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.
The article explains what digital humans are, reviews the evolution of 3D modeling techniques—from early 2D hand‑drawn methods and 3DMM to deep‑learning‑based implicit models like NeRF—and discusses current challenges and future research directions.
This article introduces OpenAI’s CLIP multimodal model, explains its architecture and contrastive training, details hardware and installation steps, and demonstrates a hands‑on zero‑shot image classification workflow that achieves 97% confidence on a cat image without any task‑specific fine‑tuning.
This article walks through Amap's Model Context Protocol (MCP) service, explaining its purpose, installation steps, configuration in the Cursor client, and practical examples such as travel planning and location‑based queries, while also evaluating its strengths and current limitations.
This article surveys current network load‑balancing approaches—including CONGA, Hula, DRILL, Hermes, MP‑RDMA, ConWeave, Proteus, and CAVER—detailing their granularity, information exchange, signaling methods, and the performance gains they achieve in modern data‑center environments.
This article walks through exporting a PyTorch image‑classification model to ONNX, identifying a hidden layer node, adding it as an output, and using ONNX Runtime to retrieve both the final prediction and the intermediate feature tensor.
This article walks through using DeepQueueNet—a deep‑learning‑enhanced network performance estimator—to set up a device model, train the PyTorch version, configure a fattree16 topology, and run multi‑GPU simulations that deliver minute‑level, packet‑accurate results in as little as 1 minute 27 seconds.
The article recounts the authors' participation in EuroSys'25 in Rotterdam, detailing the conference schedule, their presentation of the Atlas network verification paper, technical insights into distributed verification, interactions with peers, and memorable social and cultural experiences during the five‑day event.
The article analyzes the Model Context Protocol (MCP) as an emerging open standard that unifies how applications provide context to large language models, reviews its rapid ecosystem growth, highlights security and performance challenges, and discusses future directions such as vertical small‑model opportunities and broader protocol integrations.
The article analyzes the anti‑scaling phenomenon in video large‑language models, identifies a “temporal hacking” shortcut where models focus on a few key frames, formalizes it via reward‑hacking theory, introduces the Temporal Perplexity (TPL) metric, and proposes an Unhackable Temporal Rewarding (UTR) framework to mitigate the issue.
This guide introduces Hugging Face's TRL library, explains how to install it alongside Transformers, and walks through modifying LLaVA's trainer, dataset, and data collator to apply the DPO reinforcement‑learning algorithm for multimodal model fine‑tuning.
