Tootfinder

Mooie blog,
Cloudbeleid NL: Kabinet verstopt gebrek aan politieke wil en morele moed achter drogreden (in de wolken)
https://medium.com/@simon.lelieveldt/cloudbeleid-nl-kabinet-verstopt-gebr…

Cloudbeleid NL: Kabinet verstopt gebrek aan politieke wil en morele moed achter drogreden (in de wolken)
De discussie in Den Haag over de toekomst van onze overheidsdata in Amerikaanse cloud lijkt ingewikkeld, maar draait in de kern om één vraag: durven we onze digitale infrastructuur en gevoelige…

Someone captured the last 30 seconds of a piano improvisation I did here in Iceland. =)

Frugal Machine Learning for Energy-efficient, and Resource-aware Artificial Intelligence
John Violos, Konstantina-Christina Diamanti, Ioannis Kompatsiaris, Symeon Papadopoulos
https://arxiv.org/abs/2506.01869

Frugal Machine Learning for Energy-efficient, and Resource-aware Artificial Intelligence
Frugal Machine Learning (FML) refers to the practice of designing Machine Learning (ML) models that are efficient, cost-effective, and mindful of resource constraints. This field aims to achieve acceptable performance while minimizing the use of computational resources, time, energy, and data for both training and inference. FML strategies can be broadly categorized into input frugality, learning process frugality, and model frugality, each focusing on reducing resource consumption at different…

DrKGC: Dynamic Subgraph Retrieval-Augmented LLMs for Knowledge Graph Completion across General and Biomedical Domains
Yongkang Xiao, Sinian Zhang, Yi Dai, Huixue Zhou, Jue Hou, Jie Ding, Rui Zhang
https://arxiv.org/abs/2506.00708

DrKGC: Dynamic Subgraph Retrieval-Augmented LLMs for Knowledge Graph Completion across General and Biomedical Domains
Knowledge graph completion (KGC) aims to predict missing triples in knowledge graphs (KGs) by leveraging existing triples and textual information. Recently, generative large language models (LLMs) have been increasingly employed for graph tasks. However, current approaches typically encode graph context in textual form, which fails to fully exploit the potential of LLMs for perceiving and reasoning about graph structures. To address this limitation, we propose DrKGC (Dynamic Subgraph Retrieval-…

Analysis of Forbidden Neon Emission lines in HAeBe Stars using Spitzer IRS spectra
D. Akhila, Blesson Mathew, S. Nidhi, B. Shridharan, R. Arun, Hema Anilkumar, G. Maheswar, Sreeja S. Kartha, P. Manoj, Suman Bhattacharyya
https://arxiv.org/abs/2506.01036

Analysis of Forbidden Neon Emission lines in HAeBe Stars using Spitzer IRS spectra
We analyzed high-resolution mid-infrared spectra of 78 well-known Herbig Ae/Be (HAeBe) stars using Spitzer InfraRed Spectrograph data, focusing on the detection of [Ne {\sc II}] and [Ne {\sc III}] emission lines as indicators of ionized outflows or disk winds. Emission from [Ne {\sc II}] at 12.81 $μ$m or [Ne {\sc III}] at 15.55 $μ$m was identified in 25 sources, constituting the largest sample of HAeBe stars with these detected lines. Our analysis revealed a higher detection frequency of [Ne …

The Soviet way with words 😂
(from https://en.m.wikipedia.org/wiki/Trofim_Lysenko)

Trofim Lysenko - Wikipedia

FusionAudio-1.2M: Towards Fine-grained Audio Captioning with Multimodal Contextual Fusion
Shunian Chen, Xinyuan Xie, Zheshu Chen, Liyan Zhao, Owen Lee, Zhan Su, Qilin Sun, Benyou Wang
https://arxiv.org/abs/2506.01111

FusionAudio-1.2M: Towards Fine-grained Audio Captioning with Multimodal Contextual Fusion
High-quality, large-scale audio captioning is crucial for advancing audio understanding, yet current automated methods often generate captions that lack fine-grained detail and contextual accuracy, primarily due to their reliance on limited unimodal or superficial multimodal information. Drawing inspiration from human auditory perception, which adeptly integrates cross-modal cues and performs sophisticated auditory scene analysis, we introduce a novel two-stage automated pipeline. This pipeline…

Sensor Fusion Methods for Gaussian Mixture Models
Ishan Paranjape, Islam Hussein, Jeremy Murray-Krezan, Sean Phillips, Suman Chakravorty
https://arxiv.org/abs/2506.00383

Sensor Fusion Methods for Gaussian Mixture Models
Consensus is a popular technique for distributed state estimation. This formulation allows networks of connected agents or sensors to exchange information about the distribution of a set of targets with their immediate neighbors without the need of a centralized node or layer. We present decentralized consensus-based fusion techniques for a system whose target prior estimates are a weighted mixture of Gaussian probability density functions (PDFs) for the following cases: 1) in which all agents …

WHEN TO ACT, WHEN TO WAIT: Modeling Structural Trajectories for Intent Triggerability in Task-Oriented Dialogue
Yaoyao Qian, Jindan Huang, Yuanli Wang, Simon Yu, Kyrie Zhixuan Zhou, Jiayuan Mao, Mingfu Liang, Hanhan Zhou
https://arxiv.org/abs/2506.01881

WHEN TO ACT, WHEN TO WAIT: Modeling Structural Trajectories for Intent Triggerability in Task-Oriented Dialogue
Task-oriented dialogue systems often face difficulties when user utterances seem semantically complete but lack necessary structural information for appropriate system action. This arises because users frequently do not fully understand their own needs, while systems require precise intent definitions. Current LLM-based agents cannot effectively distinguish between linguistically complete and contextually triggerable expressions, lacking frameworks for collaborative intent formation. We present…

Dyna-Think: Synergizing Reasoning, Acting, and World Model Simulation in AI Agents
Xiao Yu, Baolin Peng, Ruize Xu, Michel Galley, Hao Cheng, Suman Nath, Jianfeng Gao, Zhou Yu
https://arxiv.org/abs/2506.00320

Dyna-Think: Synergizing Reasoning, Acting, and World Model Simulation in AI Agents
Recent progress in reasoning with large language models (LLMs), such as DeepSeek-R1, demonstrates impressive capabilities in domains like mathematics and coding, by exhibiting complex cognitive behaviors such as verification, goal decomposition, and self-reflection. However, it is unclear what behavior is effective and what behavior is missing for long-horizon AI agents tasks. In this work, we propose Dyna-Think, a thinking framework that integrates planning with an internal world model with re…