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Related Articles from SNS
SearchSwarm: Towards Delegation Intelligence in Agentic LLMs for Long-Horizon Deep Research
Announce Type: new Abstract: Large language models are increasingly expected to handle complex, long-horizon real-world tasks whose context demands can grow without bound, yet model context windows remain inherently finite. Recent work explores a paradigm where a main agent decomposes tasks and dispatches subtasks to subagents, which execute and return only summarized results, conserving the main agent's context budget. However, performing this well requires delegation intelligence: the...
Majestic manta rays dive deep to survive storm events, data reveal
Majestic manta rays dive deep to survive storm events, data reveal Sadie Harley Scientific Editor Robert Egan Associate Editor New research led by the University of the Sunshine Coast has found that reef manta rays are diving deep in storm events to find food and stay alive. As World Environment Day is celebrated around the globe on June 5, the findings offer hope for the future of a species listed as vulnerable to extinction. Lead author Anna Knochel said the team was surprised to record no...
A Foundation Model for Wearable Movement Data in Mental Health Research
arXiv:2411.15240v5 Announce Type: replace Abstract: Wearable movement data is collected by nearly all commercially available smartwatches and is a valuable resource for mental health research, reflecting fine-grained temporal behavioral trends. Despite its promise, the development of foundation models for health wearable modeling remains limited when compared to clinical image and text analysis. We designed transformers with patch embeddings and used self-supervised masked autoencoder...
Achieving Rotation-Invariant Convolution via Non-Learnable Orientation Alignment Operators
Announce Type: replace Abstract: Achieving rotational invariance in deep neural networks without data augmentation is a research hotspot. Intrinsic invariance enables features to capture targets' inherent properties, enhancing deep learning performance in visual tasks. Based on various types of non-learnable operators, this paper proposes a comprehensive set of convolution operations that are natually invariant to arbitrary rotations.
ViTAMIn-O: Democratizing computer vision-based machine learning for stem cell research
Deep Learning (DL) holds exciting potential in automating the prediction of organoid differentiation results. Nevertheless, current models lack adaptability, openness, and robustness in performance. Additionally, broad employments of predictive models in wet-lab settings necessitate machine learning expertise, often not readily available in biologically oriented laboratories.
Scientists lose critical climate record as ocean observatory will go dark under Trump funding cuts
Scientists lose critical climate record as ocean observatory will go dark under Trump funding cuts Andrew Zinin Lead Editor A portion of one of the most ambitious ocean monitoring networks ever built will go dark this month when scientists board a research vessel and motor off the Oregon coast to pull a research buoy from deep out of the Pacific. The buoy 80 meters (260 feet) below the water's surface will be removed June 16 from the Ocean Observatories Initiative—a network of more than 900...
Unraveling the Ai2 Asta Scholarly Research Assistant Citation System
Announce Type: new Abstract: Despite the growing integration of Deep Research tools into academic workflows, empirical evidence on the operation, stability, and potential biases of their citation systems remains scarce. This study addresses this gap by evaluating the intensity, consistency, and bibliographic characteristics of references cited in the literature reports generated by Ai2 Asta, with the aim of understanding how its citation system operates and assessing its implications for...
Construction of Historical Knowledge Graphs Based on BERT and Graph Neural Networks
Announce Type: new Abstract: Through digital humanities research and scale-up historical data analysis, a significant amount of traditional historical text is converted into structured knowledge graphs. This paper provides a high-level architecture that combines bidirectional encoder representations of transformers (BERT) and graph neural networks (GNN) to extract the entities and relationships from various types of historical texts. The texts of traditional history resolve linguistic...
Age verification tech could put children at greater risk, says think tank
momius - stock.adobe.com Age verification tech could put children at greater risk, says think tank UK proposals for mandatory age verification will not mitigate children’s exposure to harmful content and ‘addictive’ app design, and risks excluding vulnerable groups from online services, says Foundation for Information Policy Research A technology think tank has raised “deep concerns” with government proposals to mandate strong age verification to access online services, as ministers consider...
PAC-Bayesian Adversarially Robust Generalization for Message Passing Graph Neural Networks: A Sensitivity Analysis
arXiv:2606.06293v1 Announce Type: new Abstract: Whilst the vulnerability of graph neural networks (GNNs) to adversarial attacks poses a critical threat to graph representation learning, the understanding of the robust generalization behavior remains a fundamental challenge in the adversarial setting. Recently, PAC-Bayesian margin-based generalization analysis substantially advances this line of research by providing a flexible and data-dependent analytical framework. However, existing robust...