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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 ↗128 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
Tracking the Invisible: Self-Supervised Wheel Track Detection with Thermal-RGB Fusion ↗
Accurate detection of drivable area is essential for autonomous driving and advanced driver assistance systems. While state-of-the-art methods focus on general traversability estimation, they prove inadequate for snow-covered roads where following existing wheel tracks offers crucial advantages: improved traction, predictable vehicle dynamics, and verified path stability. To address this, we propose a low-cost, self-supervised approach detecting safety-critical wheel tracks using synchronized RGB-I (RGB + Long-Wave Infrared) imagery. Our method automatically generates training labels by projec…
- arXiv · Published · First archived
Diversity-Aware Ensemble of Quantum-Classical Models via Combinatorial Fusion ↗
Variational quantum circuits (VQCs) are highly sensitive to circuit architecture, yet most quantum machine learning pipelines select a single best design and discard the remaining candidates. This paper addresses whether the structural diversity across quantum circuit search spaces can be used to produce stronger predictions through model fusion. We apply Combinatorial Fusion Analysis (CFA), a model fusion framework that selects and combines multiple scoring systems using rank-score function (RSF) and cognitive diversity (CD), to a pool of 29 architecturally diverse classical models and quantu…
- GitHub · Repository created · First archived
Clawfitzfilter/Display-Fusion ↗
Display Fusion optimizes desktop software utility configurations by providing a robust and efficient management system that enhances productivity without...
- OpenAlex · Published · First archived
TPA-NET: A Progressive Cross-Modal Alignment and Confidence Fusion Network for Object Detection in Low-Resolution Optical-SAR Remote Sensing Imagery ↗
Object detection in low-resolution optical-SAR remote sensing imagery is challenged by degraded object structures, residual cross-modal spatial inconsistency, and unreliable SAR responses. To address these issues, we propose TPA-NET, a progressive cross-modal alignment and confidence fusion network built on YOLOv10. TPA-NET adopts a three-stage coarse-to-fine processing strategy. The Shallow Coarse Alignment (SCA) module performs initial feature-space calibration, the Pyramid Residual Alignment (PRA) module further refines local cross-modal correspondence across multiple scales, and the Detect…
- OpenAlex · Published · First archived
Position-dependent fusion and non-fusion of colliding dark solitons in harmonically trapped Bose–Einstein condensates ↗
Dark-soliton collisions exhibit two distinct regimes, fusion and non-fusion, characterized by different density profiles at the collision instant. For symmetric soliton pairs on a homogeneous background, the two regimes are separated by the critical velocity vc=cs/2. In a harmonically trapped Bose–Einstein condensate, however, both the background density and the soliton velocities vary along the trajectories, making the incoming local conditions position dependent. Combining the exact homogeneous two-soliton solution with a local-density description of the trapped background, we obtain ρpred=n…
- OpenAlex · Published · First archived
Deep Learning-Based Inverse Identification of the Johnson–Cook Yield and Fracture Parameters for Ti-6Al-4V Fabricated by Laser Powder Bed Fusion Using Batch Finite Element Simulation Data ↗
Finite element analysis (FEA) is a key tool for predicting the mechanical behavior of additively manufactured parts. However, its predictive accuracy relies on the underlying constitutive model and, in particular, on precise model parameters. The main purpose of this study is to develop and validate a condition-grouped multimodal deep learning framework for efficiently identifying the ten parameters of the Johnson–Cook (JC) yield and fracture models for Ti-6Al-4V (TC4) alloy fabricated by laser powder bed fusion (L-PBF) using batch finite element simulation data. Using nearly 20,000 simulation…
- OpenAlex · Published · First archived
Constructing a graduate employability knowledge graph with multi-source data fusion and graph neural network-based multi-task prediction ↗
Addressing the limitations of existing employability prediction frameworks in cross-domain data integration and heterogeneous knowledge modelling, this study proposes a graduate employability knowledge graph construction framework grounded in multi-source heterogeneous data fusion and graph neural networks. A bidirectional confidence-scored alignment strategy is employed to integrate learning behaviour, market demand, and knowledge mastery data across three cross-domain sources, constructing a dynamic knowledge graph encompassing student, skill, and occupation entities across three hierarchica…
- OpenAlex · Published · First archived
Lite-MSTFNet: A lightweight sEMG gesture recognition network BASED on multi-scale spatiotemporal fusion and dynamic gated attention ↗
Surface electromyography (sEMG) has emerged as a promising modality for gesture recognition in deep learning applications. However, deploying state-of-the-art deep learning models on resource-constrained embedded devices remains a critical challenge due to their substantial computational demands. To address this, we propose Lite-MSTFNet, a lightweight multi-scale spatio-temporal fusion network specifically tailored for efficient sEMG-based gesture recognition on edge devices. The architecture leverages a parallel multi-branch design with depthwise separable convolutions to effectively capture…
- OpenAlex · Published · First archived
Efficient dual-attention fusion with gated feed-forward learning for image restoration under compound degradations ↗
Image restoration is a fundamental task in computer vision, widely applied to low-light enhancement, image denoising and compression artifact reduction. Transformer-based restoration methods are effective at capturing long-range dependencies and modeling global features, but they often suffer from insufficient interaction between shallow and deep features, as well as high computational overhead. To address these limitations, this paper proposes an image restoration method named DAGF-Swin, based on dual-attention fusion and gated feed-forward collaboration. First, a dual-attention fusion (DAF)…
- OpenAlex · Published · First archived
Cross-modal attention fusion of ultrasound imaging and clinical tabular data for breast lesion classification: A multimodal deep learning framework ↗
Abstract Accurate classification of breast lesions in ultrasound images is an important yet challenging task, plagued by visual ambiguity between classes and morphological variability within classes. This paper presents a multimodal deep learning framework combining EfficientNet-B4 visual features with an 11-dimensional, leakage-free clinical tabular feature vector, organized into three feature-group tokens (intensity, texture, and geometry), through a multi-token cross-modal attention fusion mechanism. A systematic audit of candidate tabular features identified that eight commonly used lesion…
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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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