Supreme Group has consolidated two of the companies it bought during a nine-deal, 18-month splurge, combining Amendola Communications and Health+Commerce to form a PR and communications agency.
Conditional Morphogenesis: Emergent Generation of Structural Digits via Neural Cellular Automata
arXiv:2512.08360v1 Announce Type: cross Abstract: Biological systems exhibit remarkable morphogenetic plasticity, where a single genome can encode various specialized cellular structures triggered by local chemical signals. In the domain of Deep Learning, Differentiable Neural Cellular Automata (NCA) have emerged as a paradigm to mimic this self-organization. However, existing NCA research has predominantly focused on continuous […]
ClinicalTrialsHub: Bridging Registries and Literature for Comprehensive Clinical Trial Access
arXiv:2512.08193v1 Announce Type: cross Abstract: We present ClinicalTrialsHub, an interactive search-focused platform that consolidates all data from ClinicalTrials.gov and augments it by automatically extracting and structuring trial-relevant information from PubMed research articles. Our system effectively increases access to structured clinical trial data by 83.8% compared to relying on ClinicalTrials.gov alone, with potential to make access […]
Model-Based Diffusion Sampling for Predictive Control in Offline Decision Making
arXiv:2512.08280v1 Announce Type: cross Abstract: Offline decision-making requires synthesizing reliable behaviors from fixed datasets without further interaction, yet existing generative approaches often yield trajectories that are dynamically infeasible. We propose Model Predictive Diffuser (MPDiffuser), a compositional model-based diffusion framework consisting of: (i) a planner that generates diverse, task-aligned trajectories; (ii) a dynamics model that enforces […]
Restrictive Hierarchical Semantic Segmentation for Stratified Tooth Layer Detection
arXiv:2512.07984v1 Announce Type: cross Abstract: Accurate understanding of anatomical structures is essential for reliably staging certain dental diseases. A way of introducing this within semantic segmentation models is by utilising hierarchy-aware methodologies. However, existing hierarchy-aware segmentation methods largely encode anatomical structure through the loss functions, providing weak and indirect supervision. We introduce a general framework […]
ByteStorm: a multi-step data-driven approach for Tropical Cyclones detection and tracking
arXiv:2512.07885v1 Announce Type: cross Abstract: Accurate tropical cyclones (TCs) tracking represents a critical challenge in the context of weather and climate science. Traditional tracking schemes mainly rely on subjective thresholds, which may introduce biases in their skills on the geographical region of application. We present ByteStorm, an efficient data-driven framework for reconstructing TC tracks without […]
CFD-copilot: leveraging domain-adapted large language model and model context protocol to enhance simulation automation
arXiv:2512.07917v1 Announce Type: cross Abstract: Configuring computational fluid dynamics (CFD) simulations requires significant expertise in physics modeling and numerical methods, posing a barrier to non-specialists. Although automating scientific tasks with large language models (LLMs) has attracted attention, applying them to the complete, end-to-end CFD workflow remains a challenge due to its stringent domain-specific requirements. We […]
Short-Context Dominance: How Much Local Context Natural Language Actually Needs?
arXiv:2512.08082v1 Announce Type: cross Abstract: We investigate the short-context dominance hypothesis: that for most sequences, a small local prefix suffices to predict their next tokens. Using large language models as statistical oracles, we measure the minimum context length (MCL) needed to reproduce accurate full-context predictions across datasets with sequences of varying lengths. For sequences with […]
LayerPipe2: Multistage Pipelining and Weight Recompute via Improved Exponential Moving Average for Training Neural Networks
arXiv:2512.08160v1 Announce Type: cross Abstract: In our prior work, LayerPipe, we had introduced an approach to accelerate training of convolutional, fully connected, and spiking neural networks by overlapping forward and backward computation. However, despite empirical success, a principled understanding of how much gradient delay needs to be introduced at each layer to achieve desired level […]
SpeechQualityLLM: LLM-Based Multimodal Assessment of Speech Quality
arXiv:2512.08238v1 Announce Type: cross Abstract: Objective speech quality assessment is central to telephony, VoIP, and streaming systems, where large volumes of degraded audio must be monitored and optimized at scale. Classical metrics such as PESQ and POLQA approximate human mean opinion scores (MOS) but require carefully controlled conditions and expensive listening tests, while learning-based models […]