Tootfinder

From Pixels to Damage Severity: Estimating Earthquake Impacts Using Semantic Segmentation of Social Media Images
Danrong Zhang, Huili Huang, N. Simrill Smith, Nimisha Roy, J. David Frost
https://arxiv.org/abs/2507.02781

From Pixels to Damage Severity: Estimating Earthquake Impacts Using Semantic Segmentation of Social Media Images
In the aftermath of earthquakes, social media images have become a crucial resource for disaster reconnaissance, providing immediate insights into the extent of damage. Traditional approaches to damage severity assessment in post-earthquake social media images often rely on classification methods, which are inherently subjective and incapable of accounting for the varying extents of damage within an image. Addressing these limitations, this study proposes a novel approach by framing damage seve…

Defining and classifying models of groups: The social ontology of higher-order networks
Jonathan St-Onge, Randall Harp, Giulio Burgio, Timothy M. Waring, Juniper Lovato, Laurent H\'ebert-Dufresne
https://arxiv.org/abs/2507.02758

Defining and classifying models of groups: The social ontology of higher-order networks
In complex systems research, the study of higher-order interactions has exploded in recent years. Researchers have formalized various types of group interactions, such as public goods games, biological contagion, and information broadcasting, showing how higher-order networks can capture group effects more directly than pairwise models. However, equating hyperedges-edges involving more than two agents-with groups can be misleading, as it obscures the polysemous nature of ``group interactions''.…

As part of the #IWP9 #hackathon, we were debugging the bringup of the RISC-V port of 9front to QEMU yesterday. It was really painful, compared to my experiences with other kernels like Linux and the ease of Rust and its built-in print format capabilities. We were finally able to use uartputs with a s…

Active Learning for Forecasting Severity among Patients with Post Acute Sequelae of SARS-CoV-2
Jing Wang, Amar Sra, Jeremy C. Weiss
https://arxiv.org/abs/2506.22444

Active Learning for Forecasting Severity among Patients with Post Acute Sequelae of SARS-CoV-2
The long-term effects of Postacute Sequelae of SARS-CoV-2, known as PASC, pose a significant challenge to healthcare systems worldwide. Accurate identification of progression events, such as hospitalization and reinfection, is essential for effective patient management and resource allocation. However, traditional models trained on structured data struggle to capture the nuanced progression of PASC. In this study, we introduce the first publicly available cohort of 18 PASC patients, with text t…

A Data-Driven Model Predictive Controller to manage epidemics: The case of SARS-CoV-2 in Mauritius
S. Z. Sayed Hassen, A. Aboudonia, J. Lygeros
https://arxiv.org/abs/2507.01996

A Data-Driven Model Predictive Controller to manage epidemics: The case of SARS-CoV-2 in Mauritius
This work investigates the benefits of implementing a systematic approach to social isolation policies during epidemics. We develop a mixed integer data-driven model predictive control (MPC) scheme based on an SIHRD model which is identified from available data. The case of the spread of the SARS-CoV-2 virus (also known as COVID-19) in Mauritius is used as a reference point with data obtained during the period December 2021 to May 2022. The isolation scheme is designed with the control decision…

Beyond Static Responses: Multi-Agent LLM Systems as a New Paradigm for Social Science Research
Jennifer Haase, Sebastian Pokutta
https://arxiv.org/abs/2506.01839

Beyond Static Responses: Multi-Agent LLM Systems as a New Paradigm for Social Science Research
As large language models (LLMs) transition from static tools to fully agentic systems, their potential for transforming social science research has become increasingly evident. This paper introduces a structured framework for understanding the diverse applications of LLM-based agents, ranging from simple data processors to complex, multi-agent systems capable of simulating emergent social dynamics. By mapping this developmental continuum across six levels, the paper clarifies the technical and …

"Ahead of hosting COP30, Brazil is set to weaken environmental licensing"
#Brazil #COP30 #Environment #Climate

Ahead of hosting COP30, Brazil is set to weaken environmental licensing
The new rules, considered unconstitutional by experts, would benefit around 80% of the ventures with a self-licensing process that exempts environmental impact studies and mitigation measures.

TRAPDOC: Deceiving LLM Users by Injecting Imperceptible Phantom Tokens into Documents
Hyundong Jin, Sicheol Sung, Shinwoo Park, SeungYeop Baik, Yo-Sub Han
https://arxiv.org/abs/2506.00089

TRAPDOC: Deceiving LLM Users by Injecting Imperceptible Phantom Tokens into Documents
The reasoning, writing, text-editing, and retrieval capabilities of proprietary large language models (LLMs) have advanced rapidly, providing users with an ever-expanding set of functionalities. However, this growing utility has also led to a serious societal concern: the over-reliance on LLMs. In particular, users increasingly delegate tasks such as homework, assignments, or the processing of sensitive documents to LLMs without meaningful engagement. This form of over-reliance and misuse is em…

A Budgeted Multi-Level Monte Carlo Method for Full Field Estimates of Multi-PDE Problems
Niklas Baumgarten, Robert Kutri, Robert Scheichl
https://arxiv.org/abs/2506.01644

A Budgeted Multi-Level Monte Carlo Method for Full Field Estimates of Multi-PDE Problems
We present a high-performance budgeted multi-level Monte Carlo method for estimates on the entire spatial domain of multi-PDE problems with random input data. The method is designed to operate optimally within memory and CPU-time constraints and eliminates the need for a priori knowledge of the problem's regularity and the algorithm's potential memory demand. To achieve this, we build on the budgeted multi-level Monte Carlo framework and enhance it with a sparse multi-index update algorithm ope…

Alignment Phase Transition in Socially Driven Motion
Debasish Sarker, Yi Zhang, Lynn K. Perry, Daniel S. Messinger, Chaoming Song
https://arxiv.org/abs/2506.01550

Alignment Phase Transition in Socially Driven Motion
Collective human movement is a hallmark of complex systems, exhibiting emergent order across diverse settings, from pedestrian flows to biological collectives. In high-speed scenarios, alignment interactions ensure efficient flow and navigation. In contrast, alignment in low-speed, socially engaged contexts emerges not from locomotion goals but from interpersonal interaction. Using high-resolution spatial and orientation data from preschool classrooms, we uncover a sharp, distance-dependent tra…