Tootfinder

Mathematical Computation and Reasoning Errors by Large Language Models
Liang Zhang, Edith Aurora Graf
https://arxiv.org/abs/2508.09932 https://arxiv.org/pd…

Mathematical Computation and Reasoning Errors by Large Language Models
Large Language Models (LLMs) are increasingly utilized in AI-driven educational instruction and assessment, particularly within mathematics education. The capability of LLMs to generate accurate answers and detailed solutions for math problem-solving tasks is foundational for ensuring reliable and precise feedback and assessment in math education practices. Our study focuses on evaluating the accuracy of four LLMs (OpenAI GPT-4o and o1, DeepSeek-V3 and DeepSeek-R1) solving three categories of m…

Two out of three night trains on this trip has had our sleeper carriage cancelled or replaced last minute (causing 1hr delay).. I wonder if there's a bed bug problem in Europe at the moment

Discovery of 10,059 new three-dimensional periodic orbits of general three-body problem
Xiaoming Li, Shijun Liao
https://arxiv.org/abs/2508.08568 https://a…

Discovery of 10,059 new three-dimensional periodic orbits of general three-body problem
A very few three-dimensional (3D) periodic orbits of general three-body problem (with three finite masses) have been discovered since Newton mentioned it in 1680s. Using a high-accuracy numerical strategy we discovered 10,059 three-dimensional periodic orbits of the three-body problem in the cases of $m_{1}=m_{2}=1$ and $m_{3}=0.1n$ where $1\leq n\leq 20$ is an integer, among which 1,996 (about 20\%) are linearly stable. Note that our approach is valid for arbitrary mass $m_{3}$ so that in theo…

Should we teach vibe coding? Here's why not.
Should AI coding be taught in undergrad CS education?
1/2
I teach undergraduate computer science labs, including for intro and more-advanced core courses. I don't publish (non-negligible) scholarly work in the area, but I've got years of craft expertise in course design, and I do follow the academic literature to some degree. In other words, In not the world's leading expert, but I have spent a lot of time thinking about course design, and consider myself competent at it, with plenty of direct experience in what knowledge & skills I can expect from students as they move through the curriculum.
I'm also strongly against most uses of what's called "AI" these days (specifically, generative deep neutral networks as supplied by our current cadre of techbro). There are a surprising number of completely orthogonal reasons to oppose the use of these systems, and a very limited number of reasonable exceptions (overcoming accessibility barriers is an example). On the grounds of environmental and digital-commons-pollution costs alone, using specifically the largest/newest models is unethical in most cases.
But as any good teacher should, I constantly question these evaluations, because I worry about the impact on my students should I eschew teaching relevant tech for bad reasons (and even for his reasons). I also want to make my reasoning clear to students, who should absolutely question me on this. That inspired me to ask a simple question: ignoring for one moment the ethical objections (which we shouldn't, of course; they're very stark), at what level in the CS major could I expect to teach a course about programming with AI assistance, and expect students to succeed at a more technically demanding final project than a course at the same level where students were banned from using AI? In other words, at what level would I expect students to actually benefit from AI coding "assistance?"
To be clear, I'm assuming that students aren't using AI in other aspects of coursework: the topic of using AI to "help you study" is a separate one (TL;DR it's gross value is not negative, but it's mostly not worth the harm to your metacognitive abilities, which AI-induced changes to the digital commons are making more important than ever).
So what's my answer to this question?
If I'm being incredibly optimistic, senior year. Slightly less optimistic, second year of a masters program. Realistic? Maybe never.
The interesting bit for you-the-reader is: why is this my answer? (Especially given that students would probably self-report significant gains at lower levels.) To start with, [this paper where experienced developers thought that AI assistance sped up their work on real tasks when in fact it slowed it down] (https://arxiv.org/abs/2507.09089) is informative. There are a lot of differences in task between experienced devs solving real bugs and students working on a class project, but it's important to understand that we shouldn't have a baseline expectation that AI coding "assistants" will speed things up in the best of circumstances, and we shouldn't trust self-reports of productivity (or the AI hype machine in general).
Now we might imagine that coding assistants will be better at helping with a student project than at helping with fixing bugs in open-source software, since it's a much easier task. For many programming assignments that have a fixed answer, we know that many AI assistants can just spit out a solution based on prompting them with the problem description (there's another elephant in the room here to do with learning outcomes regardless of project success, but we'll ignore this over too, my focus here is on project complexity reach, not learning outcomes). My question is about more open-ended projects, not assignments with an expected answer. Here's a second study (by one of my colleagues) about novices using AI assistance for programming tasks. It showcases how difficult it is to use AI tools well, and some of these stumbling blocks that novices in particular face.
But what about intermediate students? Might there be some level where the AI is helpful because the task is still relatively simple and the students are good enough to handle it? The problem with this is that as task complexity increases, so does the likelihood of the AI generating (or copying) code that uses more complex constructs which a student doesn't understand. Let's say I have second year students writing interactive websites with JavaScript. Without a lot of care that those students don't know how to deploy, the AI is likely to suggest code that depends on several different frameworks, from React to JQuery, without actually setting up or including those frameworks, and of course three students would be way out of their depth trying to do that. This is a general problem: each programming class carefully limits the specific code frameworks and constructs it expects students to know based on the material it covers. There is no feasible way to limit an AI assistant to a fixed set of constructs or frameworks, using current designs. There are alternate designs where this would be possible (like AI search through adaptation from a controlled library of snippets) but those would be entirely different tools.
So what happens on a sizeable class project where the AI has dropped in buggy code, especially if it uses code constructs the students don't understand? Best case, they understand that they don't understand and re-prompt, or ask for help from an instructor or TA quickly who helps them get rid of the stuff they don't understand and re-prompt or manually add stuff they do. Average case: they waste several hours and/or sweep the bugs partly under the rug, resulting in a project with significant defects. Students in their second and even third years of a CS major still have a lot to learn about debugging, and usually have significant gaps in their knowledge of even their most comfortable programming language. I do think regardless of AI we as teachers need to get better at teaching debugging skills, but the knowledge gaps are inevitable because there's just too much to know. In Python, for example, the LLM is going to spit out yields, async functions, try/finally, maybe even something like a while/else, or with recent training data, the walrus operator. I can't expect even a fraction of 3rd year students who have worked with Python since their first year to know about all these things, and based on how students approach projects where they have studied all the relevant constructs but have forgotten some, I'm not optimistic seeing these things will magically become learning opportunities. Student projects are better off working with a limited subset of full programming languages that the students have actually learned, and using AI coding assistants as currently designed makes this impossible. Beyond that, even when the "assistant" just introduces bugs using syntax the students understand, even through their 4th year many students struggle to understand the operation of moderately complex code they've written themselves, let alone written by someone else. Having access to an AI that will confidently offer incorrect explanations for bugs will make this worse.
To be sure a small minority of students will be able to overcome these problems, but that minority is the group that has a good grasp of the fundamentals and has broadened their knowledge through self-study, which earlier AI-reliant classes would make less likely to happen. In any case, I care about the average student, since we already have plenty of stuff about our institutions that makes life easier for a favored few while being worse for the average student (note that our construction of that favored few as the "good" students is a large part of this problem).
To summarize: because AI assistants introduce excess code complexity and difficult-to-debug bugs, they'll slow down rather than speed up project progress for the average student on moderately complex projects. On a fixed deadline, they'll result in worse projects, or necessitate less ambitious project scoping to ensure adequate completion, and I expect this remains broadly true through 4-6 years of study in most programs (don't take this as an endorsement of AI "assistants" for masters students; we've ignored a lot of other problems along the way).
There's a related problem: solving open-ended project assignments well ultimately depends on deeply understanding the problem, and AI "assistants" allow students to put a lot of code in their file without spending much time thinking about the problem or building an understanding of it. This is awful for learning outcomes, but also bad for project success. Getting students to see the value of thinking deeply about a problem is a thorny pedagogical puzzle at the best of times, and allowing the use of AI "assistants" makes the problem much much worse. This is another area I hope to see (or even drive) pedagogical improvement in, for what it's worth.
1/2

A Saddle Point Algorithm for Robust Data-Driven Factor Model Problems
Shabnam Khodakaramzadeh, Soroosh Shafiee, Gabriel de Albuquerque Gleizer, Peyman Mohajerin Esfahani
https://arxiv.org/abs/2506.09776

A Saddle Point Algorithm for Robust Data-Driven Factor Model Problems
We study the factor model problem, which aims to uncover low-dimensional structures in high-dimensional datasets. Adopting a robust data-driven approach, we formulate the problem as a saddle-point optimization. Our primary contribution is a general first-order algorithm that solves this reformulation by leveraging a linear minimization oracle (LMO). We further develop semi-closed form solutions (up to a scalar) for three specific LMOs, corresponding to the Frobenius norm, Kullback-Leibler diver…

Fast adaptive tubal rank-revealing algorithm for t-product based tensor approximation
Qiaohua Liu, Jiehui Gu
https://arxiv.org/abs/2508.08557 https://arxiv…

Fast adaptive tubal rank-revealing algorithm for t-product based tensor approximation
Color images and video sequences can be modeled as three-way tensors, which admit low tubal-rank approximations via convex surrogate minimization. This optimization problem is efficiently addressed by tensor singular value thresholding (t-SVT). To mitigate the computational burden of tensor singular value decomposition (t-SVD) in each iteration, this paper introduces an adaptive randomized algorithm for tubal rank revelation in data tensors \(\mathcal{A}\). Our method selectively captures the p…

A Directed Lazy Random Walk Model to Three-Way Dynamic Matching Problem
Souvik Roy, Agamani Saha
https://arxiv.org/abs/2507.06126 https://

A Directed Lazy Random Walk Model to Three-Way Dynamic Matching Problem
This paper explores a novel extension of dynamic matching theory by analyzing a three-way matching problem involving agents from three distinct populations, each with two possible types. Unlike traditional static or two-way dynamic models, our setting captures more complex team-formation environments where one agent from each of the three populations must be matched to form a valid team. We consider two preference structures: assortative or homophilic, where agents prefer to be matched with oth…

Hodge Duals in Spherical Compactifications
Arash Azizi
https://arxiv.org/abs/2507.06324 https://arxiv.org/pdf/2507.06324

Hodge Duals in Spherical Compactifications
We present a general formalism for computing the Hodge dual of differential forms in arbitrary dimensions subject to a spherical constraint. This problem arises naturally in Kaluza-Klein compactifications, where sphere reductions demand careful treatment of differential forms constrained to lie on embedded submanifolds. We derive an explicit expression for the Hodge dual of a $p$-form in the presence of such constraints and validate our general ansatz through illustrative examples in three dime…

Probably a good day 🥵 to remind of our CoolWalks study: https://www.nature.com/articles/s41598-025-97200-2
- Above all, stop burning fossil fuels
- Buildings (and trees) cast a lot of shade in cities. We systematically quantify the benefits for 🚶🚴
- Make shade plans, b…

CoolWalks for active mobility in urban street networks - Scientific Reports
Walking is the most sustainable form of urban mobility, but is compromised by uncomfortable or unhealthy sun exposure, which is an increasing problem due to global warming. Shade from buildings can provide cooling and protection for pedestrians, but the extent of this potential benefit is unknown. Here we explore the potential for shaded walking, using building footprints and street networks from both synthetic and real cities. We introduce a route choice model with a sun avoidance parameter $$…

On three-dimensional associative algebras
U. Bekbaev, I. Rakhimov
https://arxiv.org/abs/2508.04104 https://arxiv.org/pdf/2508.04104

On three-dimensional associative algebras
The paper is devoted to the classification problem of associative algebras over any base field. We provide a list of three-dimensional associative algebras with canonical representatives of the isomorphism classes over a base field of the characteristic is not two and three. Comparisons of our lists with the most recent lists over the field of complex number in dimensions three, with some comments, are given.

Inference of maximum parsimony phylogenetic trees with model-based classical and quantum methods
Jiawei Zhang, Yibo Chen, Yang Zhou, Jun-Han Huang
https://arxiv.org/abs/2508.00468

Inference of maximum parsimony phylogenetic trees with model-based classical and quantum methods
The maximum parsimony phylogenetic tree reconstruction problem is NP-hard, presenting a computational bottleneck for classical computing and motivating the exploration of emerging paradigms like quantum computing. To this end, we design three optimization models compatible with both classical and quantum solvers. Our method directly searches the complete solution space of all possible tree topologies and ancestral states, thereby avoiding the potential biases associated with pre-constructing ca…

A posteriori error estimates for a $C^1$ virtual element method applied to the thin plate vibration problem
Franco Dassi, Andres E. Rubiano, Iv\'an Vel\'asquez
https://arxiv.org/abs/2507.06846

A posteriori error estimates for a $C^1$ virtual element method applied to the thin plate vibration problem
We propose and analyse residual-based a posteriori error estimates for the virtual element discretisation applied to the thin plate vibration problem in both two and three dimensions. Our approach involves a conforming $C^1$ discrete formulation suitable for meshes composed of polygons and polyhedra. The reliability and efficiency of the error estimator are established through a dimension-independent proof. Finally, several numerical experiments are reported to demonstrate the optimal performan…

Empirical Analysis Of Heuristic and Approximation Algorithms for the The Mutual-Visibility Problem
Vanja Stojanovi\'c, Bor Panger\v{s}i\v{c}
https://arxiv.org/abs/2507.01076

Empirical Analysis Of Heuristic and Approximation Algorithms for the The Mutual-Visibility Problem
The NP-complete mutual-visibility (MV) problem currently lacks empirical analysis on its practical behaviour despite theoretical studies. This paper addresses this gap by implementing and evaluating three distinct algorithms - a direct greedy heuristic, a hypergraph-based approximation, and a genetic algorithm - on diverse synthetic graph datasets, including those with analytically known $μ(G)$ values and general graph models. Our results demonstrate that for smaller graphs, the algorithms con…

This https://arxiv.org/abs/2505.09708 has been replaced.
initial toot: https://mastoxiv.page/@arXiv_…

The uncertainty of magnetic fields in 3D non-local thermodynamic equilibrium inversions
We describe our approach to solve the problem of ensuring the solenoidality of the magnetic field vector in three-dimensional (3D) inversions, as well as the estimation of the uncertainty in the inferred magnetic field. The solenoidality of the magnetic field vector is often disregarded in the inversion of spectropolarimetric data due to limitations in the traditional one-dimensional inversion techniques. We propose a method to ensure the solenoidal condition in 3D inversions based on our meshf…

Investigating Hallucination in Conversations for Low Resource Languages
Amit Das, Md. Najib Hasan, Souvika Sarkar, Zheng Zhang, Fatemeh Jamshidi, Tathagata Bhattacharya, Nilanjana Raychawdhury, Dongji Feng, Vinija Jain, Aman Chadha
https://arxiv.org/abs/2507.22720

Investigating Hallucination in Conversations for Low Resource Languages
Large Language Models (LLMs) have demonstrated remarkable proficiency in generating text that closely resemble human writing. However, they often generate factually incorrect statements, a problem typically referred to as 'hallucination'. Addressing hallucination is crucial for enhancing the reliability and effectiveness of LLMs. While much research has focused on hallucinations in English, our study extends this investigation to conversational data in three languages: Hindi, Farsi, and Mandari…

This https://arxiv.org/abs/2501.01817 has been replaced.
initial toot: https://mastoxiv.page/@arXiv_ees…

Distributed Framework Construction for Affine Formation Control
In affine formation control problems, the construction of the framework with universal rigidity and affine localizability is a critical prerequisite, but it has not yet been well addressed, especially when additional agents join the formation or link/agent failures emerge. Motivated by this observation, we investigate the problem of constructing affine frameworks in three scenarios, including vertex addition, edge deletion and vertex deletion. Our approach starts from the original affine format…

Estimation of Algebraic Sets: Extending PCA Beyond Linearity
Alberto Gonz\'alez-Sanz, Gilles Mordant, \'Alvaro Samperio, Bodhisattva Sen
https://arxiv.org/abs/2508.01976

Estimation of Algebraic Sets: Extending PCA Beyond Linearity
An algebraic set is defined as the zero locus of a system of real polynomial equations. In this paper we address the problem of recovering an unknown algebraic set $\mathcal{A}$ from noisy observations of latent points lying on $\mathcal{A}$ -- a task that extends principal component analysis, which corresponds to the purely linear case. Our procedure consists of three steps: (i) constructing the {\it moment matrix} from the Vandermonde matrix associated with the data set and the degree of the …

Robotic Manipulation of a Rotating Chain with Bottom End Fixed
Qi Jing Chen, Shilin Shan, Quang-Cuong Pham
https://arxiv.org/abs/2506.18355 https://…

Robotic Manipulation of a Rotating Chain with Bottom End Fixed
This paper studies the problem of using a robot arm to manipulate a uniformly rotating chain with its bottom end fixed. Existing studies have investigated ideal rotational shapes for practical applications, yet they do not discuss how these shapes can be consistently achieved through manipulation planning. Our work presents a manipulation strategy for stable and consistent shape transitions. We find that the configuration space of such a chain is homeomorphic to a three-dimensional cube. Using …

A predictor-corrector scheme for approximating signed distances using finite element methods
Amina El Bachari, Johann Rannou, Vladislav A. Yastrebov, Pierre Kerfriden, Susanne Claus
https://arxiv.org/abs/2506.17830

A predictor-corrector scheme for approximating signed distances using finite element methods
In this article, we introduce a finite element method designed for the robust computation of approximate signed distance functions to arbitrary boundaries in two and three dimensions. Our method employs a novel prediction-correction approach, involving first the solution of a linear diffusion-based prediction problem, followed by a nonlinear minimization-based correction problem associated with the Eikonal equation. The prediction step efficiently generates a suitable initial guess, significant…

Are Large Language Models Capable of Deep Relational Reasoning? Insights from DeepSeek-R1 and Benchmark Comparisons
Chi Chiu So, Yueyue Sun, Jun-Min Wang, Siu Pang Yung, Anthony Wai Keung Loh, Chun Pong Chau
https://arxiv.org/abs/2506.23128

Are Large Language Models Capable of Deep Relational Reasoning? Insights from DeepSeek-R1 and Benchmark Comparisons
How far are Large Language Models (LLMs) in performing deep relational reasoning? In this paper, we evaluate and compare the reasoning capabilities of three cutting-edge LLMs, namely, DeepSeek-R1, DeepSeek-V3 and GPT-4o, through a suite of carefully designed benchmark tasks in family tree and general graph reasoning. Our experiments reveal that DeepSeek-R1 consistently achieves the highest F1-scores across multiple tasks and problem sizes, demonstrating strong aptitude in logical deduction and …

Probing EFX via PMMS: (Non-)Existence Results in Discrete Fair Division
Jaros{\l}aw Byrka, Franciszek Malinka, Tomasz Ponitka
https://arxiv.org/abs/2507.14957

Probing EFX via PMMS: (Non-)Existence Results in Discrete Fair Division
We study the fair division of indivisible items and provide new insights into the EFX problem, which is widely regarded as the central open question in fair division, and the PMMS problem, a strictly stronger variant of EFX. Our first result constructs a three-agent instance with two monotone valuations and one additive valuation in which no PMMS allocation exists. Since EFX allocations are known to exist under these assumptions, this establishes a formal separation between EFX and PMMS. We p…

Formation of vacuum state and delta-shock in the solution of two-dimensional Riemann problem for zero pressure gas dynamics
Anamika Pandey, T. Raja Sekhar
https://arxiv.org/abs/2507.17213

Formation of vacuum state and delta-shock in the solution of two-dimensional Riemann problem for zero pressure gas dynamics
In this article, we investigate the two-dimensional pressureless Euler equations with three constant Riemann initial data. Our primary focus is on the wave interactions involving contact discontinuities and delta shocks. A distinguishing feature of the solution is the emergence of a delta shock wave which is characterized by a Dirac delta function appearing in both the density and internal energy variables. By exploiting generalized characteristic analysis, nine topologically distinct solution …

Supervised Extraction of the Thermal Sunyaev$-$Zel'dovich Effect with a Three-Dimensional Convolutional Neural Network
Cameron T. Pratt, Zhijie Qu, Joel N. Bregman
https://arxiv.org/abs/2507.13400

Supervised Extraction of the Thermal Sunyaev$-$Zel'dovich Effect with a Three-Dimensional Convolutional Neural Network
The thermal Sunyaev$-$Zel'dovich (SZ) effect offers a unique probe of the hot and diffuse universe that could help close the missing baryon problem. Traditional extractions of the SZ effect, however, exhibit systematic noise that may lead to unreliable results. In this work, we provide an alternative solution using a three-dimensional Attention Nested U-Net trained end-to-end with supervised learning. Our labeled data consists of simulated SZ signals injected into $\textit{Planck}$ frequency ma…

Distortion-Aware Hybrid Beamforming for Integrated Sensing and Communication
Zeyuan Zhang, Yue Xiu, Phee Lep Yeoh, Guangyi Liu, Zixing Wu, Ning Wei
https://arxiv.org/abs/2507.14018

Distortion-Aware Hybrid Beamforming for Integrated Sensing and Communication
This paper investigates a practical partially-connected hybrid beamforming transmitter for integrated sensing and communication (ISAC) with distortion from nonlinear power amplification. For this ISAC system, we formulate a communication rate and sensing mutual information maximization problem driven by our distortion-aware hybrid beamforming design. To address this non-convex problem, we first solve for a fully digital beamforming matrix by alternatively solving three sub-problems using manifo…

Toroidal area-preserving parameterizations of genus-one closed surfaces
Marco Sutti, Mei-Heng Yueh
https://arxiv.org/abs/2508.05111 https://arxiv.org/pdf/2…

Toroidal area-preserving parameterizations of genus-one closed surfaces
We consider the problem of computing toroidal area-preserving parameterizations of genus-one closed surfaces. We propose four algorithms based on Riemannian geometry: the projected gradient descent method, the projected conjugate gradient method, the Riemannian gradient method, and the Riemannian conjugate gradient method. Our objective function is based on the stretch energy functional, and the minimization is constrained on a power manifold of ring tori embedded in three-dimensional Euclidean…

Three-point functions from integrability in $\mathcal{N}=2$ orbifold theories
Dennis le Plat, Torben Skrzypek
https://arxiv.org/abs/2506.21323 https://

Three-point functions from integrability in $\mathcal{N}=2$ orbifold theories
Besides solving the spectral problem of $\mathcal{N}=4$ Super-Yang-Mills (SYM) theory, integrability also provides us with tools to compute the structure constants of the theory, most prominently through the hexagon formalism. We show that, with minor modifications, this formalism can also be applied to orbifolds of $\mathcal{N}=4$ SYM theory, which are integrable theories in their own right. To substantiate this claim, we test our results against a direct gauge-theory calculation at tree-level…

Global existence and optimal time-decay rates of the compressible Navier-Stokes equations with density-dependent viscosities
Jie Fan, Xiangdi Huang, Anchun Ni
https://arxiv.org/abs/2507.16436

Global existence and optimal time-decay rates of the compressible Navier-Stokes equations with density-dependent viscosities
This paper is devoted to studying the Cauchy problem for the three-dimensional isentropic compressible Navier-Stokes equations with density-dependent viscosities given by $μ=ρ^α,λ=ρ^α(α>0)$. We establish the global existence and optimal decay rates of classical solutions under the assumptions of small initial data in $L^1(\mathbb{R}^3)\cap L^2(\mathbb{R}^3)$ and the viscosity constraint $|α-1|\ll 1$. The key idea of our proof lies in the combination of Green's function method,…

Epstein shit and adjacent, Rural America, Poverty, Abuse
Everyone who's not a pedophile thinks pedophiles are bad, but there's this special obsessed hatred you'll find among poor rural Americans. The whole QAnon/Epstein obsession may not really make sense to folks raised in cities. Like, why do these people think *so much* about pedophiles? Why do they think that everyone in power is a pedophile? Why would the Pizzagate thing make sense to anyone? What is this unhinged shit? A lot of folks (who aren't anarchists) might be inclined to ask "why can't these people just let the cops take care of it?"
I was watching Legal Eagle's run down on the Trump Epstein thing earlier today and I woke up thinking about something I don't know if I've ever talked about. Now that I'm not in the US, I'm not at any risk of talking about it. I don't know how much I would have been before, but that's not something I'm gonna dig into right now. So let me tell you a story that might explain a few things.
I'm like 16, maybe 17. I have my license, so this girl I was dating/not dating/just friends with/whatever would regularly convince me to drive her and her friends around. I think she's like 15 at the time. Her friends are younger than her.
She tells me that there's a party we can go to where they have beer. She was told to invite her friends, so I can come too. We're going to pick her friends up (we regularly fill the VW Golf well beyond the legal limit and drive places) and head to the party.
So I take these girls, at least is 13 years old, down to this party. I'm already a bit sketched out bringing a 13 year old to a party. We drive out for a while. It's in the country. We drive down a long dark road. Three are some barrel fires and a shack. This is all a bit strange, but not too abnormal for this area. We're a little ways outside of a place called Mill City (in Oregon).
We park and walk towards the shack. This dude who looks like a rat comes up and offers us beer. He laughs and talks to the girl who invited me, "What's he doing here? You're supposed to bring your girl friends." She's like, "He's our ride." I don't remember if he offered me a beer or not.
We go over to this shed and everyone starts smoking, except me because I didn't smoke until I turned 18. The other girls start talking about the rat face dude, who's wandered over by the fire with some other guys. They're mainly teasing one of the 13 year old girls about having sex with him a bunch of times. They say he's like, 32 or something. The other girls joke about him only having sex with 13 year olds because he's too ugly to have sex with anyone closer to his own age.
Somewhere along the line it comes out that he's a cop. I never forgot that, it's absolutely seared in to my memory. I can picture his face perfectly still, decades later, and them talking about how he's a deputy, he was in his 30's, and he was having sex with a 13 year old girl. I was the only boy there, but there were a few older men. This was a chunk of the good ol' boys club of the town. I think there were a couple of cops besides the one deputy, and a judge or the mayor or some kind of big local VIP.
I kept trying to get my friend to leave, but she wanted to stay. Turns out under age drinking with cops seems like a great deal if you're a kid because you know you won't get busted. I left alone, creeped the fuck out.
I was told later that I wasn't invited and that I couldn't talk about it, I've always been good at compartmentalization, so I never did.
Decades later it occurred to me what was actually happening. I'm pretty sure that cop was giving meth he'd seized as evidence to these kids. This wasn't some one-off thing. It was regular. Who knows how many decades it went on after I left, or how many decades it had been going on before I found out. I knew this type of thing had happened at least a few times before because that's how that 13 year old girl and that 32 year old cop had hooked up in the first place.
Hearing about Epstein's MO, targeting these teenage girls from fucked up backgrounds, it's right there for me. I wouldn't be surprised if they were involved in sex trafficking of minors or some shit like that... but who would you call if you found out? Half the sheriff's department was there and the other half would cover for them.
You live in the city and shit like that doesn't happen, or at least you don't think it happens. But rural poor folks have this intuition about power and abuse. It's right there and you know it.
Trump is such a familiar character for me, because he's exactly that small town mayor or sheriff. He'll will talk about being tough on crime and hunting down pedophiles, while hanging out at a party that exists so people can fuck 8th graders.
The problem with the whole thing is that rural folks will never break the cognitive dissonance between "kill the peods" and "back the blue." They'll never go kill those cops. No, the pedos must be somewhere else. It must be the elites. It must be outsiders. It can't be the cops and good ol' boys everyone respects. It can't be the mayor who rigs the election to win every time. It can't be the "good upstanding" sheriff. Nah, it's the Clintons.
To be fair, it's probably also the Clitnons, a bunch of other politicians, billionaires, etc. Epstein was exactly who everyone thought he was, and he didn't get away with it for so long without a whole lot of really powerful help.
There are still powerful people who got away with involvement with #Epstein. #Trump is one of them, but I don't really believe that he's the only one.
#USPol #ACAB