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Earlier sources: Fusion
This energy thread follows fusion through original research, public projects, and source reports. Read the evidence below to see which developments are represented in SYMTRI’s archive.
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Open in the map ↗129 classified signals in the fourteen-day window. These counts include signals awaiting readable evidence.
Earlier sources
Up to ten sources with readable evidence, ordered by their feed timestamp. These archive sources can be older than the live fourteen-day window. This list is a sample, not a measure of total activity. Dates distinguish paper publication, repository creation, model/dataset activity, Hacker News posts, and KEV catalog additions. “First archived” records when SYMTRI first stored the signal.
- arXiv · Published · First archived
FIREWALL: A surrogate model for the rapid assessment of tokamak wall loading and melting by runaway electrons ↗
Runaway electron (RE) beams generated during tokamak disruptions can deposit highly localized heat loads on plasma-facing components, posing a serious risk of melting and damage. Monte Carlo particle transport simulations coupled with three-dimensional thermomechanical response modeling can quantify this damage but are too computationally demanding for device-scale assessments and extensive scenario scans. We present FIREWALL (Fast Integrated Runaway Electron WALL loads), a surrogate model that combines a database of \textsc{Geant4} volumetric energy-deposition profiles with a one-dimensional…
- arXiv · Published · First archived
Training-Free Affinity Fusion of Neural and Embedding-Based Speaker Diarization ↗
Speaker diarization systems based on speaker embeddings and neural diarization exploit complementary forms of speaker information, but their intermediate representations are not directly compatible. We introduce Training-Free Affinity Fusion (TFAF), which integrates the speaker structure inferred by a neural diarizer into an embedding-based diarization system. The neural speaker partition is used to condition local speaker representations, from which we construct a continuous affinity matrix and combine it with the embedding-based acoustic affinity before a single global clustering step. The m…
- OpenAlex · Published · First archived
DAA-YOLOv8s with downsampling aware high resolution fusion for real time tiny vehicle detection in UAV imagery ↗
Detecting very small vehicles in UAV imagery remains challenging due to severe scale variations, dense object distributions, diverse viewing directions, and the loss of fine-grained spatial information caused by repeated downsampling. To address these limitations, we propose DAA-YOLOv8s (Downsampling-Aware Architecture for YOLOv8s), a lightweight detection framework that explicitly preserves high-resolution spatial information for improved tiny-object localization in aerial scenes. The proposed model integrates a Multi-Receptive Field Downsampling (MRFD) module, a C2f-Partial Residual Bottlene…
- OpenAlex · Published · First archived
A PDE-Driven Laplacian Multi-Exposure Fusion for Structure Preserving Image ↗
Multi-Exposure Image Fusion (MEF) aims to fuse a single visually symmetricalimage, which combines the luminosity and preserves the structural data of a scene atdifferent exposure levels. Traditionally, Laplacian Pyramid (LP) fusion methods rely onGaussian smoothing, which diffuses intensity isotropically, resulting in blurred edges, haloartifacts, and loss of fine detail. To overcome these limitations, the paper proposes amethod comprising Mean Curvature and Laplacian Pyramid that combines curvature-based edge preservation with multiscale structural decomposition. Mean CurvatureFiltering (MCF)…
- OpenAlex · Published · First archived
Open-ended Human Activity Understanding via LLM-assisted Motion Decomposition and Semantic Fusion ↗
Human activity is a fundamental component of intelligent interactive systems. Existing human activity recognition (HAR) follows a closed-ended assumption—defining a fixed set of activity classes and classifying each sensor signal into one of them—yet real-world human behavior is complex and ever-changing and thus cannot be fully represented by any fixed set. We present a new paradigm for open-ended human activity understanding (HAU), shifting the goal from closed-set classification to understanding activities via their meta-motions. To achieve this, we design MOSAIC, a motion decomposition and…
- OpenAlex · Published · First archived
Hierarchical multimodal semantic fusion for few-shot reader action recognition in reading environments ↗
Abstract Understanding reader behavior in real-world reading environments is important for data-driven content services and intelligent publishing analytics. However, most action recognition methods target motion-dominant scenarios and struggle under few-shot conditions to capture the subtle visual cues and hierarchical semantics of reading-related behaviors. To address this problem, we construct the Reader Action Recognition Dataset (RARS), containing 12 action classes and 1,728 video clips, and propose a Hierarchical Semantic Fusion Network (HSFN) for few-shot reader action recognition. HSFN…
- OpenAlex · Published · First archived
VDLF-Net: Variational Feature Fusion for Adaptive and Few-Shot Visual Learning ↗
Modeling high-dimensional visual data presents two intertwined challenges: learning feature representations that adapt to complex structures, and enabling robust inference with limited labeled examples. Although deep learning models, such as Convolutional Neural Networks, excel at representation learning and variational methods, e.g., Variational Autoencoders, offer a principled framework for uncertainty quantification, combining them for both challenges remains an active modeling problem. Driven by this limitation, this paper proposes a novel variational deep learning fusion framework. Its co…
- arXiv · Published · First archived
Lights, Camera, Attack: Exploiting Temporal HDR Fusion with Pulsed Light ↗
Modern cameras widely use temporal High Dynamic Range (HDR) to improve visibility by capturing a sequence of exposures with different integration times and fusing them into a single image. This process implicitly assumes that scene illumination remains sufficiently stable during capture. We introduce FLASH (Fusion-Level Attack by Saturating HDR), an external pulsed-light attack that deliberately attacks this assumption by creating cross-exposure inconsistency before downstream perception. FLASH exploits an algorithmic assumption rather than relying on sensor damage or hardware failure, and req…
- OpenAlex · Published · First archived
Hyperspectral and Multispectral Image Fusion via Deep Generalised Linear Mixed Model ↗
ABSTRACT Hyperspectral–multispectral image fusion is an incomplete information fusion problem, and directly embedding information can lead to modality‐specific responses and cause spectral distortion, whereas the strong interband redundancy of hyperspectral images makes full‐dimensional fusion inefficient. To address the aforementioned issues, we propose a novel fusion framework based on the generalised linear mixed model theory. Specifically, high‐dimensional hyperspectral images can be composed of a low‐dimensional abundance matrix, which describes the pixel‐wise spatial proportions of the s…
- OpenAlex · Published · First archived
Edge-Aware Dynamic Convolution and Cross-Modal Relation-Aware Fusion Network for Infrared and Visible Image Fusion ↗
Infrared and visible image fusion combines complementary thermal and structural information from the two modalities into a single composite image. Existing methods have two critical limitations: (1) inadequate utilization of visible structural information causes blurred edges, and (2) modality-specific and shared responses are not always separately represented when learning the fusion weights. To address these problems, an edge-aware dynamic convolution and cross-modal relation-aware fusion network is proposed to fully exploit visible structural details and explicitly model complementary infor…
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How to assess evidence about Fusion
Check each original source for its methods, release status, limitations, and supporting data. A repository announcement, a preprint, and a published study provide different kinds of evidence. SYMTRI’s subject classification helps discovery; it does not independently validate the source’s claims.
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