Tootfinder

Rule-Assisted Attribute Embedding
Sibo Zhao, Michael Bewong, Selasi Kwashie, Junwei Hu, Zaiwen Feng
https://arxiv.org/abs/2506.08314 https://

Rule-Assisted Attribute Embedding
Recommendation systems often overlook the rich attribute information embedded in property graphs, limiting their effectiveness. Existing graph convolutional network (GCN) models either ignore attributes or rely on simplistic triples, failing to capture deeper semantic structures. We propose RAE (Rule- Assisted Approach for Attribute Embedding), a novel method that improves recommendations by mining semantic rules from property graphs to guide attribute embedding. RAE per…

Explaining Risks: Axiomatic Risk Attributions for Financial Models
Dangxing Chen
https://arxiv.org/abs/2506.06653 https://arxiv.org/p…

Explaining Risks: Axiomatic Risk Attributions for Financial Models
In recent years, machine learning models have achieved great success at the expense of highly complex black-box structures. By using axiomatic attribution methods, we can fairly allocate the contributions of each feature, thus allowing us to interpret the model predictions. In high-risk sectors such as finance, risk is just as important as mean predictions. Throughout this work, we address the following risk attribution problem: how to fairly allocate the risk given a model with data? We demons…

This https://arxiv.org/abs/2504.06550 has been replaced.
initial toot: https://mastoxiv.page/@arXiv_eco…

Blameocracy: Causal Attribution in Political Communication
We propose a supervised method to detect causal attribution in political texts, distinguishing between expressions of merit and blame. Analyzing four million tweets shared by U.S. Congress members from 2012 to 2023, we document a pronounced shift toward causal attribution following the 2016 presidential election. The shift reflects changes in rhetorical strategy rather than compositional variation in the actors or topics of the political debate. Within causal communication, a trade-off emerges …

Even if “AI” worked (it doesn’t), there’s many reasons why you shouldn’t use it:
1. It’s destroying Internet sites that you love as you use chat bots instead of actually going to sources of information—this will cause them to be less active and eventually shut down.
2. Pollution and water use from server farms cause immediate harm; often—just like other heavy industry—these are built in underprivileged communities and harming poor people. Without any benefits as the big tech companies get tax breaks and don’t pay for power, while workers aren’t from the community but commute in.
3. The basic underlying models of any LLM rely on stolen data, even when specific extra data is obtained legally. Chatbots can’t learn to speak English just by reading open source code.
4. You’re fueling a speculation bubble that is costing many people their jobs—because the illusion of “efficiency” is kept up by firing people and counting that as profit.
5. Whenever you use the great cheat machine in the cloud you’re robbing yourself from doing real research, writing or coding—literally atrophying your brain and making you stupider.
It’s a grift, through and through.

Classification by Separating Hypersurfaces: An Entropic Approach
Argimiro Arratia, Mahmoud El Daou, Henryk Gzyl
https://arxiv.org/abs/2507.02732 https://…

Classification by Separating Hypersurfaces: An Entropic Approach
We consider the following classification problem: Given a population of individuals characterized by a set of attributes represented as a vector in ${\mathbb R}^N$, the goal is to find a hyperplane in ${\mathbb R}^N$ that separates two sets of points corresponding to two distinct classes. This problem, with a history dating back to the perceptron model, remains central to machine learning. In this paper we propose a novel approach by searching for a vector of parameters in a bounded $N$-dimensi…

Poster No 857: Plastic changes in the occipital regions during pitch perception associate with blindness onset age; "Specifically, the primary visual cortex remains engaged in pitch processing tasks following late blindness." https://doi.org/10.5281/zenodo.15641972

Primordial Black Holes as Coma Cluster Dark Matter and the Unresolved {\gamma}-Ray Background
Jeremy Mould, Bhashin Thakore
https://arxiv.org/abs/2507.01421

Primordial Black Holes as Coma Cluster Dark Matter and the Unresolved γ-Ray Background
If 0.1% of the dark matter in the Coma cluster is constituted by primordial black holes (PBHs) with masses ranging from 10^-19 to 10^-17 solar masses, then the observed GeV γ-ray emission from the cluster could potentially be attributed to Hawking radiation. The emitted spectrum is inversely proportional to the black hole's mass, meaning lighter PBHs radiate at higher energies, potentially falling within the GeV range. If 0.1% of the Coma cluster's dark matter is PBH in this mass range, a fit …

Computer scientist Greg Nelson and I presented "Following AI's Footsteps from School to Society" at the 2025 UMaine Faculty Institute https://mycampus.maine.edu/web/uc-faculty-portal/faculty-institute-2025 including the IMPACT RISK framew…

ChatGPT didn't object to putting a red nose on the dog or painting the rabbits green, but at least it didn't suggest I use staplers on the mice.
#scrooged #chatgpt

Exocomets of $\beta$ Pictoris I: Exocomet destruction, sodium and disk line variability in 17 years of HARPS observations
H. J. Hoeijmakers, K. P. Jaworska, B. Prinoth
https://arxiv.org/abs/2505.21625

Exocomets of $β$ Pictoris I: Exocomet destruction, sodium and disk line variability in 17 years of HARPS observations
The young $β$ Pictoris system has been monitored with high-resolution optical spectrographs for decades. These observations have revealed strongly variable absorption in the Ca II H\&K lines attributed to in-falling cometary bodies. Since 2003, over 9000 HARPS observations of $β$ Pictoris have been taken and many of these have not yet been used for exocomet studies. We search these spectra for new exocomet phenomenology enabled by the long time coverage and large volume of this dataset. We sy…