Tootfinder

Source: Fox has officially closed its upfront discussions, reaching $2B for core sports properties, excluding the World Cup, for the first time (Saleah Blancaflor/Adweek)
https://www.adweek.com/convergent-tv/fox-wraps-upfront-2-billion-sports-properties/…

Fox Wraps Upfront, Reaches $2 Billion for Core Sports Properties
Fox closed its upfront talks with double-digit volume growth across its portfolio.

A Few Fit Most: Improving Performance Portability of SGEMM on GPUs using Multi-Versioning
Robert Hochgraf (Rochester Institute of Technology), Sreepathi Pai (University of Rochester)
https://arxiv.org/abs/2507.15277

A Few Fit Most: Improving Performance Portability of SGEMM on GPUs using Multi-Versioning
Hand-optimizing linear algebra kernels for different GPU devices and applications is complex and labor-intensive. Instead, many developers use automatic performance tuning (autotuning) to achieve high performance on a variety of devices. However, autotuning "overfits", and must be redone if any part of the environment changes, such as if the device or input characteristics change. In most non-trivial cases, a single compute kernel cannot maintain near-optimal performance across all environmen…

Evaluating Ensemble and Deep Learning Models for Static Malware Detection with Dimensionality Reduction Using the EMBER Dataset
Md Min-Ha-Zul Abedin, Tazqia Mehrub
https://arxiv.org/abs/2507.16952

Evaluating Ensemble and Deep Learning Models for Static Malware Detection with Dimensionality Reduction Using the EMBER Dataset
This study investigates the effectiveness of several machine learning algorithms for static malware detection using the EMBER dataset, which contains feature representations of Portable Executable (PE) files. We evaluate eight classification models: LightGBM, XGBoost, CatBoost, Random Forest, Extra Trees, HistGradientBoosting, k-Nearest Neighbors (KNN), and TabNet, under three preprocessing settings: original feature space, Principal Component Analysis (PCA), and Linear Discriminant Analysis (L…

Pelosi raked in millions last year -- and her portfolio out-performed every large hedge fund with stunning returns (Gabrielle Fahmy/New York Post)
https://nypost.com/2025/06/21/us-news/pelosi-added-millions-to-net-worth-last-year-report/
http://www.memeorandum.com/250621/p25#a250621p25

LAMMPS-KOKKOS: Performance Portable Molecular Dynamics Across Exascale Architectures
Anders Johansson, Evan Weinberg, Christian R. Trott, Megan J. McCarthy, Stan G. Moore
https://arxiv.org/abs/2508.13523

LAMMPS-KOKKOS: Performance Portable Molecular Dynamics Across Exascale Architectures
Since its inception in 1995, LAMMPS has grown to be a world-class molecular dynamics code, with thousands of users, over one million lines of code, and multi-scale simulation capabilities. We discuss how LAMMPS has adapted to the modern heterogeneous computing landscape by integrating the Kokkos performance portability library into the existing C++ code. We investigate performance portability of simple pairwise, many-body reactive, and machine-learned force-field interatomic potentials. We pres…

Effects of Cognitive Distraction and Driving Environment Complexity on Adaptive Cruise Control Use and Its Impact on Driving Performance: A Simulator Study
Ana\"is Halin, Marc Van Droogenbroeck, Christel Devue
https://arxiv.org/abs/2507.13886

Effects of Cognitive Distraction and Driving Environment Complexity on Adaptive Cruise Control Use and Its Impact on Driving Performance: A Simulator Study
In this simulator study, we adopt a human-centered approach to explore whether and how drivers' cognitive state and driving environment complexity influence reliance on driving automation features. Besides, we examine whether such reliance affects driving performance. Participants operated a vehicle equipped with adaptive cruise control (ACC) in a simulator across six predefined driving scenarios varying in traffic conditions while either performing a cognitively demanding task (i.e., respondin…

Detect Any Sound: Open-Vocabulary Sound Event Detection with Multi-Modal Queries
Pengfei Cai, Yan Song, Qing Gu, Nan Jiang, Haoyu Song, Ian McLoughlin
https://arxiv.org/abs/2507.16343

Detect Any Sound: Open-Vocabulary Sound Event Detection with Multi-Modal Queries
Most existing sound event detection~(SED) algorithms operate under a closed-set assumption, restricting their detection capabilities to predefined classes. While recent efforts have explored language-driven zero-shot SED by exploiting audio-language models, their performance is still far from satisfactory due to the lack of fine-grained alignment and cross-modal feature fusion. In this work, we propose the Detect Any Sound Model (DASM), a query-based framework for open-vocabulary SED guided by …

Optimal Regulation and Investment Incentives in Financial Networks
Matthew O. Jackson, Agathe Pernoud
https://arxiv.org/abs/2506.16648 https://

Optimal Regulation and Investment Incentives in Financial Networks
We examine optimal regulation of financial networks with debt interdependencies between financial firms. We first characterize when it is firms have an incentive to choose excessively risky portfolios and overly correlate their portfolios with those of their counterparties. We then characterize how optimal regulation depends on a firm's financial centrality and its available investment opportunities. In standard core-periphery networks, optimal regulation depends non-monotonically on the correl…

Design and Implementation of a Lightweight Object Detection System for Resource-Constrained Edge Environments
Jiyue Jiang, Mingtong Chen, Zhengbao Yang
https://arxiv.org/abs/2507.16155

Design and Implementation of a Lightweight Object Detection System for Resource-Constrained Edge Environments
This project aims to develop a system to run the object detection model under low power consumption conditions. The detection scene is set as an outdoor traveling scene, and the detection categories include people and vehicles. In this system, users data does not need to be uploaded to the cloud, which is suitable for use in environments with portable needs and strict requirements for data privacy. The MCU device used in this system is STM32H7, which has better performance among low power devic…

WAMI: Compilation to WebAssembly through MLIR without Losing Abstraction
Byeongjee Kang, Harsh Desai, Limin Jia, Brandon Lucia
https://arxiv.org/abs/2506.16048

WAMI: Compilation to WebAssembly through MLIR without Losing Abstraction
WebAssembly (Wasm) is a portable bytecode format that serves as a compilation target for high-level languages, enabling their secure and efficient execution across diverse platforms, including web browsers and embedded systems. To improve support for high-level languages without incurring significant code size or performance overheads, Wasm continuously evolves by integrating high-level features such as Garbage Collection and Stack Switching. However, existing compilation approaches either lack…