Complex Acquisition
No mentions found
This entity hasn't been tracked yet, or Iris is still building its knowledge base.
Related Articles from SNS
HD-DinoMoE: A Class-Aware Hierarchical Dual Mixture-of-Experts Network for Scleral Anomaly Segmentation in Complex Acquisition Scenarios
Announce Type: new Abstract: Traditional Chinese Medicine (TCM) ocular inspection provides empirical cues for assessing scleral surface anomalies, but its clinical use remains subjective and difficult to quantify. To support intelligent and quantifiable ocular inspection, this study presents the TCM-inspired Artificial Intelligence Ocular Auxiliary Diagnosis System (TAO) and focuses on pixel-level scleral surface anomaly segmentation. For clinical and user-acquired images affected by...
Building Trust in Black-box Optimization: A Comprehensive Framework for Explainability
Announce Type: replace Abstract: Optimizing costly black-box functions within a constrained evaluation budget presents significant challenges in many real-world applications. Surrogate Optimization (SO) is a common resolution, yet its proprietary nature introduced by the complexity of surrogate models and the sampling core (e.g., acquisition functions) often leads to a lack of explainability and transparency. While existing literature has primarily concentrated on enhancing convergence to...
The emergent mental operation of the learning brain
How does the learning brain give rise to emergent mental operations that enable skill internalization and generalization? Using five-year longitudinal tracking of children acquiring abacus-based mental calculation, we reveal how sustained practice progressively reconfigures whole-brain states. Learning induces nonlinear transitions from stable baseline configurations through a transient exploratory phase toward restabilized associative brain networks.
India’s bullet train project: Nine years later, is the dream finally nearing reality?
When Prime Minister Narendra Modi and former Japanese Prime Minister Shinzo Abe laid the foundation stone for India’s first bullet train project in Ahmedabad in September 2017, the event was presented as more than just the launch of a railway corridor. It was pitched as India’s first major step into the world of high-speed rail — a technology long associated with countries like Japan, China and France. Nearly nine years later, the Mumbai-Ahmedabad High-Speed Rail (MAHSR) corridor remains...
Magnon momentum microscopy: A new window into nanoscale spin-wave physics
Magnon momentum microscopy: A new window into nanoscale spin-wave physics Sadie Harley Scientific Editor Robert Egan Associate Editor An international team led by the Max Born Institute has developed a new type of momentum microscopy to image magnons—the quanta of collectively excited spins—directly in two-dimensional reciprocal space using soft X-rays. Owing to its remarkable sensitivity, simplicity, and access to nanometer-scale wavelengths, this novel technique establishes a powerful and...
Urban Flood Observations: A hand-labeled training and validation dataset of post-flood inundation
arXiv:2604.23066v2 Announce Type: replace Abstract: Urban flooding affects lives and infrastructure worldwide. Mapping inundation in complex urban environments from satellite imagery remains challenging due to limited spatial resolution, infrequent acquisitions, and cloud cover. We present Urban Flood Observations (UFO), a global, hand-labeled dataset of post-flood inundation in diverse urban settings.
Snowflake buys Natoma to help freeze out rogue agents
Snowflake is acquiring Natoma, a startup that provides a gateway for managing AI agent permissions across enterprise applications. This acquisition is part of Snowflake's strategy to establish an "agentic control plane," ensuring that AI agents can interact with business systems while adhering to strict security and governance policies. Natoma's technology enforces identity verification and access controls at the level of individual tool calls, allowing for secure and controlled AI actions.
Data Synthesis and Parameter-Efficient Fine-Tuning for Low-Resource NMT: A Case Study on Q'eqchi' Mayan
arXiv:2606.09767v1 Announce Type: new Abstract: Neural machine translation for digitally low-resource Indigenous languages is often hindered by extreme data scarcity, prompting reliance on extractive web-scraping. To ensure data sovereignty, this study introduces a data synthesis methodology to bootstrap NMT models without scraping target-language parallel text. Focusing on Q'eqchi' Mayan, we transformed community-sourced dictionaries into a massive synthetic corpus, utilizing...
Computational Modeling of Human Adaptation in Urban Infrastructure Management under Extreme Conditions: A Case Study of Subway Flood Scenarios
arXiv:2606.06429v1 Announce Type: new Abstract: Decision-making in urban infrastructure management during extreme events relies heavily on human operators, yet current computational support systems often fail to account for non-monotonic human adaptation and latent psychological biases like overconfidence and defensive overcorrection. This study addresses this gap by integrating Instance-Based Learning Theory (IBLT) into the domain of civil engineering computing. We establish a computational...
Robust learning-driven structural and functional plasticity of spines in the mature mouse cortex
Spines in the adult cortex are thought to be highly stable, and that their capacity for modest remodeling supports learning. Using a visual association task and a multilevel imaging approach in adult mice, we found a robust learning-driven increase in the complexity of spine nanostructure, as well as a rapid and persistent increase in spine formation during task acquisition that were accompanied by an overall reduction in spine size of layer 2/3 neurons in the primary visual cortex (V1)....