Tootfinder

For my next modern culture and media studies work, I'd like to use "THE SIMPLE NEW SOCIO-ECONOMICGEOPOLITICALBIO-HISTORICAL APPROACH" (TSR 1987)
(The eighties were a very weird time, y'all.)
(I found this in WCA #26, ZODIAC ATTACK)

tryna make myself mentally register im an adult now. are there any adult things besides
- jobs (my workplace is filled with child laborers)
- independence (in this economy?)
- sex (need to be spiritually bonded with them to feel like i won't drop dead)
- drinking (not old enough to drink)
- gambling (the house always wins)
- voting (i hit voting age in an age where it don't mean shit)

Nonlinear Dynamics of Wind-Drift Currents at Mid-Latitudes
Christian Puntini
https://arxiv.org/abs/2506.10454 https://arxiv.org/pdf/2…

Nonlinear Dynamics of Wind-Drift Currents at Mid-Latitudes
Starting from the Navier-Stokes equation in the $f$-plane approximation, we provide an exact and explicit solution of the governing equations at leading order for fluid flows in the upper layer of the ocean, driven by a wind stress. Such a solution highlights the presence of a mean Ekman current superimposed to trochoidal oscillations and a background geostrophic current.

The space of multifurcating ranked tree shapes: enumeration, lattice structure, and Markov chains
Julie Zhang, Noah A. Rosenberg, Julia A. Palacios
https://arxiv.org/abs/2506.10856

The space of multifurcating ranked tree shapes: enumeration, lattice structure, and Markov chains
Coalescent models of bifurcating genealogies are used to infer evolutionary parameters from molecular data. However, there are many situations where bifurcating genealogies do not accurately reflect the true underlying ancestral history of samples, and a multifurcating genealogy is required. The space of multifurcating genealogical trees, where nodes can have more than two descendants, is largely underexplored in the setting of coalescent inference. In this paper, we examine the space of rooted…

Augmenting Large Language Models with Static Code Analysis for Automated Code Quality Improvements
Seyed Moein Abtahi, Akramul Azim
https://arxiv.org/abs/2506.10330

Augmenting Large Language Models with Static Code Analysis for Automated Code Quality Improvements
This study examined code issue detection and revision automation by integrating Large Language Models (LLMs) such as OpenAI's GPT-3.5 Turbo and GPT-4o into software development workflows. A static code analysis framework detects issues such as bugs, vulnerabilities, and code smells within a large-scale software project. Detailed information on each issue was extracted and organized to facilitate automated code revision using LLMs. An iterative prompt engineering process is applied to ensure tha…

This https://arxiv.org/abs/2505.22083 has been replaced.
initial toot: https://mastoxiv.page/@arXiv_qu…

Hyperbolic recurrent neural network as the first type of non-Euclidean neural quantum state ansatz
In this work, we introduce the first type of non-Euclidean neural quantum state (NQS) ansatz, in the form of the hyperbolic GRU (a variant of recurrent neural networks (RNNs)), to be used in the Variational Monte Carlo method of approximating the ground state wavefunction for quantum many-body systems. In particular, we examine the performances of NQS ansatzes constructed from both conventional or Euclidean RNN/GRU and from hyperbolic GRU in the prototypical settings of the one- and two-dimensi…

Intelligent Travel Activity Monitoring: Generalized Distributed Acoustic Sensing Approaches
Ruikang Zhong, Chia-Yen Chiang, Mona Jaber, Rupert De Wilde, Peter Hayward
https://arxiv.org/abs/2506.10237

Intelligent Travel Activity Monitoring: Generalized Distributed Acoustic Sensing Approaches
Obtaining data on active travel activities such as walking, jogging, and cycling is important for refining sustainable transportation systems (STS). Effectively monitoring these activities not only requires sensing solutions to have a joint feature of being accurate, economical, and privacy-preserving, but also enough generalizability to adapt to different climate environments and deployment conditions. In order to provide a generalized sensing solution, a deep learning (DL)-enhanced distribute…

$D$ and $D^*$ mesons in isospin asymmetric nuclear medium
Anshu Gautam, Dhananjay Singh, Navpreet Kaur, Satyajit Puhan, Suneel Dutt, Harleen Dahiya, Arvind Kumar
https://arxiv.org/abs/2506.08707

$D$ and $D^*$ mesons in isospin asymmetric nuclear medium
We investigate the properties of pseudoscalar $D$ and vector $D^*$ mesons in an isospin asymmetric nuclear medium using a hybrid approach that integrates the light-front quark model with the chiral SU(3) quark mean field model. The influence of isospin asymmetric nuclear medium is examined by utilizing the in-medium quark masses derived from the chiral SU(3) quark mean field model as an input in the light-front quark model to study the medium modification of $D$ mesons. We examine the impact of…

Inference on panel data models with a generalized factor structure
Juan M. Rodriguez-Poo, Alexandra Soberon, Stefan Sperlich
https://arxiv.org/abs/2506.10690

Inference on panel data models with a generalized factor structure
We consider identification, inference and validation of linear panel data models when both factors and factor loadings are accounted for by a nonparametric function. This general specification encompasses rather popular models such as the two-way fixed effects and the interactive fixed effects ones. By applying a conditional mean independence assumption between unobserved heterogeneity and the covariates, we obtain consistent estimators of the parameters of interest at the optimal rate of conve…

Foundation Models in Medical Imaging -- A Review and Outlook
Vivien van Veldhuizen, Vanessa Botha, Chunyao Lu, Melis Erdal Cesur, Kevin Groot Lipman, Edwin D. de Jong, Hugo Horlings, Cl\'arisa Sanchez, Cees Snoek, Ritse Mann, Eric Marcus, Jonas Teuwen
https://arxiv.org/abs/2506.09095…

Foundation Models in Medical Imaging -- A Review and Outlook
Foundation models (FMs) are changing the way medical images are analyzed by learning from large collections of unlabeled data. Instead of relying on manually annotated examples, FMs are pre-trained to learn general-purpose visual features that can later be adapted to specific clinical tasks with little additional supervision. In this review, we examine how FMs are being developed and applied in pathology, radiology, and ophthalmology, drawing on evidence from over 150 studies. We explain the co…