Tootfinder

Finiteness of purely cosmetic fillings
Kazuhiro Ichihara
https://arxiv.org/abs/2509.03788 https://arxiv.org/pdf/2509.03788…

Finiteness of purely cosmetic fillings
A pair of Dehn fillings on a compact, orientable 3-manifold with a torus boundary is said to be purely cosmetic if the resulting 3-manifolds are orientation-preservingly homeomorphic. In this paper, we show that if the torus boundary is incompressible, then there are only finitely many pairs of purely cosmetic fillings.

"A paper published last month in the Journal of the American Medical Association analyzed drug seizures by law enforcement in San Francisco and concluded that such interdictions were associated with an increase in fatal overdoses in the surrounding area the next day."
Mayor Lurie's #WarOnDrugs 2.0 is not just failing, it's actively killing people. Public health requires m…

R2GenKG: Hierarchical Multi-modal Knowledge Graph for LLM-based Radiology Report Generation
Futian Wang, Yuhan Qiao, Xiao Wang, Fuling Wang, Yuxiang Zhang, Dengdi Sun
https://arxiv.org/abs/2508.03426

R2GenKG: Hierarchical Multi-modal Knowledge Graph for LLM-based Radiology Report Generation
X-ray medical report generation is one of the important applications of artificial intelligence in healthcare. With the support of large foundation models, the quality of medical report generation has significantly improved. However, challenges such as hallucination and weak disease diagnostic capability still persist. In this paper, we first construct a large-scale multi-modal medical knowledge graph (termed M3KG) based on the ground truth medical report using the GPT-4o. It contains 2477 enti…

The White House published a photo in which Netanyahu is seen forced by Trump to apologize to Qatari PM Al Thani while reading a piece of paper on the phone. I guess the script was 99% sure provided to him by Trump or his administration.
https://www.flickr.com/photos/whitehouse/5

On the sum-of-squares function
Vitalii V. Iudelevich
https://arxiv.org/abs/2508.02701 https://arxiv.org/pdf/2508.02701

On the sum-of-squares function
In this paper, we derive the following asymptotic formula $$ \mathop{{\sum}'}_{n\leqslant x}\dfrac{r(n)}{r(n+1)} = {x}{(\ln x)^{-3/4}}(c+o(1)),\ \ x \to +\infty,$$ where $r(n)$ is the number of representations of $n$ as a sum of two squares, $c$ is a positive constant, and the prime indicates summation over those $n$ for which $r(n+1)\neq 0$.

Spatial-Temporal-Spectral Mamba with Sparse Deformable Token Sequence for Enhanced MODIS Time Series Classification
Zack Dewis, Zhengsen Xu, Yimin Zhu, Motasem Alkayid, Mabel Heffring, Lincoln Linlin Xu
https://arxiv.org/abs/2508.02839

Spatial-Temporal-Spectral Mamba with Sparse Deformable Token Sequence for Enhanced MODIS Time Series Classification
Although MODIS time series data are critical for supporting dynamic, large-scale land cover land use classification, it is a challenging task to capture the subtle class signature information due to key MODIS difficulties, e.g., high temporal dimensionality, mixed pixels, and spatial-temporal-spectral coupling effect. This paper presents a novel spatial-temporal-spectral Mamba (STSMamba) with deformable token sequence for enhanced MODIS time series classification, with the following key contrib…

Decoupled Entity Representation Learning for Pinterest Ads Ranking
Jie Liu, Yinrui Li, Jiankai Sun, Kungang Li, Han Sun, Sihan Wang, Huasen Wu, Siyuan Gao, Paulo Soares, Nan Li, Zhifang Liu, Haoyang Li, Siping Ji, Ling Leng, Prathibha Deshikachar
https://arxiv.org/abs/2509.04337

Decoupled Entity Representation Learning for Pinterest Ads Ranking
In this paper, we introduce a novel framework following an upstream-downstream paradigm to construct user and item (Pin) embeddings from diverse data sources, which are essential for Pinterest to deliver personalized Pins and ads effectively. Our upstream models are trained on extensive data sources featuring varied signals, utilizing complex architectures to capture intricate relationships between users and Pins on Pinterest. To ensure scalability of the upstream models, entity embeddings are …

Constructing new probability distributions on the unit interval
Roberto Vila, Helton Saulo, Poliana Matos, Subhankar Dutta
https://arxiv.org/abs/2508.01154 https://

Constructing new probability distributions on the unit interval
In this paper, we introduce a novel method for constructing probability distributions on the unit interval by exploiting the non-injective transformation defined by the ratio of two positive random variables, $X$ and $Y$. For simplicity and tractability, we focus on independent random variables $X$ and $Y$ following gamma distributions. We derive some results including laws, density functions, quantile function and closed-form expressions for the moments, and discuss maximum likelihood estimati…

Investigating the Cognitive Response of Brake Lights in Initiating Braking Action Using EEG
Ramaswamy Palaniappan, Surej Mouli, Howard Bowman, Ian McLoughlin
https://arxiv.org/abs/2508.03274

Investigating the Cognitive Response of Brake Lights in Initiating Braking Action Using EEG
Half of all road accidents result from either lack of driver attention or from maintaining insufficient separation between vehicles. Collision from the rear, in particular, has been identified as the most common class of accident in the UK, and its influencing factors have been widely studied for many years. Rear-mounted stop lamps, illuminated when braking, are the primary mechanism to alert following drivers to the need to reduce speed or brake. This paper develops a novel brain response appr…

Even Faster Kernel Matrix Linear Algebra via Density Estimation
Rikhav Shah, Sandeep Silwal, Haike Xu
https://arxiv.org/abs/2510.02540 https://arxiv.org/pd…

Even Faster Kernel Matrix Linear Algebra via Density Estimation
This paper studies the use of kernel density estimation (KDE) for linear algebraic tasks involving the kernel matrix of a collection of $n$ data points in $\mathbb R^d$. In particular, we improve upon existing algorithms for computing the following up to $(1+\varepsilon)$ relative error: matrix-vector products, matrix-matrix products, the spectral norm, and sum of all entries. The runtimes of our algorithms depend on the dimension $d$, the number of points $n$, and the target error $\varepsilon…