Relation Adaptive Network
No mentions found
This entity hasn't been tracked yet, or Iris is still building its knowledge base.
Related Articles from SNS
DRAN: A Distribution and Relation Adaptive Network for Spatio-temporal Forecasting
arXiv:2504.01531v4 Announce Type: replace Abstract: Accurate predictions of spatio-temporal systems are crucial for tasks such as system management, control, and crisis prevention. However, the inherent time variance of many spatio-temporal systems poses challenges to achieving accurate predictions whenever stationarity is not granted. In order to address non-stationarity, we propose a Distribution and Relation Adaptive Network (DRAN) capable of dynamically adapting to relation and...
TALAN: Task-Aligned Latent Adaptation Networks for Targeted Post-Training of Large Language Models
Announce Type: new Abstract: Targeted post-training aims to improve reasoning, math, and code without degrading strengths. Low-rank adapters are efficient but task-global; activation interventions are input-aware but often require separate probes, vectors, or inference-time steering. We introduce TALAN (Task-Aligned Latent Adaptation Networks), a sequence-conditioned latent side path inserted into a transformer's residual stream and co-trained with a low-rank adapter in one SFT loop.
Multi-Modal Graph Neural Network with Transformer-Guided Adaptive Diffusion for Preclinical Alzheimer Classification
arXiv:2606.03322v1 Announce Type: new Abstract: The graphical representation of the brain offers critical insights into diagnosing and prognosing neurodegenerative disease via relationships between regions of interest (ROIs). Despite recent emergence of various Graph Neural Networks (GNNs) to effectively capture the relational information, there remain inherent limitations in interpreting the brain networks. Specifically, convolutional approaches ineffectively aggregate information from...
A Human-Sensitive Controller: Adapting to Human Musculoskeletal Disorder-Related Constraints via Reinforcement Learning
Announce Type: replace Abstract: Work-Related Musculoskeletal Disorders continue to be a major challenge in industrial environments, leading to reduced workforce participation, increased healthcare costs, and long-term disability. This study introduces a human-sensitive robotic system aimed at reintegrating individuals with a history of musculoskeletal disorders into standard job roles, while simultaneously optimizing ergonomic conditions for the broader workforce. This research leverages...
What Makes a Desired Graph for Relational Deep Learning?
Announce Type: new Abstract: Relational deep learning (RDL) converts relational databases (RDBs) into heterogeneous graphs, but graphs derived directly from database schemas are often not well suited for how graph neural networks (GNNs) perform relational reasoning. We study what makes a relational graph suitable for deep learning and show that schema-derived graphs suffer from two systematic failures: information overload and semantic fragmentation.
The small RNA Teg16 represses rsbV and modulates SigB-dependent gene expression in Staphylococcus aureus
Staphylococcus aureus relies on coordinated regulatory networks to adapt to environmental stress and host-associated conditions. The alternative sigma factor SigB plays a central role in this process and is controlled by the anti-anti-sigma factor RsbV, which functions as a key regulatory node in the pathway. While numerous small regulatory RNAs (sRNAs) have been identified in S. aureus, relatively few have been directly linked to the SigB stress response network.
A prognostic human brain network for diffuse midline glioma
Abstract Diffuse midline gliomas (DMGs) are near-universally lethal tumours of the childhood central nervous system1,2. In animal models, DMGs form brain-wide integrated networks through neuron-to-glioma synapses3,4,5,6 and glioma-to-glioma gap junctional coupling3. This extensive connectivity robustly promotes the growth and invasion of DMG3,4,5,6,7,8,9 and other glial malignancies10,11,12 through paracrine mechanisms and direct neuron-to-glioma synapses.
Recursive exploration of metabolic yield space
Genome-scale metabolic network reconstructions contain extremely detailed and valuable information regarding cellular metabolism. For many applications such as finding genetic engineering targets and reduced kinetic model construction, metabolic network analysis techniques exist. Yield spaces based on the extreme rays of solution cones related to the metabolic network are frequently constructed for these types of analyses.
A global regulatory atlas of Streptomyces reveals conserved and diversified transcriptional networks across actinomycetes
Transcriptional regulatory networks determine how bacteria integrate environmental signals with growth, metabolism and stress adaptation. Actinomycetes encode exceptionally large repertoires of transcription factors (TFs) coordinating morphological development, environmental adaptation and specialized metabolism, yet their regulatory networks remain poorly defined. Here, we apply DNA affinity purification sequencing (DAP-seq) to 789 predicted TFs of Streptomyces coelicolor, generating...
RAVQ-HoloNet: Rate-Adaptive Vector-Quantized Hologram Compression
arXiv:2511.21035v2 Announce Type: replace Abstract: Holography offers significant potential for AR/VR applications. However, its adoption is limited by the high demand for data compression. Existing deep learning approaches generally lack rate adaptivity within a single network and often require multiple models to cover different bandwidth requirements.