Tootfinder

A deep Search for Ethylene Glycol and Glycolonitrile in the V883 Ori #Protoplanetary Disk: https://arxiv.org/abs/2507.14905 (and two slightly older papers) -> The evolution of life may have its origins in outer space: https://www.mpia.de/news/science/2025-05-v881-ori - astronomers find signs of complex organic molecules (precursors to sugars and amino acids) in a planet-forming disc.

🇺🇦 #NowPlaying on KEXP's #MorningShow
Pretty Bitter:
🎵 Outer Heaven Dude Ranch
#PrettyBitter
https://prettybitter.bandcamp.com/track/outer-heaven-dude-ranch
https://open.spotify.com/track/32S4UTJzF0XzYwkWApK2TL

Wealthfront, which uses AI to build investment portfolios, confidentially files for a US IPO; it was valued at $1.4B in 2022 during a failed acquisition by UBS (Prakhar Srivastava/Reuters)
https://www.reuters.com/technology/wealthf

An Elementary Characterization of Bargmann Invariants
Sagar Silva Pratapsi, Jo\~ao Gouveia, Leonardo Novo, Ernesto F. Galv\~ao
https://arxiv.org/abs/2506.17132

An Elementary Characterization of Bargmann Invariants
Bargmann invariants, also known as multivariate traces of quantum states $\operatorname{Tr}(ρ_1 ρ_2 \cdots ρ_n)$, are unitary invariant quantities used to characterize weak values, Kirkwood-Dirac quasiprobabilities, out-of-time-order correlators (OTOCs), and geometric phases. Here we give a complete characterization of the set $B_n$ of complex values that $n$-th order invariants can take, resolving some recently proposed conjectures. We show that $B_n$ is equal to the range of invariants ari…

Fnally, there is the option to run Github dependabot on a cron job base <3
(released in April, I haven't seen it before)
https://github.blog/changelog/2025-04-22-dependabot-now-lets-you-schedule-update-frequenci…

Dependabot now lets you schedule update frequencies with cron expressions - GitHub Changelog
Dependabot users can now schedule custom update frequencies by using cron expressions in schedule.interval in the Dependabot configuration file. This enhances the predefined intervals (daily, weekly, and monthly) and provides…

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…

Temporally-Constrained Video Reasoning Segmentation and Automated Benchmark Construction
Yiqing Shen, Chenjia Li, Chenxiao Fan, Mathias Unberath
https://arxiv.org/abs/2507.16718

Temporally-Constrained Video Reasoning Segmentation and Automated Benchmark Construction
Conventional approaches to video segmentation are confined to predefined object categories and cannot identify out-of-vocabulary objects, let alone objects that are not identified explicitly but only referred to implicitly in complex text queries. This shortcoming limits the utility for video segmentation in complex and variable scenarios, where a closed set of object categories is difficult to define and where users may not know the exact object category that will appear in the video. Such sce…

EASE: Embodied Active Event Perception via Self-Supervised Energy Minimization
Zhou Chen, Sanjoy Kundu, Harsimran S. Baweja, Sathyanarayanan N. Aakur
https://arxiv.org/abs/2506.17516

EASE: Embodied Active Event Perception via Self-Supervised Energy Minimization
Active event perception, the ability to dynamically detect, track, and summarize events in real time, is essential for embodied intelligence in tasks such as human-AI collaboration, assistive robotics, and autonomous navigation. However, existing approaches often depend on predefined action spaces, annotated datasets, and extrinsic rewards, limiting their adaptability and scalability in dynamic, real-world scenarios. Inspired by cognitive theories of event perception and predictive coding, we p…

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…

A workflow for generating synthetic LiDAR datasets in simulation environments
Abhishek Phadke, Shakib Mahmud Dipto, Pratip Rana
https://arxiv.org/abs/2506.17378

A workflow for generating synthetic LiDAR datasets in simulation environments
This paper presents a simulation workflow for generating synthetic LiDAR datasets to support autonomous vehicle perception, robotics research, and sensor security analysis. Leveraging the CoppeliaSim simulation environment and its Python API, we integrate time-of-flight LiDAR, image sensors, and two dimensional scanners onto a simulated vehicle platform operating within an urban scenario. The workflow automates data capture, storage, and annotation across multiple formats (PCD, PLY, CSV), produ…