse racing is a microcosm of life."

Research#Sports Analytics📝 BlogAnalyzed: Dec 29, 2025 01:43

Method for Extracting "One Strike" from Continuous Acceleration Data

Published:Dec 22, 2025 22:00
1 min read
Zenn DL

Analysis

This article from Nislab discusses the crucial preprocessing step of isolating individual strikes from continuous motion data, specifically focusing on boxing and mass boxing applications using machine learning. The challenge lies in accurately identifying and extracting a single strike from a stream of data, including continuous actions and periods of inactivity. The article uses 3-axis acceleration data from smartwatches as its primary data source. The core of the article will likely detail the definition of a "single strike" and the methodology employed to extract it from the time-series data, with experimental results to follow. The context suggests a focus on practical application within the field of sports analytics and machine learning.
Reference

The most important and difficult preprocessing step when handling striking actions in boxing and mass boxing with machine learning is accurately extracting only one strike from continuous motion data.

Research#llm📝 BlogAnalyzed: Dec 24, 2025 18:20

Which LLM Should I Use? Asking LLMs Themselves

Published:Dec 13, 2025 15:00
1 min read
Zenn GPT

Analysis

This article explores the question of which Large Language Model (LLM) is best suited for specific tasks by directly querying various LLMs like GPT and Gemini. It's a practical approach for engineers who frequently use LLMs and face the challenge of selecting the right tool. The article promises to present the findings of this investigation, offering potentially valuable insights into the strengths and weaknesses of different LLMs for different applications. The inclusion of links to the author's research lab and an advent calendar suggests a connection to ongoing research and a broader context of AI exploration.

Key Takeaways

Reference

「こういうことしたいんだけど、どのLLM使ったらいいんだろう...」

Finance#AI in Finance📝 BlogAnalyzed: Dec 29, 2025 08:42

(5/5) AlphaVertex - Creating a Worldwide Financial Knowledge Graph - TWiML Talk #18

Published:Apr 7, 2017 18:30
1 min read
Practical AI

Analysis

This article is a brief announcement of an interview with AlphaVertex, a FinTech startup. The interview focuses on AlphaVertex's work in creating a global financial knowledge graph to aid investors in predicting stock prices. The article mentions the location of the interview (NYU/ffVC AI NexusLab) and the sponsoring organizations (Future Labs at NYU Tandon and ffVenture Capital). It also provides a link to the series notes. The article is concise and informative, providing a quick overview of the topic and the company's focus.
Reference

This week I'm on location at NYU/ffVC AI NexusLab startup accelerator, speaking with founders from the 5 companies in the program's inaugural batch.

Analysis

The article highlights Behold.ai, a startup leveraging computer vision and natural language processing (NLP) to streamline healthcare insurance billing. The interview took place at the NYU/ffVC AI NexusLab startup accelerator, indicating a focus on early-stage AI ventures. The article's brevity suggests it's an introduction or announcement, likely part of a series. The mention of sponsors (Future Labs at NYU Tandon and ffVenture Capital) points to the financial backing of the program and the startups involved. The focus is on efficiency gains in a specific industry, showcasing a practical application of AI.
Reference

This week I'm on location at NYU/ffVC AI NexusLab startup accelerator, speaking with founders from the 5 companies in the program's inaugural batch.

Technology#Robotics📝 BlogAnalyzed: Dec 29, 2025 08:42

Cambrian Intelligence: Simplifying Robot Programming with AI

Published:Apr 7, 2017 18:14
1 min read
Practical AI

Analysis

This article highlights Cambrian Intelligence, a company leveraging AI to streamline the programming of industrial robots, specifically within the automotive sector. The interview took place at the NYU/ffVC AI NexusLab startup accelerator, indicating a focus on early-stage AI ventures. The article's brevity suggests it's a promotional piece or a brief overview of the company's activities. The mention of the 'TWiML Talk' podcast and the sponsors (Future Labs at NYU Tandon and ffVenture Capital) provides context and indicates the article's origin within a broader series of interviews. The focus is on the application of AI to solve a practical problem in manufacturing.
Reference

This week I'm on location at NYU/ffVC AI NexusLab startup accelerator, speaking with founders from the 5 companies in the program's inaugural batch.

Business#Marketing AI📝 BlogAnalyzed: Dec 29, 2025 08:42

(2/5) Klustera - Location-Based Intelligence for Smarter Marketing - TWiML Talk #18

Published:Apr 7, 2017 18:14
1 min read
Practical AI

Analysis

This article provides a brief overview of Klustera, a company utilizing location-based intelligence and machine learning for marketing campaigns. It's part of a series of interviews with startups from the NYU/ffVC AI NexusLab accelerator. The article highlights Klustera's focus on helping brands improve their marketing strategies through data analysis. The context suggests a focus on practical applications of AI in a business setting, specifically within the marketing domain. The article is concise and serves as an introduction to the company and its work.
Reference

This interview is with Klustera, a company applying location-based intelligence and machine learning to help brands execute smarter marketing campaigns.

Research#llm📝 BlogAnalyzed: Dec 29, 2025 08:42

HelloVera - AI-Powered Customer Support - TWiML Talk #18

Published:Apr 7, 2017 18:14
1 min read
Practical AI

Analysis

This article introduces HelloVera, an AI-powered customer support solution, as discussed in a TWiML Talk episode. The interview took place at the NYU/ffVC AI NexusLab startup accelerator, highlighting the company's participation in the inaugural batch. The focus is on how HelloVera leverages artificial intelligence to automate and improve customer support interactions. The article also acknowledges the sponsors, Future Labs at NYU Tandon and ffVenture Capital, for supporting the series. The provided link offers further details.

Key Takeaways

Reference

This week I'm on location at NYU/ffVC AI NexusLab startup accelerator, speaking with founders from the 5 companies in the program's inaugural batch.