Tootfinder

A look at life in online scam compounds run by Chinese gangs on the Myanmar-Thai border, with purported Thai immigration officials luring trafficking victims (Reuters)
https://www.reuters.com/graphics/SOUTHEASTASIA-SCAMS/mypmxwdwwvr/

They traveled to Thailand. They wound up cyber scam slaves in Myanmar.
A multibillion-dollar fraud industry has taken root across Southeast Asia, fueled partly by trafficking victims. In lawless zones along the Myanmar-Thai border, Chinese-run compounds force thousands to scam strangers online under threat of violence. Thailand has become a key transit hub, Reuters found.

Neural acoustic multipole splatting for room impulse response synthesis
Geonwoo Baek, Jung-Woo Choi
https://arxiv.org/abs/2509.17410 https://arxiv.org/pdf/…

Neural acoustic multipole splatting for room impulse response synthesis
Room Impulse Response (RIR) prediction at arbitrary receiver positions is essential for practical applications such as spatial audio rendering. We propose Neural Acoustic Multipole Splatting (NAMS), which synthesizes RIRs at unseen receiver positions by learning the positions of neural acoustic multipoles and predicting their emitted signals and directivities using a neural network. Representing sound fields through a combination of multipoles offers sufficient flexibility to express complex ac…

On the application of refractive index matching to study the buoyancy-driven motion of spheres
Jibu Tom Jose, Aviel Ben-Harosh, Omri Ram
https://arxiv.org/abs/2509.16384 https:/…

On the application of refractive index matching to study the buoyancy-driven motion of spheres
Refractive index matching (RIM) is a powerful tool for multiphase flow studies as it eliminates optical distortions and enables high-fidelity tomographic measurements near solid-fluid interfaces of freely moving solids in the flow. However, by improving the RIM and optical quality, the solids become effectively invisible, preventing direct identification of their location. To address this limitation, we develop a physics-informed detection framework that locates transparent spheres within time-…

Jointly Computation- and Communication-Efficient Distributed Learning
Xiaoxing Ren, Nicola Bastianello, Karl H. Johansson, Thomas Parisini
https://arxiv.org/abs/2508.15509 https…

Jointly Computation- and Communication-Efficient Distributed Learning
We address distributed learning problems over undirected networks. Specifically, we focus on designing a novel ADMM-based algorithm that is jointly computation- and communication-efficient. Our design guarantees computational efficiency by allowing agents to use stochastic gradients during local training. Moreover, communication efficiency is achieved as follows: i) the agents perform multiple training epochs between communication rounds, and ii) compressed transmissions are used. We prove exac…

From 5G RAN Queue Dynamics to Playback: A Performance Analysis for QUIC Video Streaming
Jashanjot Singh Sidhu, Jorge Ignacio Sandoval, Abdelhak Bentaleb, Sandra Cespedes
https://arxiv.org/abs/2508.15087

From 5G RAN Queue Dynamics to Playback: A Performance Analysis for QUIC Video Streaming
The rapid adoption of QUIC as a transport protocol has transformed content delivery by reducing latency, enhancing congestion control (CC), and enabling more efficient multiplexing. With the advent of 5G networks, which support ultra-low latency and high bandwidth, streaming high-resolution video at 4K and beyond has become increasingly viable. However, optimizing Quality of Experience (QoE) in mobile networks remains challenging due to the complex interactions among Adaptive Bit Rate (ABR) sch…

Towards Scalable and Interpretable Mobile App Risk Analysis via Large Language Models
Yu Yang, Zhenyuan Li, Xiandong Ran, Jiahao Liu, Jiahui Wang, Bo Yu, Shouling Ji
https://arxiv.org/abs/2508.15606

Towards Scalable and Interpretable Mobile App Risk Analysis via Large Language Models
Mobile application marketplaces are responsible for vetting apps to identify and mitigate security risks. Current vetting processes are labor-intensive, relying on manual analysis by security professionals aided by semi-automated tools. To address this inefficiency, we propose Mars, a system that leverages Large Language Models (LLMs) for automated risk identification and profiling. Mars is designed to concurrently analyze multiple applications across diverse risk categories with minimal human …

The Hidden Life of Stars: Embedded Beginnings to AGB Endings in the PHANGS-JWST Sample. I. Catalog of Mid-IR Sources
Hamid Hassani, Erik Rosolowsky, Adam K. Leroy, Karin Sandstrom, M\'ed\'eric Boquien, David A. Thilker, Bradley C. Whitmore, Gagandeep S. Anand, Ashley T. Barnes, Yixian Cao, Ryan Chown, Enrico Congiu, Daniel A. Dale, Oleg V. Egorov, Ivan Gerasimov, Kathryn Grasha, Remy Indebetouw, Janice C. Lee, Fu-Heng Liang, Daniel Maschmann, Sharon E. Meidt, Elias K. Oakes, Is…

The Hidden Life of Stars: Embedded Beginnings to AGB Endings in the PHANGS-JWST Sample. I. Catalog of Mid-IR Sources
We present a multiwavelength catalog of mid-infrared-selected compact sources in 19 nearby galaxies, combining JWST NIRCam/MIRI, HST UV-optical broadband, H$α$ narrow-band, and ALMA CO observations. We detect 24,945 compact sources at 21 $μ$m and 55,581 at 10 $μ$m. Artificial star tests show 50% completeness limits of $\sim$5 $μ$Jy for the 10 $μ$m catalog, and $\sim$24 $μ$Jy for the 21 $μ$m catalog. We find that 21 $μ$m compact sources contribute $\sim$20% of the total galaxy emission i…

Declarative Data Pipeline for Large Scale ML Services
Yunzhao Yang, Runhui Wang, Xuanqing Liu, Adit Krishnan, Yefan Tao, Yuqian Deng, Kuangyou Yao, Peiyuan Sun, Henrik Johnson, Aditi sinha, Davor Golac, Gerald Friedland, Usman Shakeel, Daryl Cooke, Joe Sullivan, Chris Kong
https://arxiv.org/abs/2508.15105

Declarative Data Pipeline for Large Scale ML Services
Modern distributed data processing systems face significant challenges in balancing system performance with code maintainability and developer productivity, particularly when integrating machine learning capabilities at scale. In large collaborative environments, these challenges are amplified by high communication overhead between teams and the complexity of coordinating development across multiple groups. This paper presents a novel "Declarative Data Pipeline" architecture that addresses thes…

Fundamental Mechanisms of Human Learning
Scott E. Allen, A. David Redish, Ren\'e F. Kizilcec
https://arxiv.org/abs/2509.17202 https://arxiv.org/pdf/250…

Fundamental Mechanisms of Human Learning
Learning underlies nearly all human behavior and is central to education and education reform. Although recent advances in neuroscience have revealed the fundamental structure of learning processes, these insights have yet to be integrated into research and practice. Specifically, neuroscience has found that decision-making is governed by a structured process of perception, action-selection, and execution, supported by multiple neural systems with distinct memory stores and learning mechanisms.…

The SOFIA Massive (SOMA) Radio Survey. II. Radio Emission from High-Luminosity Protostars
Francisco Sequeira-Murillo, Viviana Rosero, Joshua Marvil, Jonathan C. Tan, Ruben Fedriani, Yichen Zhang, Azia Robinson, Prasanta Gorai, Kei E. I. Tanaka, James M. De Buizer, Maria T. Beltr\'an, Ryan D. Boyden
https://arxiv.org/abs/2507…

The SOFIA Massive (SOMA) Radio Survey. II. Radio Emission from High-Luminosity Protostars
We present centimeter continuum observations of seven high luminosity massive protostars and their surroundings as part of the SOFIA Massive (SOMA) Star Formation Survey. With data from the Very Large Array and the Australia Telescope Compact Array, we analyze the spectral index, morphology and multiplicity of the detected radio sources. The high sensitivity and high resolution observations allow us to resolve many sources. We report thirteen new detections and two previously known detections t…