ACHEM delivers the first end-to-end software I/Q-level channel and radio emulator for SDR systems, supporting MIMO, mobility, heterogeneous rates, and integration with GNU Radio, srsRAN, and OpenAirInterface.
Prototyping next-generation O-RAN research testbeds with SDRs
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EmbGen creates synthetic QA data by entity decomposition, embedding-based reassembly into clusters, and multi-level sampling with cluster-specific prompts, yielding up to 88.9% higher Binary Accuracy than baselines on heterogeneous datasets under fixed token budgets.
Holi-DETR improves fashion item detection by integrating co-occurrence probabilities, inter-item spatial arrangements, and body keypoint relationships into the DETR architecture.
PF-CD3Q uses online particle filtering to estimate fatigue parameters and constrains a deep Q-learning agent to solve fatigue-aware human-robot task planning as a CMDP.
LLM agents use a Cartesian split between learned prediction and engineered control, enabling modularity but creating sensitivity and bottlenecks unlike integrated biological systems.
citing papers explorer
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ACHEM: A Real-Time Digital Twin Framework with Channel and Radio Emulation
ACHEM delivers the first end-to-end software I/Q-level channel and radio emulator for SDR systems, supporting MIMO, mobility, heterogeneous rates, and integration with GNU Radio, srsRAN, and OpenAirInterface.
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EmbGen: Teaching with Reassembled Corpora
EmbGen creates synthetic QA data by entity decomposition, embedding-based reassembly into clusters, and multi-level sampling with cluster-specific prompts, yielding up to 88.9% higher Binary Accuracy than baselines on heterogeneous datasets under fixed token budgets.
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Holi-DETR: Holistic Fashion Item Detection Leveraging Contextual Information
Holi-DETR improves fashion item detection by integrating co-occurrence probabilities, inter-item spatial arrangements, and body keypoint relationships into the DETR architecture.
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Safe reinforcement learning with online filtering for fatigue-predictive human-robot task planning and allocation in production
PF-CD3Q uses online particle filtering to estimate fatigue parameters and constrains a deep Q-learning agent to solve fatigue-aware human-robot task planning as a CMDP.
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The Cartesian Cut in Agentic AI
LLM agents use a Cartesian split between learned prediction and engineered control, enabling modularity but creating sensitivity and bottlenecks unlike integrated biological systems.