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Learning to Refine: Spectral-Decoupled Iterative Refinement Framework for Precipitation Nowcasting
arXiv:2606.02661v1 Announce Type: cross Abstract: Accurate precipitation nowcasting is vital for disaster mitigation, but deep learning methods face a key trade-off: regression models produce over-smoothed, spectrally decaying predictions that blur convective details and violate turbulence power laws; diffusion models generate realistic yet unanchored hallucinations lacking physical grounding. We propose Spectral-Decoupled Iterative Refinement (SDIR), a deterministic framework that...
Pool-Select-Refine: Allocation-Aware Generative Dataset Distillation with Soft-Label-Guided Latent Refinement
Announce Type: new Abstract: Diffusion-based dataset distillation has recently emerged as a promising paradigm for condensing large-scale datasets into compact synthetic sets. By leveraging pretrained generative priors, these methods can produce realistic class-conditional samples more efficiently than traditional matching-based approaches. However, most existing diffusion-based methods still adopt a rigid ``Generate-and-Use'' strategy, where the generated samples are directly treated as the...
Hormuz crisis fallout: How Indian refiners are adjusting to new crude oil mix to maximise output
The Middle East conflict has complicated the refining calculations for Indian refiners - not just in terms of the amount of crude oil that is available, but also for the kind of crude that is available. Indian oil refiners are modifying operations to handle a wider range of complex crude grades and boost production of fuels that are currently in strong demand, as the Iran conflict and disruptions around the Strait of Hormuz force them to source crude from alternative suppliers. The ongoing...
scBatchProx: Federated-Inspired Refinement for Stable Cell-Type Discriminability under Heterogeneous Batch Compositions
arXiv:2602.00423v3 Announce Type: replace Abstract: Single-cell integration workflows often construct low-dimensional cell embeddings and then refine them with post-hoc methods to reduce batch effects. This refinement process can become unstable when cell-type compositions vary across batches, with some populations underrepresented or absent in particular batches. The problem becomes more consequential in dynamic single-cell data systems, where newly acquired batches can change both...
Huawei chips refine DeepSeek model in major leap for China’s AI self-reliance
Huawei chips refine DeepSeek model in major leap for China’s AI self-reliance While Chinese chipmakers have found success in supporting AI inference, they are struggling with the far more complex process of training While Chinese chipmakers have found success in supporting AI inference – the relatively simple process of running an already-finished model to answer user prompts – they have struggled with training, the far more complex process of building or refining a model’s brain. If initial...
Physics-Informed Residuals for Adaptive Mesh Refinement in Finite-Difference PDE Solvers
Announce Type: new Abstract: Classical finite-difference solvers remain reliable tools for partial differential equations, but their efficiency depends on where mesh resolution is placed. Uniform refinement can waste degrees of freedom when solution difficulty is localised near sharp gradients, fronts, oscillations, or constraint-sensitive regions. This paper studies a hybrid strategy in which a physics-informed neural network (PINN) is used not as the final solver, but as an off-grid...
Physics-Informed Residuals for Adaptive Mesh Refinement in Finite-Difference PDE Solvers
arXiv:2606.02475v2 Announce Type: replace Abstract: Classical finite-difference solvers remain reliable tools for partial differential equations, but their efficiency depends on where mesh resolution is placed. Uniform refinement can waste degrees of freedom when solution difficulty is localised near sharp gradients, fronts, oscillations, or constraint-sensitive regions. This paper studies a hybrid strategy in which a physics-informed neural network (PINN) is used not as the final solver,...
Counterfactual Transport Flows for Offline Conservative Trajectory Refinement
Announce Type: new Abstract: Offline reinforcement learning (RL) offers a path to policy improvement from logged data alone, using historical returns or other measurable outcomes as world feedback. A key difficulty is improving observed behavior without extrapolating beyond what the offline data supports. We propose \emph{counterfactual transport flows}, a source-conditioned trajectory refinement framework for offline decision-making guided by world feedback.
Value-Refined Modal Fixed-Point Semantics with Certified Choice and Public Share-Alike Certificates
arXiv:2606.07884v1 Announce Type: new Abstract: This paper presents a finite modal semantics where truth is closed under admissible continuation, then refined by discounted value, and finally certified by residual tests. The admissibility kernel is the classical greatest fixed point of a one-step predecessor expressing that some choice cell has all compatible successors inside a set. Certified choices are exactly local witnesses; the discounted value transformer is defined only over those...
Joint angle based learning to refine kinematic human pose estimation
arXiv:2507.11075v2 Announce Type: replace Abstract: Marker-free human pose estimation (HPE) has found increasing applications in various fields. Current HPE suffers from occasional errors in keypoint recognition and random fluctuation in keypoint trajectories when analyzing kinematic human poses. The performance of existing deep learning-based models for HPE refinement is considerably limited by inaccurate training datasets in which the keypoints are manually annotated.