Tootfinder

Marlee Matlin documentary "Not Alone Anymore"
‘It’s hard to find work’: Marlee Matlin on making Hollywood history but waiting for change
https://www.theguardian.com/film/2025/jun/21/marlee-matlin-documentary-not-alone-anymore

🧢 Modeling the World in 280 Characters
#graphics

Modeling the World in 280 Characters | Codrops
An exploration of the mindset, methods, and motivations behind crafting tiny, expressive shaders that combine code, art, and constraint.

🇺🇦 #NowPlaying on KEXP's #AfternoonShow
Midland:
🎵 Final Credits
#Midland
https://syndicatemusic1.bandcamp.com/track/midland-final-credits-italobros-edit
https://open.spotify.com/track/6b5IJEVYFEZ1YkgZ0oXaJK

Wide Binaries from GAIA DR3 - testing #GR vs MOND with realistic triple modelling: https://astro.theoj.org/article/142887-wide-binaries-from-gaia-dr3-testing-gr-vs-mond-with-realistic-triple-modelling -> "We find as before that Newtonian models provide a significantly better fit than MOND, though improved understanding of the triple population is necessary to make this fully decisive."

ECHO: Ego-Centric modeling of Human-Object interactions
Ilya A. Petrov, Vladimir Guzov, Riccardo Marin, Emre Aksan, Xu Chen, Daniel Cremers, Thabo Beeler, Gerard Pons-Moll
https://arxiv.org/abs/2508.21556

ECHO: Ego-Centric modeling of Human-Object interactions
Modeling human-object interactions (HOI) from an egocentric perspective is a largely unexplored yet important problem due to the increasing adoption of wearable devices, such as smart glasses and watches. We investigate how much information about interaction can be recovered from only head and wrists tracking. Our answer is ECHO (Ego-Centric modeling of Human-Object interactions), which, for the first time, proposes a unified framework to recover three modalities: human pose, object motion, and…

Bericht: Einsteiger-Stromer von Hyundai bekommt offenbar CarPlay Ultra
Der Ioniq 3 könnte das zweite Fahrzeug neben Modellen von Aston Martin sein, die Apples deutlich engere iPhone-Fahrzeugintegration erhalten.

PMODE: Theoretically Grounded and Modular Mixture Modeling
Robert A. Vandermeulen
https://arxiv.org/abs/2508.21396 https://arxiv.org/pdf/2508.21396

PMODE: Theoretically Grounded and Modular Mixture Modeling
We introduce PMODE (Partitioned Mixture Of Density Estimators), a general and modular framework for mixture modeling with both parametric and nonparametric components. PMODE builds mixtures by partitioning the data and fitting separate estimators to each subset. It attains near-optimal rates for this estimator class and remains valid even when the mixture components come from different distribution families. As an application, we develop MV-PMODE, which scales a previously theoretical approach …

Performance of LLMs on Stochastic Modeling Operations Research Problems: From Theory to Practice
Akshit Kumar, Tianyi Peng, Yuhang Wu, Assaf Zeevi
https://arxiv.org/abs/2506.23924

Performance of LLMs on Stochastic Modeling Operations Research Problems: From Theory to Practice
Large language models (LLMs) have exhibited expert-level capabilities across various domains. However, their abilities to solve problems in Operations Research (OR) -- the analysis and optimization of mathematical models derived from real-world problems or their verbal descriptions -- remain underexplored. In this work, we take a first step toward evaluating LLMs' abilities to solve stochastic modeling problems, a core class of OR problems characterized by uncertainty and typically involving to…

Modeling Data Diversity for Joint Instance and Verbalizer Selection in Cold-Start Scenarios
Mohna Chakraborty, Adithya Kulkarni, Qi Li
https://arxiv.org/abs/2507.00330

Modeling Data Diversity for Joint Instance and Verbalizer Selection in Cold-Start Scenarios
Prompt-based methods leverage the knowledge of pre-trained language models (PLMs) trained with a masked language modeling (MLM) objective; however, these methods are sensitive to template, verbalizer, and few-shot instance selection, particularly in cold-start settings with no labeled data. Existing studies overlook the dependency between instances and verbalizers, where instance-label probabilities depend on verbalizer token proximity in the embedding space. To address this, we propose COLDSEL…

Capsule Network-Based Semantic Intent Modeling for Human-Computer Interaction
Shixiao Wang, Yifan Zhuang, Runsheng Zhang, Zhijun Song
https://arxiv.org/abs/2507.00540

Capsule Network-Based Semantic Intent Modeling for Human-Computer Interaction
This paper proposes a user semantic intent modeling algorithm based on Capsule Networks to address the problem of insufficient accuracy in intent recognition for human-computer interaction. The method represents semantic features in input text through a vectorized capsule structure. It uses a dynamic routing mechanism to transfer information across multiple capsule layers. This helps capture hierarchical relationships and part-whole structures between semantic entities more effectively. The mod…