Tootfinder

Just extend an invite for Palestine to participate. Everyone is a winner (probably Palestine 🇵🇸)
https://flipboard.com/@huffpostuk/entertainment-fg1pgso7z/-/a-yi3kitMkTGi7vqcmJV44Ng:a:878833414-/0

Germany's Broadcaster Responds To Reports They'll Leave Eurovision If Israel Is Banned
Israel's involvement in the Eurovision Song Contest continues to be a controversial issue. Germany’s national broadcaster has responded to claims that …

Two-Stage Swarm Intelligence Ensemble Deep Transfer Learning (SI-EDTL) for Vehicle Detection Using Unmanned Aerial Vehicles
Zeinab Ghasemi Darehnaei, Mohammad Shokouhifar, Hossein Yazdanjouei, S. M. J. Rastegar Fatemi
https://arxiv.org/abs/2509.08026

Two-Stage Swarm Intelligence Ensemble Deep Transfer Learning (SI-EDTL) for Vehicle Detection Using Unmanned Aerial Vehicles
This paper introduces SI-EDTL, a two-stage swarm intelligence ensemble deep transfer learning model for detecting multiple vehicles in UAV images. It combines three pre-trained Faster R-CNN feature extractor models (InceptionV3, ResNet50, GoogLeNet) with five transfer classifiers (KNN, SVM, MLP, C4.5, Naïve Bayes), resulting in 15 different base learners. These are aggregated via weighted averaging to classify regions as Car, Van, Truck, Bus, or background. Hyperparameters are optimized with t…

Identifying Present-Biased Discount Functions in Dynamic Discrete Choice Models
Jaap H. Abbring, {\O}ystein Daljord, Fedor Iskhakov
https://arxiv.org/abs/2507.07286

Identifying Present-Biased Discount Functions in Dynamic Discrete Choice Models
We study the identification of dynamic discrete choice models with sophisticated, quasi-hyperbolic time preferences under exclusion restrictions. We consider both standard finite horizon problems and empirically useful infinite horizon ones, which we prove to always have solutions. We reduce identification to finding the present-bias and standard discount factors that solve a system of polynomial equations with coefficients determined by the data and use this to bound the cardinality of the ide…

🇺🇦 #NowPlaying on BBCRadio3's #NewMusicShow
Claire M Singer & Claire M Singer:
🎵 Forrig
#ClaireMSinger
https://open.spotify.com/track/1y9eUIKz8th3un3taU9D4M
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Der #Stadthafen von #Ueckermünde bei #Sonnenuntergang. Ausgehend von Ueckermünde kann man auch super

This was always how it was going to end, just a financial settlement and AI companies can continue.
https://www.nytimes.com/2025/09/05/technology/anth…

Anthropic Agrees to Pay $1.5 Billion to Settle Lawsuit With Book Authors
The settlement is the largest payout in the history of U.S. copyright cases and could lead more A.I. companies to pay rights holders for use of their works.

🇺🇦 #NowPlaying on KEXP's #MiddayShow
Tres Leches:
🎵 No Llores
#TresLeches
https://tresleches.bandcamp.com/track/no-llores-2
https://open.spotify.com/track/3YjyTnWG4yHcCUwpB0zFe4

🇺🇦 #NowPlaying on BBCRadio3's #NightTracks
Yijia Tu:
🎵 Red Mountain Flowers
#YijiaTu
https://open.spotify.com/track/7BpWxEVYkdn0HMZpOa8F2y

CTA: Cross-Task Alignment for Better Test Time Training
Samuel Barbeau, Pedram Fekri, David Osowiechi, Ali Bahri, Moslem YazdanpanahMasih Aminbeidokhti, Christian Desrosiers
https://arxiv.org/abs/2507.05221

CTA: Cross-Task Alignment for Better Test Time Training
Deep learning models have demonstrated exceptional performance across a wide range of computer vision tasks. However, their performance often degrades significantly when faced with distribution shifts, such as domain or dataset changes. Test-Time Training (TTT) has emerged as an effective method to enhance model robustness by incorporating an auxiliary unsupervised task during training and leveraging it for model updates at test time. In this work, we introduce CTA (Cross-Task Alignment), a nov…

🇺🇦 #NowPlaying on BBCRadio3's #EssentialClassics
Alexander Scriabin & Yevgeny Sudbin:
🎵 12 Études, Op. 8: No. 4 in B Major (Piacevole)
#AlexanderScriabin #YevgenySudbin
https://open.spotify.com/track/1berWWa7YyaZYt8nVZxIxq
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