Tootfinder

Discovery of H$\alpha$ Emission from a Protoplanet Candidate Around the Young Star 2MASS J16120668-3010270 with MagAO-X
Jialin Li, Laird M. Close, Feng Long, Jared R. Males, Sebastiaan Y. Haffert, Alycia Weinberger, Katherine Follette, Sean Andrews, John Carpenter, Warren B. Foster, Kyle Van Gorkom, Alexander D. Hedglen, Gregory J. Herczeg, Parker T. Johnson, Maggie Y. Kautz, Jay K. Kueny, Rixin Li, Joshua Liberman, Joseph D. Long, Jennifer Lumbres, Sebastian Marino, Luca Matr`a, Eden …

Discovery of H$α$ Emission from a Protoplanet Candidate Around the Young Star 2MASS J16120668-3010270 with MagAO-X
2MASS J16120668-3010270 (hereafter 2MJ1612) is a young M0 star that hosts a protoplanetary disk in the Upper Scorpious star-forming region. Recent ALMA observations of 2MJ1612 show a mildly inclined disk ($i$=37$^\circ$) with a large dust-depleted gap (R$_\text{cav}\approx$0.4" or 53 au). We present high-contrast H$α$ observations from MagAO-X on the 6.5m Magellan Telescope and new high resolution sub-mm dust continuum observations with ALMA of 2MJ1612. On both 2025 April 13 and 16, we recover…

An Attention-Based Stochastic Simulator for Multisite Extremes to Evaluate Nonstationary, Cascading Flood Risk
Adam Nayak, Pierre Gentine, Upmanu Lall
https://arxiv.org/abs/2509.14162

An Attention-Based Stochastic Simulator for Multisite Extremes to Evaluate Nonstationary, Cascading Flood Risk
Compound flood risks from spatially and temporally clustered extremes challenge traditional risk models and insurance portfolios that often neglect correlated risks across regions. Spatiotemporally clustered floods exhibit fat-tail behavior, modulated by low-frequency hydroclimatic variability and large-scale moisture transport. Nonstationary stochastic simulators and regional compound event models aim to capture such tail risk, but have not yet unified spatial and temporal extremes under low-f…

From Chaos to Automation: Enabling the Use of Unstructured Data for Robotic Process Automation
Kelly Kurowski, Xixi Lu, Hajo A. Reijers
https://arxiv.org/abs/2507.11364

From Chaos to Automation: Enabling the Use of Unstructured Data for Robotic Process Automation
The growing volume of unstructured data within organizations poses significant challenges for data analysis and process automation. Unstructured data, which lacks a predefined format, encompasses various forms such as emails, reports, and scans. It is estimated to constitute approximately 80% of enterprise data. Despite the valuable insights it can offer, extracting meaningful information from unstructured data is more complex compared to structured data. Robotic Process Automation (RPA) has ga…

Image-Based Multi-Survey Classification of Light Curves with a Pre-Trained Vision Transformer
Daniel Moreno-Cartagena, Guillermo Cabrera-Vives, Alejandra M. Mu\~noz Arancibia, Pavlos Protopapas, Francisco F\"orster, M\'arcio Catelan, A. Bayo, Pablo A. Est\'evez, P. S\'anchez-S\'aez, Franz E. Bauer, M. Pavez-Herrera, L. Hern\'andez-Garc\'ia, Gonzalo Rojas

Image-Based Multi-Survey Classification of Light Curves with a Pre-Trained Vision Transformer
We explore the use of Swin Transformer V2, a pre-trained vision Transformer, for photometric classification in a multi-survey setting by leveraging light curves from the Zwicky Transient Facility (ZTF) and the Asteroid Terrestrial-impact Last Alert System (ATLAS). We evaluate different strategies for integrating data from these surveys and find that a multi-survey architecture which processes them jointly achieves the best performance. These results highlight the importance of modeling survey-s…

Data Synchronization at High Frequencies
Xinbing Kong, Cheng Liu, Bin Wu
https://arxiv.org/abs/2507.12220 https://arxiv.org/pdf/2507.…

Data Synchronization at High Frequencies
Asynchronous trading in high-frequency financial markets introduces significant biases into econometric analysis, distorting risk estimates and leading to suboptimal portfolio decisions. Existing synchronization methods, such as the previous-tick approach, suffer from information loss and create artificial price staleness. We introduce a novel framework that recasts the data synchronization challenge as a constrained matrix completion problem. Our approach recovers the potential matrix of high-…

Arbitrary high-fidelity binomial codes from multiphoton spin-boson interactions
Pradip Laha, Peter van Loock
https://arxiv.org/abs/2507.08585 https://

Arbitrary high-fidelity binomial codes from multiphoton spin-boson interactions
Encoding a qubit in the continuous degrees of freedom of a quantum system, such as bosonic modes, is a powerful alternative to modern quantum error correction (QEC). Among the most prominent bosonic QEC codes, binomial codes provide protection against loss and dephasing errors by encoding logical states in superpositions of Fock states with binomially weighted coefficients. While much attention has been given to their error-correcting capabilities and integration into fault-tolerant architectur…

Chemical transformation of CO in evolving protoplanetary discs across stellar masses: a route to C-rich inner regions
Andrew D. Sellek, Ewine F. van Dishoeck
https://arxiv.org/abs/2507.11631

Chemical transformation of CO in evolving protoplanetary discs across stellar masses: a route to C-rich inner regions
Protoplanetary discs around Very Low Mass Stars (VLMS) show hydrocarbon-rich MIR spectra indicative of C/O>1 in their inner discs, in contrast to discs around higher-mass hosts which mostly show O-bearing species. One scenario proposed to elevate C/O in VLMS inner discs is the advection of O-depleted gas from the outer disc. However, if CO gas remains abundant, C/O can be at most ~1. We test if chemical transformation of CO into other species allows this transport scenario to produce C/O signif…

FastTrack: a fast method to evaluate mass transport in solid leveraging universal machine learning interatomic potential
Hanwen Kang, Tenglong Lu, Zhanbin Qi, Jiandong Guo, Sheng Meng, Miao Liu
https://arxiv.org/abs/2508.10505

FastTrack: a fast method to evaluate mass transport in solid leveraging universal machine learning interatomic potential
We introduce a rapid, accurate framework for computing atomic migration barriers in crystals by combining universal machine learning force fields (MLFFs) with 3D potential energy surface sampling and interpolation. Our method suppresses periodic self interactions via supercell expansion, builds a continuous PES from MLFF energies on a spatial grid, and extracts minimum energy pathways without predefined NEB images. Across twelve benchmark electrode and electrolyte materials including LiCoO2, Li…

Integrating Rules and Semantics for LLM-Based C-to-Rust Translation
Feng Luo, Kexing Ji, Cuiyun Gao, Shuzheng Gao, Jia Feng, Kui Liu, Xin Xia, Michael R. Lyu
https://arxiv.org/abs/2508.06926

Integrating Rules and Semantics for LLM-Based C-to-Rust Translation
Automated translation of legacy C code into Rust aims to ensure memory safety while reducing the burden of manual migration. Early approaches in code translation rely on static rule-based methods, but they suffer from limited coverage due to dependence on predefined rule patterns. Recent works regard the task as a sequence-to-sequence problem by leveraging large language models (LLMs). Although these LLM-based methods are capable of reducing unsafe code blocks, the translated code often exhibit…

Multi-Faceted Large Embedding Tables for Pinterest Ads Ranking
Runze Su, Jiayin Jin, Jiacheng Li, Sihan Wang, Guangtong Bai, Zelun Wang, Li Tang, Yixiong Meng, Huasen Wu, Zhimeng Pan, Kungang Li, Han Sun, Zhifang Liu, Haoyang Li, Siping Ji, Ling Leng, Prathibha Deshikachar
https://arxiv.org/abs/2508.05700

Multi-Faceted Large Embedding Tables for Pinterest Ads Ranking
Large embedding tables are indispensable in modern recommendation systems, thanks to their ability to effectively capture and memorize intricate details of interactions among diverse entities. As we explore integrating large embedding tables into Pinterest's ads ranking models, we encountered not only common challenges such as sparsity and scalability, but also several obstacles unique to our context. Notably, our initial attempts to train large embedding tables from scratch resulted in neutral…