Granular
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Related Articles from SNS
Investigating frictional instability due to pressurization in granular media: insights from coupled computational fluid dynamics discrete element method
arXiv:2606.06257v1 Announce Type: cross Abstract: Fluid pressurization can reactivate subcritically stressed granular layers in faults, slopes, and injection-perturbed reservoirs, but grain-scale feedbacks among pressure diffusion, drainage, and contact-network degradation remain unresolved. Here, 3D coupled CFD-DEM simulations investigate pore-pressure-induced reactivation of confined, fluid-saturated granular shear layers under imposed shear stress. Strain-controlled tests define the...
Impact of viscoelastic polymer solution droplets on a granular bed
arXiv:2606.00319v1 Announce Type: cross Abstract: The impact of polymer solution droplets on granular beds is relevant to powder processing, binder jetting additive manufacturing, and environmental applications involving erosion control or spray deposition, yet most controlled studies of drop--grain interactions have focused on Newtonian liquids. In this study, we experimentally investigate the impact of viscoelastic polyethylene oxide (PEO) droplets on a dry granular bed and compare the...
The Impact of Temporal Granularity on Socio-Demographic Inference from Household Load Profiles
Announce Type: new Abstract: Smart meter data can reveal sensitive socio-demographic characteristics of households, raising privacy concerns. While this risk has been demonstrated at fixed granularities, the role of temporal resolution in shaping inference performance remains insufficiently explored. This paper addresses this gap by analyzing how load profiles with granularities from 15 minutes to 7 days affect the predictability of eight socio-demographic attributes in a dataset of 1,589...
From Coarse to Fine: Managing Temporal Granularity in Spatio-Temporal Data for Fine-Grained Traffic Prediction
Announce Type: new Abstract: Efficient acquisition, storage, and utilization of traffic data are critical challenges in spatio-temporal data management. Most traffic data systems collect and store observations at fixed, coarse-grained temporal intervals to reduce storage and computation costs. However, such coarse-grained data severely limits downstream applications that require predictions at a finer temporal granularity.
Skill Availability and Presentation Granularity in Large-Language-Model Agents: A Controlled SkillsBench Study
Announce Type: new Abstract: Skill documents provide procedural knowledge to large-language-model agents at inference time. This article studies whether the presentation granularity of controlled skill knowledge changes downstream task success. The experiment uses a pinned SkillsBench version, a 30-task domain-balanced subset validated by official oracle runs, two reasoning-enabled model configurations, six skill conditions, and five trials per task-condition-model cell.
A Finetuned SpeechLLM for Joint Multi-Granular L2 Assessment and Natural-Language Rationales
Announce Type: new Abstract: Automated L2 speech assessment can assign proficiency labels, but often lacks interpretability. We propose a rubric-guided SpeechLLM for multi-aspect, multi-granular assessment, trained with a hybrid objective combining supervised fine-tuning and Bounded Direct Preference Optimization. The model jointly predicts ordinal labels at the sentence-level (accuracy, fluency, prosody), word/phoneme-level accuracy, and generates a natural-language rationale in the same...
Injection-rate effects on failure in a fluid-saturated granular fault gouge
arXiv:2606.08766v1 Announce Type: new Abstract: Fluid injection into the Earth's subsurface, performed for energy extraction, waste disposal, and resource development, is known to reactivate gouge-filled faults and induce seismicity, a key hazard in modern geotechnical operations. Nevertheless, the role of injection rate in controlling fault-gouge failure remains poorly understood. Here we present both an analytical theory and coupled fluid--granular (discrete element) numerical simulations...
MTA: Multi-Granular Trajectory Alignment for Large Language Model Distillation
Announce Type: replace Abstract: Knowledge distillation is a key technique for compressing large language models (LLMs), but most existing methods align representations at fixed layers or token-level outputs, ignoring how representations evolve across depth. As a result, the student is only weakly guided to capture the teacher's internal relational structure during distillation, which limits knowledge transfer. To address this limitation, we propose Multi-Granular Trajectory Alignment (MTA),...
Multi-Granularity 3D Kidney Lesion Characterization from CT Volumes
arXiv:2606.04365v1 Announce Type: new Abstract: Radiology reports describe kidney lesions by type, size, enhancement, and attenuation, yet existing 3D methods predict only at the patient or organ level. We reformulate kidney CT characterization as a per-lesion set-prediction task: one model emits a variable number of lesions per kidney, each with four clinical attributes. We curated 2,619 CT volumes from 788 patients at one academic medical center, with multi-granularity side- and per-lesion...
Filter, Then Reweight: Rethinking Optimization Granularity in On-Policy Distillation
Announce Type: new Abstract: On-Policy distillation (OPD) in large language models is shifting from full-trace KL supervision toward more selective training paradigms. Recent OPD methods increasingly focus on selecting which trajectories to learn from, which tokens are most informative, and which supervision signals are most reliable. Motivated by this trend, we rethink optimization granularity of OPD and propose \fireicon\ FiRe-OPD (Filter, then Reweight), which jointly adjusts supervision...