Tootfinder

A look at Tea, a woman-only safety app with 4M users that lets users anonymously assign red or green flags to local men, as it goes viral with 900K new signups (Angela Yang/NBC News)
https://www.nbcnews.com/tech/tech-news/women-are-an…

Women are anonymously spilling tea about men in their cities on viral app
When Cid Walker opens the Tea app, she’s greeted by a barrage of posts about men and their apparent "red" and "green" "flags."“He’s a cheater,” Walker said, reading some of the comments on one post out loud."What clubs does he go to?" another person asked on a different post.

Treasure Hunt in Anonymous Graphs with Quantum Pebbles by Oblivious Agents
Gaurav Gaur, Barun Gorain, Rishi Ranjan Singh, Daya Gaur
https://arxiv.org/abs/2509.02909 https://

Treasure Hunt in Anonymous Graphs with Quantum Pebbles by Oblivious Agents
We investigate the problem of finding a static treasure in anonymous graphs using oblivious agents and introduce a novel approach that leverages quantum information. In anonymous graphs, vertices are unlabelled, indistinguishable, and edges are locally labelled with port numbers. Agents typically rely on stationary classical pebbles placed by an oracle to guide their search. However, this classical approach is constrained by limited information transmission and high traversal complexity. Classi…

'Diva' Micah Parsons 'not well-liked' with Cowboys, anonymous AFC coach says https://www.si.com/nfl/cowboys/news/diva-micah-parsons-not-well-liked-dallas-cowboys-anonymous-afc-coach-says

Combining Performance and Productivity: Accelerating the Network Sensing Graph Challenge with GPUs and Commodity Data Science Software
Siddharth Samsi, Dan Campbell, Emanuel Scoullos, Oded Green
https://arxiv.org/abs/2509.03653

Combining Performance and Productivity: Accelerating the Network Sensing Graph Challenge with GPUs and Commodity Data Science Software
The HPEC Graph Challenge is a collection of benchmarks representing complex workloads that test the hardware and software components of HPC systems, which traditional benchmarks, such as LINPACK, do not. The first benchmark, Subgraph Isomorphism, focused on several compute-bound and memory-bound kernels. The most recent of the challenges, the Anonymized Network Sensing Graph Challenge, represents a shift in direction, as it represents a longer end-to-end workload that requires many more softwar…

LCS-CTC: Leveraging Soft Alignments to Enhance Phonetic Transcription Robustness
Zongli Ye, Jiachen Lian, Akshaj Gupta, Xuanru Zhou, Krish Patel, Haodong Li, Hwi Joo Park, Chenxu Guo, Shuhe Li, Sam Wang, Cheol Jun Cho, Zoe Ezzes, Jet M. J. Vonk, Brittany T. Morin, Rian Bogley, Lisa Wauters, Zachary A. Miller, Maria Luisa Gorno-Tempini, Gopala Anumanchipalli
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LCS-CTC: Leveraging Soft Alignments to Enhance Phonetic Transcription Robustness
Phonetic speech transcription is crucial for fine-grained linguistic analysis and downstream speech applications. While Connectionist Temporal Classification (CTC) is a widely used approach for such tasks due to its efficiency, it often falls short in recognition performance, especially under unclear and nonfluent speech. In this work, we propose LCS-CTC, a two-stage framework for phoneme-level speech recognition that combines a similarity-aware local alignment algorithm with a constrained CTC …

SynBench: A Benchmark for Differentially Private Text Generation
Yidan Sun, Viktor Schlegel, Srinivasan Nandakumar, Iqra Zahid, Yuping Wu, Yulong Wu, Hao Li, Jie Zhang, Warren Del-Pinto, Goran Nenadic, Siew Kei Lam, Anil Anthony Bharath
https://arxiv.org/abs/2509.14594

SynBench: A Benchmark for Differentially Private Text Generation
Data-driven decision support in high-stakes domains like healthcare and finance faces significant barriers to data sharing due to regulatory, institutional, and privacy concerns. While recent generative AI models, such as large language models, have shown impressive performance in open-domain tasks, their adoption in sensitive environments remains limited by unpredictable behaviors and insufficient privacy-preserving datasets for benchmarking. Existing anonymization methods are often inadequate…

Towards Accurate Phonetic Error Detection Through Phoneme Similarity Modeling
Xuanru Zhou, Jiachen Lian, Cheol Jun Cho, Tejas Prabhune, Shuhe Li, William Li, Rodrigo Ortiz, Zoe Ezzes, Jet Vonk, Brittany Morin, Rian Bogley, Lisa Wauters, Zachary Miller, Maria Gorno-Tempini, Gopala Anumanchipalli
https://arxiv.org/abs/2507.14346…

Towards Accurate Phonetic Error Detection Through Phoneme Similarity Modeling
Phonetic error detection, a core subtask of automatic pronunciation assessment, identifies pronunciation deviations at the phoneme level. Speech variability from accents and dysfluencies challenges accurate phoneme recognition, with current models failing to capture these discrepancies effectively. We propose a verbatim phoneme recognition framework using multi-task training with novel phoneme similarity modeling that transcribes what speakers actually say rather than what they're supposed to s…