NIRC is based on the National Key Laboratory of Network and Switching Technology at Beijing University of Posts and Telecommunications. It has built a technology matrix across four AI domains—intelligent cloud networking, natural language processing, computer vision, and machine learning systems—dedicated to solving real‑world problems, creating top‑tier systems, publishing high‑impact papers, and contributing significantly to the rapid advancement of China's network technology.
Perception‑R1, a post‑training framework that applies rule‑based reinforcement learning to existing multimodal LLMs, dramatically improves visual perception tasks such as grounding, OCR, counting and object detection, as demonstrated by extensive benchmarks and ablation studies.
The article shares a hands‑on review of Claude Code, a terminal‑based AI coding assistant, covering setup with custom API endpoints, the use of Claude.md for project context, subagent parallel development, comparisons with GeminiCLI and Cursor, and practical tips for effective use.
Dexterous hand grasping, a core challenge in embodied intelligence due to high degrees of freedom, is categorized into three approaches—ego-centric, object-centric, and ego-object interaction—each with distinct trade-offs, and the article cites representative works such as UniDexGrasp, GraspTTA, and DRO_Grasp.
This article explains the DCQCN end‑to‑end congestion control algorithm for RoCEv2, describes its three roles (RP, CP, NP), and provides detailed NS‑3 code implementations for ECN marking, CNP generation, and rate‑adjustment logic, including scheduling and parameter calculations.
The article compares three cross‑platform file‑synchronization solutions—LocalSend for LAN point‑to‑point transfers, Syncthing for decentralized multi‑device syncing, and JianGuoyun's WebDAV cloud—detailing their features, limitations, and practical usage tips.
This article introduces OpenAI's Triton language, compares its block‑wise programming model to traditional CUDA, walks through vector‑addition and fused‑softmax kernel implementations, and presents benchmark results that demonstrate significant speedups over native PyTorch operations.
This guide walks through the Hugging Face Transformers Trainer library, explaining its core features such as configurable training loops, mixed‑precision and gradient‑accumulation support, seamless distributed training via Accelerate and DeepSpeed, and provides a step‑by‑step example of converting a simple PyTorch CNN model to use Trainer.
This article reviews the V2X‑ViT collaborative perception framework for autonomous driving, detailing its end‑to‑end pipeline, the novel HMSA and MSwin attention mechanisms, and the delay‑aware positional encoding that together enable high‑accuracy 3D object detection across vehicles and infrastructure.
This article walks through TensorRT's core optimization features, auxiliary debugging tools, and a step‑by‑step SMPLer‑X case study, showing how graph simplification, mixed‑precision, and engine generation cut inference latency to roughly 22‑29% of the original runtime.
The paper presents NLI2Conf, an intent‑driven network configuration model that fuses configuration files, topology and performance data via a multimodal interface, using large language and graph neural models to align natural‑language intents with forwarding and performance constraints, achieving 96.6% accuracy and a 26‑fold reduction in manual effort.
