Tootfinder

Series A, Episode 02 - Space Fall
LEYLAN: Yes.
BLAKE: These are mine. You will hand over all your weapons to my men. Whilst we hold the computer, the ship is helpless. It will remain that way until you agree. You will then fly this ship to the nearest habitable planet where the prisoners will disembark. Any attempt by your men to break into this room and we'll destroy the computer. Totally. That's all.

Identifying Key Features for Establishing Sustainable Agro-Tourism Centre: A Data Driven Approach
Alka Gadakh, Vidya Kumbhar, Sonal Khosla, Kumar Karunendra
https://arxiv.org/abs/2509.09214

Identifying Key Features for Establishing Sustainable Agro-Tourism Centre: A Data Driven Approach
Agro-tourism serves as a strategic economic model designed to facilitate rural development by diversifying income streams for local communities like farmers while promoting the conservation of indigenous cultural heritage and traditional agricultural practices. As a very booming subdomain of tourism, there is a need to study the strategies for the growth of Agro-tourism in detail. The current study has identified the important indicators for the growth and enhancement of agro-tourism. The study…

Moody Urbanity - Relations III 🧶
情绪化城市 - 关系 III 🧶
📷 Minolta Hi-Matic AF
🎞️ Shanghai GP3 400 Pan
#filmphotography #Photography #blackandwhite

Invisible Attributes, Visible Biases: Exploring Demographic Shortcuts in MRI-based Alzheimer's Disease Classification
Akshit Achara, Esther Puyol Anton, Alexander Hammers, Andrew P. King
https://arxiv.org/abs/2509.09558

Invisible Attributes, Visible Biases: Exploring Demographic Shortcuts in MRI-based Alzheimer's Disease Classification
Magnetic resonance imaging (MRI) is the gold standard for brain imaging. Deep learning (DL) algorithms have been proposed to aid in the diagnosis of diseases such as Alzheimer's disease (AD) from MRI scans. However, DL algorithms can suffer from shortcut learning, in which spurious features, not directly related to the output label, are used for prediction. When these features are related to protected attributes, they can lead to performance bias against underrepresented protected groups, such …

User Exploration and Exploitation Behavior Under the Influence of Real-time Interactions in Live Streaming Environments
Akira Matsui, Kazuki Fujikawa, Ryo Sasaki, Ryo Adachi
https://arxiv.org/abs/2509.09138

User Exploration and Exploitation Behavior Under the Influence of Real-time Interactions in Live Streaming Environments
Live streaming platforms offer a distinctive way for users and content creators to interact with each other through real-time communication. While research on user behavior in online platforms has explored how users discover their favorite content from creators and engage with them, the role of real-time features remains unclear. There are open questions as to what commonalities and differences exist in users' relationships with live streaming platforms compared to traditional on-demand style p…

Articulation-Informed ASR: Integrating Articulatory Features into ASR via Auxiliary Speech Inversion and Cross-Attention Fusion
Ahmed Adel Attia, Jing Liu, Carol Espy Wilson
https://arxiv.org/abs/2510.08585

Articulation-Informed ASR: Integrating Articulatory Features into ASR via Auxiliary Speech Inversion and Cross-Attention Fusion
Prior works have investigated the use of articulatory features as complementary representations for automatic speech recognition (ASR), but their use was largely confined to shallow acoustic models. In this work, we revisit articulatory information in the era of deep learning and propose a framework that leverages articulatory representations both as an auxiliary task and as a pseudo-input to the recognition model. Specifically, we employ speech inversion as an auxiliary prediction task, and th…

Content Moderation Futures
Lindsay Blackwell
https://arxiv.org/abs/2509.09076 https://arxiv.org/pdf/2509.09076

Content Moderation Futures
This study examines the failures and possibilities of contemporary social media governance through the lived experiences of various content moderation professionals. Drawing on participatory design workshops with 33 practitioners in both the technology industry and broader civil society, this research identifies significant structural misalignments between corporate incentives and public interests. While experts agree that successful content moderation is principled, consistent, contextual, pro…

Listening or Reading? Evaluating Speech Awareness in Chain-of-Thought Speech-to-Text Translation
Jacobo Romero-D\'iaz, Gerard I. G\'allego, Oriol Pareras, Federico Costa, Javier Hernando, Cristina Espa\~na-Bonet
https://arxiv.org/abs/2510.03115

Listening or Reading? Evaluating Speech Awareness in Chain-of-Thought Speech-to-Text Translation
Speech-to-Text Translation (S2TT) systems built from Automatic Speech Recognition (ASR) and Text-to-Text Translation (T2TT) modules face two major limitations: error propagation and the inability to exploit prosodic or other acoustic cues. Chain-of-Thought (CoT) prompting has recently been introduced, with the expectation that jointly accessing speech and transcription will overcome these issues. Analyzing CoT through attribution methods, robustness evaluations with corrupted transcripts, and p…

Signal Fidelity Index-Aware Calibration for Dementia Predictions Across Heterogeneous Real-World Data
Jingya Cheng, Jiazi Tian, Federica Spoto, Alaleh Azhir, Daniel Mork, Hossein Estiri
https://arxiv.org/abs/2509.08679

Signal Fidelity Index-Aware Calibration for Dementia Predictions Across Heterogeneous Real-World Data
\textbf{Background:} Machine learning models trained on electronic health records (EHRs) often degrade across healthcare systems due to distributional shift. A fundamental but underexplored factor is diagnostic signal decay: variability in diagnostic quality and consistency across institutions, which affects the reliability of codes used for training and prediction. \textbf{Objective:} To develop a Signal Fidelity Index (SFI) quantifying diagnostic data quality at the patient level in dementi…

Revisiting Direct Speech-to-Text Translation with Speech LLMs: Better Scaling than CoT Prompting?
Oriol Pareras, Gerard I. G\'allego, Federico Costa, Cristina Espa\~na-Bonet, Javier Hernando
https://arxiv.org/abs/2510.03093

Revisiting Direct Speech-to-Text Translation with Speech LLMs: Better Scaling than CoT Prompting?
Recent work on Speech-to-Text Translation (S2TT) has focused on LLM-based models, introducing the increasingly adopted Chain-of-Thought (CoT) prompting, where the model is guided to first transcribe the speech and then translate it. CoT typically outperforms direct prompting primarily because it can exploit abundant Automatic Speech Recognition (ASR) and Text-to-Text Translation (T2TT) datasets to explicitly model its steps. In this paper, we systematically compare CoT and Direct prompting unde…