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Pattern-Based File and Data Access with Python Glob: A Comprehensive Guide for Computational Research
Sidney Shapiro
https://arxiv.org/abs/2509.08843 https://

Pattern-Based File and Data Access with Python Glob: A Comprehensive Guide for Computational Research
Pattern-based file access is a fundamental but often under-documented aspect of computational research. The Python glob module provides a simple yet powerful way to search, filter, and ingest files using wildcard patterns, enabling scalable workflows across disciplines. This paper introduces glob as a versatile tool for data science, business analytics, and artificial intelligence applications. We demonstrate use cases including large-scale data ingestion, organizational data analysis, AI datas…

Where are GIScience Faculty Hired from? Analyzing Faculty Mobility and Research Themes Through Hiring Networks
Yanbing Chen, Jonathan Nelson, Bing Zhou, Ryan Zhenqi Zhou, Shan Ye, Haokun Liu, Zhining Gu, Armita Kar, Hoeyun Kwon, Pengyu Chen, Maoran Sun, Yuhao Kang
https://arxiv.org/abs/2508.09043

Where are GIScience Faculty Hired from? Analyzing Faculty Mobility and Research Themes Through Hiring Networks
Academia is profoundly influenced by faculty hiring networks, which serve as critical conduits for knowledge dissemination and the formation of collaborative research initiatives. While extensive research in various disciplines has revealed the institutional hierarchies inherent in these networks, their impacts within GIScience remain underexplored. To fill this gap, this study analyzes the placement patterns of 946 GIScience faculty worldwide by mapping the connections between PhD-granting ins…

DiffractGPT: Atomic Structure Determination from X-ray Diffraction Patterns using Generative Pre-trained Transformer
Kamal Choudhary
https://arxiv.org/abs/2508.08349 https://

DiffractGPT: Atomic Structure Determination from X-ray Diffraction Patterns using Generative Pre-trained Transformer
Crystal structure determination from powder diffraction patterns is a complex challenge in materials science, often requiring extensive expertise and computational resources. This study introduces DiffractGPT, a generative pre-trained transformer model designed to predict atomic structures directly from X-ray diffraction (XRD) patterns. By capturing the intricate relationships between diffraction patterns and crystal structures, DiffractGPT enables fast and accurate inverse design. Trained on t…

ForTIFAI: Fending Off Recursive Training Induced Failure for AI Models
Soheil Zibakhsh Shabgahi, Pedram Aghazadeh, Azalia Mirhosseini, Farinaz Koushanfar
https://arxiv.org/abs/2509.08972

ForTIFAI: Fending Off Recursive Training Induced Failure for AI Models
The increasing reliance on generative AI models has accelerated the generation rate of synthetic data, with some projections suggesting that most available new data for training could be machine-generated by 2030. This shift to a mainly synthetic content presents a critical challenge: repeated training in synthetic data leads to a phenomenon known as model collapse, where model performance degrades over generations of training, eventually rendering the models ineffective. Although prior studies…

Low Resource Reconstruction Attacks Through Benign Prompts
Sol Yarkoni, Roi Livni
https://arxiv.org/abs/2507.07947 https://arxiv.org/pdf/2507.07947 https://arxiv.org/html/2507.07947
arXiv:2507.07947v1 Announce Type: new
Abstract: The recent advances in generative models such as diffusion models have raised several risks and concerns related to privacy, copyright infringements and data stewardship. To better understand and control the risks, various researchers have created techniques, experiments and attacks that reconstruct images, or part of images, from the training set. While these techniques already establish that data from the training set can be reconstructed, they often rely on high-resources, excess to the training set as well as well-engineered and designed prompts.
In this work, we devise a new attack that requires low resources, assumes little to no access to the actual training set, and identifies, seemingly, benign prompts that lead to potentially-risky image reconstruction. This highlights the risk that images might even be reconstructed by an uninformed user and unintentionally. For example, we identified that, with regard to one existing model, the prompt ``blue Unisex T-Shirt'' can generate the face of a real-life human model. Our method builds on an intuition from previous works which leverages domain knowledge and identifies a fundamental vulnerability that stems from the use of scraped data from e-commerce platforms, where templated layouts and images are tied to pattern-like prompts.
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TrEnv: Transparently Share Serverless Execution Environments Across Different Functions and Nodes
Jialiang Huang, Teng Ma, Zheng Liu, Sixing Lin, Kang Chen, Jinlei Jiang, Xia Liao, Yingdi Shan, Yongwei Wu, Ning Zhang, Mengting Lu, Tao Ma, Haifeng Gong, Mingxing Zhang
https://arxiv.org/abs/2509.09525

TrEnv: Transparently Share Serverless Execution Environments Across Different Functions and Nodes
Serverless computing provides dynamic scalability, but its infrastructure overhead becomes a bottleneck for emerging workloads such as LLM agents, which exhibit unpredictable invocation patterns and variable resource demands. Our analysis shows that for these agents, the cost of running on serverless platforms can reach up to 70% of the cost of LLM API calls. This finding motivates the need for a more efficient, high-density serverless platform. We present TrEnv, a co-designed serverless platfo…

Signals in the Noise: Decoding Unexpected Engagement Patterns on Twitter
Yulin Yu, Houming Chen, Daniel Romero, Paramveer S. Dhillon
https://arxiv.org/abs/2509.08128 https://

Signals in the Noise: Decoding Unexpected Engagement Patterns on Twitter
Social media platforms offer users multiple ways to engage with content--likes, retweets, and comments--creating a complex signaling system within the attention economy. While previous research has examined factors driving overall engagement, less is known about why certain tweets receive unexpectedly high levels of one type of engagement relative to others. Drawing on Signaling Theory and Attention Economy Theory, we investigate these unexpected engagement patterns on Twitter (now known as "X"…

Replaced article(s) found for cs.NI. https://arxiv.org/list/cs.NI/new
[1/1]:
- A Deep Learning Based Resource Allocator for Communication Networks with Dynamic User Utility Dem...
Pourya Behmandpoor, Mark Eisen, Panagiotis Patrinos, Marc Moonen

DiffVolume: Diffusion Models for Volume Generation in Limit Order Books
Zhuohan Wang, Carmine Ventre
https://arxiv.org/abs/2508.08698 https://arxiv.org/pdf…

DiffVolume: Diffusion Models for Volume Generation in Limit Order Books
Modeling limit order books (LOBs) dynamics is a fundamental problem in market microstructure research. In particular, generating high-dimensional volume snapshots with strong temporal and liquidity-dependent patterns remains a challenging task, despite recent work exploring the application of Generative Adversarial Networks to LOBs. In this work, we propose a conditional \textbf{Diff}usion model for the generation of future LOB \textbf{Volume} snapshots (\textbf{DiffVolume}). We evaluate our mo…

Unveiling Biological Models Through Turing Patterns
Yuhan Li, Hongyu Liu, Catharine W. K. Lo
https://arxiv.org/abs/2509.07458 https://arxiv.org/pdf/2509.07…

Unveiling Biological Models Through Turing Patterns
Turing patterns play a fundamental role in morphogenesis and population dynamics, encoding key information about the underlying biological mechanisms. Yet, traditional inverse problems have largely relied on non-biological data such as boundary measurements, neglecting the rich information embedded in the patterns themselves. Here we introduce a new research direction that directly leverages physical observables from nature--the amplitude of Turing patterns--to achieve complete parameter identi…