Official platform of Tsinghua Big Data Research Center, sharing the team's latest research, teaching updates, and big data news.
The "Academic Path" lecture series at Tsinghua University taught non‑CS students practical Python techniques—including Requests, AJAX, Selenium, PDF parsing, and multiprocessing—to extract, process, and analyze financial data, culminating in hands‑on case studies of ESG news, A‑share reports, and IFC databases.
This article reviews the survey "Memory in the Age of AI Agents," presenting a comprehensive taxonomy that classifies agent memory by its forms, functions, and dynamic mechanisms, and explores future directions such as generative memory, reinforcement‑learning‑driven management, multimodal storage, and trustworthy handling.
This comprehensive survey examines the emerging field of Causal Reinforcement Learning, classifies its core techniques, introduces eleven benchmark environments, evaluates four novel algorithms, and outlines challenges and future research directions for building robust, generalizable, and interpretable AI systems.
ChatGPT now offers a “Your Year with ChatGPT” feature that generates a personalized annual report summarizing your message count, conversation frequency, punctuation habits, chat style, and even awards you with quirky titles, while also providing a pixel‑art illustration and a simple interactive game.
This article introduces the Spatio‑Temporal‑Frequency Attention Network (STFAN), a deep‑learning model that fuses graph neural networks, attention mechanisms, and frequency‑domain analysis to capture hidden spatial, temporal, and spectral dependencies in traffic data, achieving superior short‑ and long‑term forecasting performance on real‑world datasets.
Gemini 3.0’s publicly released system prompt provides a detailed, step‑by‑step framework—including logical dependencies, risk assessment, abductive reasoning, outcome evaluation, information integration, precision, completeness, persistence and response inhibition—to guide the model toward safer, higher‑quality answers.
This guide walks you through 20 fundamental PyTorch concepts—including tensor creation, operations, autograd, model building, data loading, GPU acceleration, and best‑practice tricks—providing clear code snippets and step‑by‑step explanations so you can quickly prototype, train, and deploy neural networks.
AI video generation now turns textual prompts into high‑quality clips using diffusion models and transformer‑based architectures; this article explains the underlying mathematics, training objectives, spatio‑temporal encoding, breakthroughs like consistent motion and physical realism, and discusses the technology’s opportunities and inherent risks.
This article traces the seventy‑year evolution of Symbolic AI, explains its core physical symbol system hypothesis, contrasts it with connectionist approaches, examines historic milestones such as the Logic Theorist, MYCIN and XCON, discusses the symbol‑grounding problem, and shows how modern neural‑symbolic systems are reviving its relevance in high‑stakes domains requiring accuracy, interpretability and safety.
DeepSeek's V3.2 release introduces a dual‑model lineup, a Sparse Attention architecture that halves long‑context inference cost, a post‑training reinforcement‑learning pipeline that exceeds 10% of pre‑training compute, and a revamped agent framework that dramatically improves tool‑use and reasoning performance across benchmarks.
