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.
This article introduces Git as a distributed version‑control tool, explains what it does, walks through installing it on Windows, macOS, and Linux, shows how to configure a user name and email, and provides the essential commands for initializing repositories, committing changes, and working with remote repositories.
The article introduces the classic four‑quadrant (Eisenhower) time‑management framework, explains each quadrant with concrete research and daily‑life examples, and provides a three‑step practical guide to help graduate students prioritize tasks, reduce anxiety, and focus on what truly matters.
The article explains why default RGB colors often look harsh in scientific graphics, how better color choices improve readability and visual impact, and introduces six online tools—MyColor.Space, Colormind, Coolors, Adobe Color, Huemint, and Khroma—detailing their main features and providing direct links for quick adoption.
By 2026, leading AI labs have turned large language models from simple chat tools into task‑execution engines through three upgrades—enhanced reasoning, built‑in multimodal perception, and autonomous agents—while open‑source projects accelerate the shift toward a digital operating system.
Google NotebookLM, built on Gemini 1.5 Pro, lets researchers upload PDFs, PPTs, text or audio and receive AI‑generated summaries, slide decks, mind maps and audio overviews that are tightly anchored to the original documents through clickable citation markers, dramatically improving workflow and credibility.
The article presents INCS, a novel framework that combines graph neural networks and deep reinforcement learning to achieve protocol‑agnostic, millisecond‑level, globally optimized network configuration synthesis, addressing scalability, protocol dependence, and lack of optimization in traditional SMT‑based methods, and demonstrates its superior performance on large‑scale topologies.
The article dissects DeepSeek’s new Engram architecture, which separates computation from memory by using a large, cheap‑RAM‑based lookup table to store factual knowledge, allowing the transformer’s compute layers to focus on reasoning, dramatically reducing GPU memory demand while improving code, math, and long‑context performance.
veRoCE is a ByteDance‑engineered RDMA protocol that retains RoCEv2 compatibility while adding multi‑path transmission, native out‑of‑order delivery, efficient loss detection and selective retransmission, allowing high‑performance clusters to operate over lossy Ethernet without PFC.
RecFlow, a new inference engine from Beijing University of Posts and Telecommunications and Meituan, tackles the resource mismatch of DLRM models by coordinating embedding and DNN operators at the intra‑SM level and introducing interference‑aware adaptive scheduling and incremental batching, achieving up to 9.34× higher throughput on RTX 3090.
DiffNBR introduces a dual‑path diffusion framework combined with an information‑bottleneck mechanism to jointly model spatial co‑occurrence and temporal evolution in next‑basket recommendation, achieving state‑of‑the‑art performance and effectively disentangling repetitive and exploratory purchase patterns.
