Tootfinder

"We can conclude that the brain has the capacity to distinguish virtual infectious patterns,
become activated,
and link this activation to a downstream response,
⭐️ resulting in systemic immunity,"
wrote Camilla Jandus, co-author of the study paper
https://www.

Human immune system reacts to the mere sight of infection
Researchers monitored brain activity when people were shown images of sick-looking avatars via VR. The brain appeared to spark an immune response.

Resonance Complexity Theory and the Architecture of Consciousness: A Field-Theoretic Model of Resonant Interference and Emergent Awareness
Michael Arnold Bruna
https://arxiv.org/abs/2505.20580

Resonance Complexity Theory and the Architecture of Consciousness: A Field-Theoretic Model of Resonant Interference and Emergent Awareness
This paper introduces Resonance Complexity Theory (RCT), which proposes that consciousness emerges from stable interference patterns of oscillatory neural activity. These patterns, shaped by recursive feedback and constructive interference, must exceed critical thresholds in complexity, coherence, gain, and fractal dimensionality to give rise to conscious experience. The resulting spatiotemporal attractors encode subjective awareness as dynamic resonance structures distributed across the neural…

Unlock the Potential of Fine-grained LLM Serving via Dynamic Module Scaling
Jingfeng Wu, Yiyuan He, Minxian Xu, Xitong Gao, Kejiang Ye, Chengzhong Xu
https://arxiv.org/abs/2507.18006

Unlock the Potential of Fine-grained LLM Serving via Dynamic Module Scaling
The rise of large language models (LLMs) has created new opportunities across various fields but has also introduced significant challenges in resource management. Current LLM serving systems face a fundamental tension: balancing serving demands with limited resources while adapting to unpredictable traffic patterns. Static deployments lead to suboptimal resource utilization and performance degradation under dynamic workloads. Furthermore, the high cost of adjusting instances hinders dynamic sc…

Recursive Equations For Imputation Of Missing Not At Random Data With Sparse Pattern Support
Trung Phung, Kyle Reese, Ilya Shpitser, Rohit Bhattacharya
https://arxiv.org/abs/2507.16107

Recursive Equations For Imputation Of Missing Not At Random Data With Sparse Pattern Support
A common approach for handling missing values in data analysis pipelines is multiple imputation via software packages such as MICE (Van Buuren and Groothuis-Oudshoorn, 2011) and Amelia (Honaker et al., 2011). These packages typically assume the data are missing at random (MAR), and impose parametric or smoothing assumptions upon the imputing distributions in a way that allows imputation to proceed even if not all missingness patterns have support in the data. Such assumptions are unrealistic in…

Is anyone aware of publications or research on what sort of bugs LLM-generated or LLM-assisted code tends to have?
Like, we have a huge body of knowledge in the security community about how to audit human-generated codebases for the types of bugs that human developers commonly write.
But we don't have that kind of data yet (AFAIK) for the vibe-coded monstrosities all of us are going to be pentesting soon.
Gut feelings:
* There are some common threads and patterns …

This https://arxiv.org/abs/2505.03458 has been replaced.
initial toot: https://mastoxiv.page/@arXiv_qbi…

The classification of Alzheimer's disease and mild cognitive impairment improved by dynamic functional network analysis
Brain network analysis using functional MRI has advanced our understanding of cortical activity and its changes in neurodegenerative disorders that cause dementia. Recently, research in brain connectivity has focused on dynamic (time-varying) brain networks that capture both spatial and temporal information on cortical, regional co-activity patterns. However, this approach has been largely unexplored within the Alzheimer's spectrum. We analysed age- and sex-matched static and dynamic fMRI brain…

Causal Graph Fuzzy LLMs: A First Introduction and Applications in Time Series Forecasting
Omid Orang, Patricia O. Lucas, Gabriel I. F. Paiva, Petronio C. L. Silva, Felipe Augusto Rocha da Silva, Adriano Alonso Veloso, Frederico Gadelha Guimaraes
https://arxiv.org/abs/2507.17016

Causal Graph Fuzzy LLMs: A First Introduction and Applications in Time Series Forecasting
In recent years, the application of Large Language Models (LLMs) to time series forecasting (TSF) has garnered significant attention among researchers. This study presents a new frame of LLMs named CGF-LLM using GPT-2 combined with fuzzy time series (FTS) and causal graph to predict multivariate time series, marking the first such architecture in the literature. The key objective is to convert numerical time series into interpretable forms through the parallel application of fuzzification and c…

Bridging Pattern-Aware Complexity with NP-Hard Optimization: A Unifying Framework and Empirical Study
Olivier Saidi
https://arxiv.org/abs/2506.13810 https:…

Bridging Pattern-Aware Complexity with NP-Hard Optimization: A Unifying Framework and Empirical Study
NP hard optimization problems like the Traveling Salesman Problem (TSP) defy efficient solutions in the worst case, yet real-world instances often exhibit exploitable patterns. We propose a novel patternaware complexity framework that quantifies and leverages structural regularities e.g., clustering, symmetry to reduce effective computational complexity across domains, including financial forecasting and LLM optimization. With rigorous definitions, theorems, and a meta learning driven solver pi…

LLM-Based Social Simulations Require a Boundary
Zengqing Wu, Run Peng, Takayuki Ito, Chuan Xiao
https://arxiv.org/abs/2506.19806 https://

LLM-Based Social Simulations Require a Boundary
This position paper argues that large language model (LLM)-based social simulations should establish clear boundaries to meaningfully contribute to social science research. While LLMs offer promising capabilities for modeling human-like agents compared to traditional agent-based modeling, they face fundamental limitations that constrain their reliability for social pattern discovery. The core issue lies in LLMs' tendency towards an ``average persona'' that lacks sufficient behavioral heterogene…

A large-scale distributed parallel discrete event simulation engines based on Warped2 for Wargaming simulation
Xiaoning Jia, Ruilin Kong, Guangya Si, Bilong Shen, Zhe Ji
https://arxiv.org/abs/2507.18050

A large-scale distributed parallel discrete event simulation engines based on Warped2 for Wargaming simulation
Rising demand for complex simulations highlights conventional engines'scalability limits, spurring Parallel Discrete Event Simulation (PDES) adoption.Warped2, a PDES engine leveraging Time Warp synchronization with Pending Event Set optimization, delivers strong performance, it struggles with inherent wargaming limitations: inefficient LP resource allocation during synchronization and unaddressed complex entity interaction patterns. To address these challenges, we present an optimized framework…