Tootfinder

Fixed points of Personalized PageRank centrality: From irreducible to reducible networks
David Aleja, Julio Flores, Eva Primo, Daniel Rodr\'iguez, Miguel Romance
https://arxiv.org/abs/2507.18652

Fixed points of Personalized PageRank centrality: From irreducible to reducible networks
In this paper we analyze the PageRank of a complex network as a function of its personalization vector. By using this approach, a complete characterization of the existence and uniqueness of fixed points of PageRank of a graph is given in terms of the number and nature of its strongly connected components. The method presented includes the use of a feedback-PageRank in order to compute exactly the fixed points following the classic Power's Method in terms of the (left-hand) Perron vector of eac…

The Gauss-Manin system of an ICIS
Guillem Blanco
https://arxiv.org/abs/2506.20280 https://arxiv.org/pdf/2506.20280

The Gauss-Manin system of an ICIS
For an isolated complete intersection singularity (ICIS), we define and study its Gauss-Manin system and its associated Hodge filtration. We show the relation between the Hodge filtration and a generalized Brieskorn lattice and study conditions for the existence of a microlocal structure. Using these ideas, we relate the residue on the saturated Brieskorn lattice with a $b$-function introduced by Torrelli.

Call Me Maybe: Enhancing JavaScript Call Graph Construction using Graph Neural Networks
Masudul Hasan Masud Bhuiyan, Gianluca De Stefano, Giancarlo Pellegrino, Cristian-Alexandru Staicu
https://arxiv.org/abs/2506.18191

Call Me Maybe: Enhancing JavaScript Call Graph Construction using Graph Neural Networks
Static analysis plays a key role in finding bugs, including security issues. A critical step in static analysis is building accurate call graphs that model function calls in a program. However, due to hard-to-analyze language features, existing call graph construction algorithms for JavaScript are neither sound nor complete. Prior work shows that even advanced solutions produce false edges and miss valid ones. In this work, we assist these tools by identifying missed call edges. Our main idea i…

Sparse optimal control for infinite-dimensional linear systems with applications to graphon control
Takuya Ikeda, Masaaki Nagahara
https://arxiv.org/abs/2507.18030 https://

Sparse optimal control for infinite-dimensional linear systems with applications to graphon control
Large-scale networked systems typically operate under resource constraints, and it is also difficult to exactly obtain the network structure between nodes. To address these issues, this paper investigates a sparse optimal control for infinite-dimensional linear systems and its application to networked systems where the network structure is represented by a limit function called a graphon that captures the overall connection pattern. The contributions of this paper are twofold: (i) To reduce com…

This https://arxiv.org/abs/2409.08906 has been replaced.
initial toot: https://mastoxiv.page/@arXiv_ees…

Gaussian is All You Need: A Unified Framework for Solving Inverse Problems via Diffusion Posterior Sampling
Diffusion models can generate a variety of high-quality images by modeling complex data distributions. Trained diffusion models can also be very effective image priors for solving inverse problems. Most of the existing diffusion-based methods integrate data consistency steps by approximating the likelihood function within the diffusion reverse sampling process. In this paper, we show that the existing approximations are either insufficient or computationally inefficient. To address these issues…

A Probabilistic Approach to Uncertainty Quantification Leveraging 3D Geometry
Rushil Desai, Frederik Warburg, Trevor Darrell, Marissa Ramirez de Chanlatte
https://arxiv.org/abs/2507.06269

A Probabilistic Approach to Uncertainty Quantification Leveraging 3D Geometry
Quantifying uncertainty in neural implicit 3D representations, particularly those utilizing Signed Distance Functions (SDFs), remains a substantial challenge due to computational inefficiencies, scalability issues, and geometric inconsistencies. Existing methods typically neglect direct geometric integration, leading to poorly calibrated uncertainty maps. We introduce BayesSDF, a novel probabilistic framework for uncertainty quantification in neural implicit SDF models, motivated by scientific …

FrontendBench: A Benchmark for Evaluating LLMs on Front-End Development via Automatic Evaluation
Hongda Zhu, Yiwen Zhang, Bing Zhao, Jingzhe Ding, Siyao Liu, Tong Liu, Dandan Wang, Yanan Liu, Zhaojian Li
https://arxiv.org/abs/2506.13832

FrontendBench: A Benchmark for Evaluating LLMs on Front-End Development via Automatic Evaluation
Large Language Models (LLMs) have made significant strides in front-end code generation. However, existing benchmarks exhibit several critical limitations: many tasks are overly simplistic, test cases often lack rigor, and end-to-end validation is absent. These issues hinder the accurate assessment of model performance. To address these challenges, we present FrontendBench, a benchmark co-developed by humans and LLMs. FrontendBench categorizes tasks based on code functionality and incorporates …

DesignCoder: Hierarchy-Aware and Self-Correcting UI Code Generation with Large Language Models
Yunnong Chen, Shixian Ding, YingYing Zhang, Wenkai Chen, Jinzhou Du, Lingyun Sun, Liuqing Chen
https://arxiv.org/abs/2506.13663

DesignCoder: Hierarchy-Aware and Self-Correcting UI Code Generation with Large Language Models
Multimodal large language models (MLLMs) have streamlined front-end interface development by automating code generation. However, these models also introduce challenges in ensuring code quality. Existing approaches struggle to maintain both visual consistency and functional completeness in the generated components. Moreover, they lack mechanisms to assess the fidelity and correctness of the rendered pages. To address these issues, we propose DesignCoder, a novel hierarchical-aware and self-corr…