Tootfinder

RADAR: Benchmarking Language Models on Imperfect Tabular Data
Ken Gu, Zhihan Zhang, Kate Lin, Yuwei Zhang, Akshay Paruchuri, Hong Yu, Mehran Kazemi, Kumar Ayush, A. Ali Heydari, Maxwell A. Xu, Girish Narayanswamy, Yun Liu, Ming-Zher Poh, Yuzhe Yang, Mark Malhotra, Shwetak Patel, Hamid Palangi, Xuhai Xu, Daniel McDuff, Tim Althoff, Xin Liu
https://

RADAR: Benchmarking Language Models on Imperfect Tabular Data
Language models (LMs) are increasingly being deployed to perform autonomous data analyses. However, their data awareness -- the ability to recognize, reason over, and appropriately handle data artifacts such as missing values, outliers, and logical inconsistencies -- remains underexplored. These artifacts are especially common in real-world tabular data and, if mishandled, can significantly compromise the validity of analytical conclusions. To address this gap, we present RADAR, a benchmark for…

This https://arxiv.org/abs/2505.14759 has been replaced.
initial toot: https://mastoxiv.page/@arXiv_csSE_…

LEANCODE: Understanding Models Better for Code Simplification of Pre-trained Large Language Models
Large Language Models for code often entail significant computational complexity, which grows significantly with the length of the input code sequence. We propose LeanCode for code simplification to reduce training and prediction time, leveraging code contexts in utilizing attention scores to represent the tokens' importance. We advocate for the selective removal of tokens based on the average context-aware attention scores rather than average scores across all inputs. LeanCode uses the attenti…

This https://arxiv.org/abs/2506.05008 has been replaced.
initial toot: https://mastoxiv.page/@arXiv_csCV_…

Structure-Aware Radar-Camera Depth Estimation
Monocular depth estimation aims to determine the depth of each pixel from an RGB image captured by a monocular camera. The development of deep learning has significantly advanced this field by facilitating the learning of depth features from some well-annotated datasets \cite{Geiger_Lenz_Stiller_Urtasun_2013,silberman2012indoor}. Eigen \textit{et al.} \cite{eigen2014depth} first introduce a multi-scale fusion network for depth regression. Following this, subsequent improvements have come from r…

Reconstructing Laurent expansion of rational functions using p-adic numbers
Tianya Xia, Li Lin Yang
https://arxiv.org/abs/2506.08452 https://

Reconstructing Laurent expansion of rational functions using p-adic numbers
We propose a novel method for reconstructing Laurent expansion of rational functions using $p$-adic numbers. By evaluating the rational functions in $p$-adic fields rather than finite fields, it is possible to probe the expansion coefficients simultaneously, enabling their reconstruction from a single set of evaluations. Compared with the reconstruction of the full expression, constructing the Laurent expansion to the first few orders significantly reduces the required computational resources. …

Hardware Limitations and Optimization Approach in 1-Bit RIS Design at 28 GHz
Hossein Rezaei, Mehmet Emin Arslan, George Yammine, Niels Neumann, Norman Franchi
https://arxiv.org/abs/2506.08930

Hardware Limitations and Optimization Approach in 1-Bit RIS Design at 28 GHz
Reconfigurable intelligent surfaces (RIS) have emerged as a transformative technology for electromagnetic (EM) wave manipulation, offering unprecedented control over wave reflections compared to traditional metallic reflectors. By utilizing an array of tunable elements, RIS can steer and shape electromagnetic waves to enhance signal quality in wireless communication and radar systems. However, practical implementations face significant challenges due to hardware limitations and phase quantizati…

Exoplaneteers Keep Overestimating Sigma Significances
David Kipping, Bj\"orn Benneke
https://arxiv.org/abs/2506.05392 https://ar…

Exoplaneteers Keep Overestimating Sigma Significances
Astronomers, and in particular exoplaneteers, have a curious habit of expressing Bayes factors as frequentist sigma values. This is of course completely unnecessary and arguably rather ill-advised. Regardless, the practice is common - especially in the detection claims of chemical species within exoplanet atmospheres. The current canonical conversion strategy stems from a statistics paper from Sellke et al. (2001), who derived an upper bound on the Bayes factor between the test and null hypothe…

Multi-Distillation from Speech and Music Representation Models
Jui-Chiang Wei, Yi-Cheng Lin, Fabian Ritter-Gutierrez, Hung-yi Lee
https://arxiv.org/abs/2506.07237

Multi-Distillation from Speech and Music Representation Models
Real-world audio often mixes speech and music, yet models typically handle only one domain. This paper introduces a multi-teacher distillation framework that unifies speech and music models into a single one while significantly reducing model size. Our approach leverages the strengths of domain-specific teacher models, such as HuBERT for speech and MERT for music, and explores various strategies to balance both domains. Experiments across diverse tasks demonstrate that our model matches the per…

Mind the Gap: A Readability-Aware Metric for Test Code Complexity
Wendk\^uuni C. Ou\'edraogo, Yinghua Li, Xueqi Dang, Xin Zhou, Anil Koyuncu, Jacques Klein, David Lo, Tegawend\'e F. Bissyand\'e
https://arxiv.org/abs/2506.06764

Mind the Gap: A Readability-Aware Metric for Test Code Complexity
Automatically generated unit tests-from search-based tools like EvoSuite or LLMs-vary significantly in structure and readability. Yet most evaluations rely on metrics like Cyclomatic Complexity and Cognitive Complexity, designed for functional code rather than test code. Recent studies have shown that SonarSource's Cognitive Complexity metric assigns near-zero scores to LLM-generated tests, yet its behavior on EvoSuite-generated tests and its applicability to test-specific code structures remai…

So, completed the first flight with the new controls. They are _significantly_ different than the setup that I have for _Elite: Dangerous_ which isn't necessarily a bad thing. Once I managed to get used to the control scheme the flight was pretty smooth.
Unfortunately I'm gonna need to practice landing and using the rudder pedals a lot because the landing was one of the absolute worst yet. The passengers were not amused, but since we all lived they should be grateful.
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This https://arxiv.org/abs/2408.11978 has been replaced.
initial toot: https://mastoxiv.page/@arXiv_csRO_…

Optimized Kalman Filter based State Estimation and Height Control in Hopping Robots
Rotor-based hopping locomotion significantly improves efficiency and operation time as compared to purely flying systems; where most hopping robots use the liftoff states and an assumed ballistic trajectory to determine the hopping height. However, significant aerial phase force (e.g., thrust and drag) can invalidate this assumption and lead to poor estimation performance. To combat this issue, a group has implemented multiple sensors (active and passive optical, inertial, and contact) and sign…