Tootfinder

TransLLM: A Unified Multi-Task Foundation Framework for Urban Transportation via Learnable Prompting
Jiaming Leng, Yunying Bi, Chuan Qin, Bing Yin, Yanyong Zhang, Chao Wang
https://arxiv.org/abs/2508.14782

TransLLM: A Unified Multi-Task Foundation Framework for Urban Transportation via Learnable Prompting
Urban transportation systems encounter diverse challenges across multiple tasks, such as traffic forecasting, electric vehicle (EV) charging demand prediction, and taxi dispatch. Existing approaches suffer from two key limitations: small-scale deep learning models are task-specific and data-hungry, limiting their generalizability across diverse scenarios, while large language models (LLMs), despite offering flexibility through natural language interfaces, struggle with structured spatiotemporal…

KI-Update Deep-Dive: KI als persönlicher Tutor im Klassenzimmer
Der Wiener Lehrer Bernhard Gmeiner plädiert für einen produktiven Umgang mit KI und für eine neue Prüfungskultur, die den Lernprozess in den Mittelpunkt rückt.

Calibrated Semantic Diffusion: A p-Laplacian Synthesis with Learnable Dissipation, Quantified Constants, and Graph-Aware Calibration
Faruk Alpay, Hamdi Alakkad
https://arxiv.org/abs/2508.13658

Calibrated Semantic Diffusion: A p-Laplacian Synthesis with Learnable Dissipation, Quantified Constants, and Graph-Aware Calibration
We develop a calibrated diffusion framework by synthesizing three established concepts: linear Laplacian smoothing, nonlinear graph p-Laplacian flows, and a learnable dissipation term derived from a strongly convex potential. This synthesis provides a general model for graph-based diffusion with controllable dynamics. Our key theoretical results include a quantified two-regime decay analysis for $p>2$, which provides stronger, p-dependent transient bounds not captured by standard ISS templates,…

Replaced article(s) found for cs.RO. https://arxiv.org/list/cs.RO/new
[1/1]:
- Towards Interactive and Learnable Cooperative Driving Automation: a Large Language Model-Driven D...
Shiyu Fang, Jiaqi Liu, Mingyu Ding, Yiming Cui, Chen Lv, Peng Hang, Jian Sun

Deep Equilibrium Convolutional Sparse Coding for Hyperspectral Image Denoising
Jin Ye, Jingran Wang, Fengchao Xiong, Jingzhou Chen, Yuntao Qian
https://arxiv.org/abs/2508.15553 …

Deep Equilibrium Convolutional Sparse Coding for Hyperspectral Image Denoising
Hyperspectral images (HSIs) play a crucial role in remote sensing but are often degraded by complex noise patterns. Ensuring the physical property of the denoised HSIs is vital for robust HSI denoising, giving the rise of deep unfolding-based methods. However, these methods map the optimization of a physical model to a learnable network with a predefined depth, which lacks convergence guarantees. In contrast, Deep Equilibrium (DEQ) models treat the hidden layers of deep networks as the solution…

GDNSQ: Gradual Differentiable Noise Scale Quantization for Low-bit Neural Networks
Sergey Salishev, Ian Akhremchik
https://arxiv.org/abs/2508.14004 https://

GDNSQ: Gradual Differentiable Noise Scale Quantization for Low-bit Neural Networks
Quantized neural networks can be viewed as a chain of noisy channels, where rounding in each layer reduces capacity as bit-width shrinks; the floating-point (FP) checkpoint sets the maximum input rate. We track capacity dynamics as the average bit-width decreases and identify resulting quantization bottlenecks by casting fine-tuning as a smooth, constrained optimization problem. Our approach employs a fully differentiable Straight-Through Estimator (STE) with learnable bit-width, noise scale an…

ViT-FIQA: Assessing Face Image Quality using Vision Transformers
Andrea Atzori, Fadi Boutros, Naser Damer
https://arxiv.org/abs/2508.13957 https://arxiv.or…

ViT-FIQA: Assessing Face Image Quality using Vision Transformers
Face Image Quality Assessment (FIQA) aims to predict the utility of a face image for face recognition (FR) systems. State-of-the-art FIQA methods mainly rely on convolutional neural networks (CNNs), leaving the potential of Vision Transformer (ViT) architectures underexplored. This work proposes ViT-FIQA, a novel approach that extends standard ViT backbones, originally optimized for FR, through a learnable quality token designed to predict a scalar utility score for any given face image. The le…

🇺🇦 #NowPlaying on BBCRadio3's #EssentialClassics
Laura Mvula, Pembroke College Girls' Choir, Pembroke College Girls' Choir & Anna Lapwood:
🎵 Sing to the Moon
#LauraMvula #PembrokeCollegeGirlsChoir #AnnaLapwood

Note to self:
When you install a dozen HDDs in a server with drive trays without indicators, it might be a good idea to write down which drive with which serial number is installed in what slot.
I just had to unscrew 5 of 14 HDDs to find the right one to remove!!
I normally like my Fractal Design Define 7 XL, but today... 🤬
#learnfrommymistakes

Task-Agnostic Learnable Weighted-Knowledge Base Scheme for Robust Semantic Communications
Shiyao Jiang, Jian Jiao, Xingjian Zhang, Ye Wang, Dusit Niyato, Qinyu Zhang
https://arxiv.org/abs/2509.11636

Task-Agnostic Learnable Weighted-Knowledge Base Scheme for Robust Semantic Communications
With the emergence of diverse and massive data in the upcoming sixth-generation (6G) networks, the task-agnostic semantic communication system is regarded to provide robust intelligent services. In this paper, we propose a task-agnostic learnable weighted-knowledge base semantic communication (TALSC) framework for robust image transmission to address the real-world heterogeneous data bias in KB, including label flipping noise and class imbalance. The TALSC framework incorporates a sample confid…