Tootfinder

Static and Dynamical Characterization of Ground State Phases Induced by Frustration and Magnetic Field in the Spin-1 Orthogonal Dimer Chain
Ernest Ong, Dhiman Bhowmick, Sharoz Schezwen, Pinaki Sengupta
https://arxiv.org/abs/2507.21771

Static and Dynamical Characterization of Ground State Phases Induced by Frustration and Magnetic Field in the Spin-1 Orthogonal Dimer Chain
The spin-$1$ orthogonal dimer chain is investigated using the Density Matrix Renormalization Group (DMRG) algorithm. A transformation to a basis that uses the local eigenstates of the orthogonal dimers, while retaining the local spin states for the parallel spins, allows for more effective implementation of the symmetries, as well as mitigating the entanglement bias of DMRG. A rich ground state phase diagram is obtained in the parameter space spanned by the ratio of inter- to intra-dimer intera…

A note on blinded continuous monitoring for continuous outcomes
Long-Hao Xu, Tim Friede
https://arxiv.org/abs/2507.22202 https://arxiv.org/pdf/2507.22202…

A note on blinded continuous monitoring for continuous outcomes
Continuous monitoring is becoming more popular due to its significant benefits, including reducing sample sizes and reaching earlier conclusions. In general, it involves monitoring nuisance parameters (e.g., the variance of outcomes) until a specific condition is satisfied. The blinded method, which does not require revealing group assignments, was recommended because it maintains the integrity of the experiment and mitigates potential bias. Although Friede and Miller (2012) investigated the ch…

Trump signed an executive order decrying so-called anti-Christian bias in February,
and a hotline was created in April to encourage State Department workers to call in and snitch on their colleagues who might be acting with any kind of “anti-Christian bias.”
The Religious Liberty Commission was established via another executive order on May 1 during a flashy White House event.
Trump has even established his own Christian “faith” office in the White House, seemingly further…

Trump’s ‘anti-Christian bias’ taskforce reveals its propagandistic agenda in first meeting
Even though Christians are the dominant religious group in the country, Trump and his allies are desperate to portray persecution where there is none

Social Group Bias in AI Finance
Thomas R. Cook, Sophia Kazinnik
https://arxiv.org/abs/2506.17490 https://arxiv.org/pdf/2506.17490

Social Group Bias in AI Finance
Financial institutions increasingly rely on large language models (LLMs) for high-stakes decision-making. However, these models risk perpetuating harmful biases if deployed without careful oversight. This paper investigates racial bias in LLMs specifically through the lens of credit decision-making tasks, operating on the premise that biases identified here are indicative of broader concerns across financial applications. We introduce a reproducible, counterfactual testing framework that evalua…

Multi-Group Equivariant Augmentation for Reinforcement Learning in Robot Manipulation
Hongbin Lin, Juan Rojas, Kwok Wai Samuel Au
https://arxiv.org/abs/2508.11204 https://

Multi-Group Equivariant Augmentation for Reinforcement Learning in Robot Manipulation
Sampling efficiency is critical for deploying visuomotor learning in real-world robotic manipulation. While task symmetry has emerged as a promising inductive bias to improve efficiency, most prior work is limited to isometric symmetries -- applying the same group transformation to all task objects across all timesteps. In this work, we explore non-isometric symmetries, applying multiple independent group transformations across spatial and temporal dimensions to relax these constraints. We intr…

Fairness for the People, by the People: Minority Collective Action
Omri Ben-Dov, Samira Samadi, Amartya Sanyal, Alexandru \c{T}ifrea
https://arxiv.org/abs/2508.15374 https://

Fairness for the People, by the People: Minority Collective Action
Machine learning models often preserve biases present in training data, leading to unfair treatment of certain minority groups. Despite an array of existing firm-side bias mitigation techniques, they typically incur utility costs and require organizational buy-in. Recognizing that many models rely on user-contributed data, end-users can induce fairness through the framework of Algorithmic Collective Action, where a coordinated minority group strategically relabels its own data to enhance fairne…

This https://arxiv.org/abs/2506.01832 has been replaced.
initial toot: https://mastoxiv.page/@arXiv_csCC_…

Pseudorandom bits for non-commutative programs
We obtain new explicit pseudorandom generators for several computational models involving groups. Our main results are as follows: 1. We consider read-once group-products over a finite group $G$, i.e., tests of the form $\prod_{i=1}^n g_i^{x_i}$ where $g_i\in G$, a special case of read-once permutation branching programs. We give generators with optimal seed length $c_G \log(n/\varepsilon)$ over any $p$-group. The proof uses the small-bias plus noise paradigm, but derandomizes the noise to av…

FairASR: Fair Audio Contrastive Learning for Automatic Speech Recognition
Jongsuk Kim, Jaemyung Yu, Minchan Kwon, Junmo Kim
https://arxiv.org/abs/2506.10747

FairASR: Fair Audio Contrastive Learning for Automatic Speech Recognition
Large-scale ASR models have achieved remarkable gains in accuracy and robustness. However, fairness issues remain largely unaddressed despite their critical importance in real-world applications. In this work, we introduce FairASR, a system that mitigates demographic bias by learning representations that are uninformative about group membership, enabling fair generalization across demographic groups. Leveraging a multi-demographic dataset, our approach employs a gradient reversal layer to suppr…

Pseudorandom bits for non-commutative programs
Chin Ho Lee, Emanuele Viola
https://arxiv.org/abs/2506.01832 https://arxiv.org/pdf/250…

Pseudorandom bits for non-commutative programs
We obtain new explicit pseudorandom generators for several computational models involving groups. Our main results are as follows: 1. We consider read-once group-products over a finite group $G$, i.e., tests of the form $\prod_{i=1}^n g_i^{x_i}$ where $g_i\in G$, a special case of read-once permutation branching programs. We give generators with optimal seed length $c_G \log(n/\varepsilon)$ over any $p$-group. The proof uses the small-bias plus noise paradigm, but derandomizes the noise to av…