Tootfinder

Big-Stop Semantics: A Simple Way to Get the Benefits of Small-Step Semantics in a Big-Step Judgment
David M Kahn, Jan Hoffmann, Runming Li
https://arxiv.org/abs/2508.15157 https…

Big-Stop Semantics: A Simple Way to Get the Benefits of Small-Step Semantics in a Big-Step Judgment
As evident in the programming language literature, many practitioners favor specifying dynamic program behavior using big-step over small-step semantics. Unlike small-step semantics, which must dwell on every intermediate program state, big-step semantics conveniently jump directly to the ever-important result of the computation. Big-step semantics also typically involve fewer inference rules than their small-step counterparts. However, in exchange for ergonomics, big-step semantics give up pow…

🇺🇦 #NowPlaying on KEXP's #Early
Samantha Crain:
🎵 Pick Apart
#SamanthaCrain
https://samanthacrain.bandcamp.com/album/pick-apart-bloomsday-husbands-remix

Depth Edge Alignment Loss: DEALing with Depth in Weakly Supervised Semantic Segmentation
Patrick Schmidt, Vasileios Belagiannis, Lazaros Nalpantidis
https://arxiv.org/abs/2509.17702

Depth Edge Alignment Loss: DEALing with Depth in Weakly Supervised Semantic Segmentation
Autonomous robotic systems applied to new domains require an abundance of expensive, pixel-level dense labels to train robust semantic segmentation models under full supervision. This study proposes a model-agnostic Depth Edge Alignment Loss to improve Weakly Supervised Semantic Segmentation models across different datasets. The methodology generates pixel-level semantic labels from image-level supervision, avoiding expensive annotation processes. While weak supervision is widely explored in tr…

2024 hat Deutschland pro Kopf 198 Euro in sein marodes #Schienennetz investiert und erreicht damit im europäischen Vergleich nur das obere Mittelfeld.
Die #AmpelRegierung finanziert eine groß angelegte

Mehr Investitionen in die Schiene - trotzdem nur Mittelfeld
Deutschland hat 2024 soviel Geld in sein Schienennetz investiert wie noch nie - und kann mit anderen Ländern trotzdem nicht mithalten. Woran das liegt und wie die neue Regierung das ändern will.

SAGE: Semantic-Aware Shared Sampling for Efficient Diffusion
Haoran Zhao, Tong Bai, Lei Huang, Xiaoyu Liang
https://arxiv.org/abs/2509.15865 https://arxiv.…

SAGE: Semantic-Aware Shared Sampling for Efficient Diffusion
Diffusion models manifest evident benefits across diverse domains, yet their high sampling cost, requiring dozens of sequential model evaluations, remains a major limitation. Prior efforts mainly accelerate sampling via optimized solvers or distillation, which treat each query independently. In contrast, we reduce total number of steps by sharing early-stage sampling across semantically similar queries. To enable such efficiency gains without sacrificing quality, we propose SAGE, a semantic-awa…

An Artificial Intelligence Driven Semantic Similarity-Based Pipeline for Rapid Literature
Abhiyan Dhakal (Kathmandu University, Dhulikhel, Nepal), Kausik Paudel (Kathmandu University, Dhulikhel, Nepal), Sanjog Sigdel (Kathmandu University, Dhulikhel, Nepal)
https://arxiv.org/abs/2509.15292

An Artificial Intelligence Driven Semantic Similarity-Based Pipeline for Rapid Literature
We propose an automated pipeline for performing literature reviews using semantic similarity. Unlike traditional systematic review systems or optimization based methods, this work emphasizes minimal overhead and high relevance by using transformer based embeddings and cosine similarity. By providing a paper title and abstract, it generates relevant keywords, fetches relevant papers from open access repository, and ranks them based on their semantic closeness to the input. Three embedding models…

Breaking Token Into Concepts: Exploring Extreme Compression in Token Representation Via Compositional Shared Semantics
Kavin R V, Pawan Goyal
https://arxiv.org/abs/2509.17737 ht…

Breaking Token Into Concepts: Exploring Extreme Compression in Token Representation Via Compositional Shared Semantics
Standard language models employ unique, monolithic embeddings for each token, potentially limiting their ability to capture the multifaceted nature of word meanings. We investigate whether tokens can be more effectively represented through a compositional structure that accumulates diverse semantic facets. To explore this, we propose Aggregate Semantic Grouping (ASG), a novel approach leveraging Product Quantization (PQ). We apply ASG to standard transformer architectures (mBERT, XLM-R, mT5) an…

CLEVER: Stream-based Active Learning for Robust Semantic Perception from Human Instructions
Jongseok Lee, Timo Birr, Rudolph Triebel, Tamim Asfour
https://arxiv.org/abs/2507.15499

CLEVER: Stream-based Active Learning for Robust Semantic Perception from Human Instructions
We propose CLEVER, an active learning system for robust semantic perception with Deep Neural Networks (DNNs). For data arriving in streams, our system seeks human support when encountering failures and adapts DNNs online based on human instructions. In this way, CLEVER can eventually accomplish the given semantic perception tasks. Our main contribution is the design of a system that meets several desiderata of realizing the aforementioned capabilities. The key enabler herein is our Bayesian for…

TrajSceneLLM: A Multimodal Perspective on Semantic GPS Trajectory Analysis
Chunhou Ji, Qiumeng Li
https://arxiv.org/abs/2506.16401 https://

TrajSceneLLM: A Multimodal Perspective on Semantic GPS Trajectory Analysis
GPS trajectory data reveals valuable patterns of human mobility and urban dynamics, supporting a variety of spatial applications. However, traditional methods often struggle to extract deep semantic representations and incorporate contextual map information. We propose TrajSceneLLM, a multimodal perspective for enhancing semantic understanding of GPS trajectories. The framework integrates visualized map images (encoding spatial context) and textual descriptions generated through LLM reasoning (…

Semantic and Visual Crop-Guided Diffusion Models for Heterogeneous Tissue Synthesis in Histopathology
Saghir Alfasly, Wataru Uegami, MD Enamul Hoq, Ghazal Alabtah, H. R. Tizhoosh
https://arxiv.org/abs/2509.17847

Semantic and Visual Crop-Guided Diffusion Models for Heterogeneous Tissue Synthesis in Histopathology
Synthetic data generation in histopathology faces unique challenges: preserving tissue heterogeneity, capturing subtle morphological features, and scaling to unannotated datasets. We present a latent diffusion model that generates realistic heterogeneous histopathology images through a novel dual-conditioning approach combining semantic segmentation maps with tissue-specific visual crops. Unlike existing methods that rely on text prompts or abstract visual embeddings, our approach preserves cri…