Artificial Intelligence (AI) and Large Language Models (LLMs) hold significant promise in revolutionizing healthcare, especially in clinical applications. Simultaneously, Digital Twin technology, which models and simulates complex systems, has gained traction in enhancing patient care. However, despite the advances in experimental clinical settings, the potential of AI and digital twins to streamline clinical operations remains largely untapped. This paper introduces a novel digital twin framework specifically designed to enhance oncology clinical operations. We propose the integration of multiple specialized digital twins, such as the Medical Necessity Twin, Care Navigator Twin, and Clinical History Twin, to enhance workflow efficiency and personalize care for each patient based on their unique data. Furthermore, by synthesizing multiple data sources and aligning them with the National Comprehensive Cancer Network (NCCN) guidelines, we create a dynamic Cancer Care Path, a continuously evolving knowledge base that enables these digital twins to provide precise, tailored clinical recommendations.

Based on the numerical manifold method principle, we developed a mathematical framework of a neural network manifold: Deep Manifold and discovered that neural networks: 1) is numerical computation combining forward and inverse; 2) have near infinite degrees of freedom; 3) exponential learning capacity with depth; 4) have self-progressing boundary conditions; 5) has training hidden bottleneck. We also define two concepts: neural network learning space and deep manifold space and introduce two concepts: neural network intrinsic pathway and fixed point. We raise three fundamental questions: 1). What is the training completion definition; 2). where is the deep learning convergence point (neural network fixed point); 3). How important is token timestamp in training data given negative time is critical in inverse problem.

Idioms represent a ubiquitous vehicle for conveying sentiments in the realm of everyday discourse, rendering the nuanced analysis of idiom sentiment crucial for a comprehensive understanding of emotional expression within real-world texts. Nevertheless, the existing corpora dedicated to idiom sentiment analysis considerably limit research in text sentiment analysis. In this paper, we propose an innovative approach to automatically expand the sentiment lexicon for idioms, leveraging the capabilities of large language models through the application of Chain-of-Thought prompting. To demonstrate the effectiveness of this approach, we integrate multiple existing resources and construct an emotional idiom lexicon expansion dataset (called EmoIdiomE), which encompasses a comprehensive repository of Chinese and English idioms. Then we designed the Dual Chain-of-Thoughts (DualCoTs) method, which combines insights from linguistics and psycholinguistics, to demonstrate the effectiveness of using large models to automatically expand the sentiment lexicon for idioms. Experiments show that DualCoTs is effective in idioms sentiment lexicon expansion in both Chinese and English. For reproducibility, we will release the data and code upon acceptance.

Extracting meaningful insights from large and complex datasets poses significant challenges, particularly in ensuring the accuracy and relevance of retrieved information. Traditional data retrieval methods such as sequential search and index-based retrieval often fail when handling intricate and interconnected data structures, resulting in incomplete or misleading outputs. To overcome these limitations, we introduce Structured-GraphRAG, a versatile framework designed to enhance information retrieval across structured datasets in natural language queries. Structured-GraphRAG utilizes multiple knowledge graphs, which represent data in a structured format and capture complex relationships between entities, enabling a more nuanced and comprehensive retrieval of information. This graph-based approach reduces the risk of errors in language model outputs by grounding responses in a structured format, thereby enhancing the reliability of results. We demonstrate the effectiveness of Structured-GraphRAG by comparing its performance with that of a recently published method using traditional retrieval-augmented generation. Our findings show that Structured-GraphRAG significantly improves query processing efficiency and reduces response times. While our case study focuses on soccer data, the framework's design is broadly applicable, offering a powerful tool for data analysis and enhancing language model applications across various structured domains.

Whether a large language model policy is an explicit constitution or an implicit reward model, it is challenging to assess coverage over the unbounded set of real-world situations that a policy must contend with. We introduce an AI policy design process inspired by mapmaking, which has developed tactics for visualizing and iterating on maps even when full coverage is not possible. With Policy Projector, policy designers can survey the landscape of model input-output pairs, define custom regions (e.g., "violence"), and navigate these regions with rules that can be applied to LLM outputs (e.g., if output contains "violence" and "graphic details," then rewrite without "graphic details"). Policy Projector supports interactive policy authoring using LLM classification and steering and a map visualization reflecting the policy designer's work. In an evaluation with 12 AI safety experts, our system helps policy designers to address problematic model behaviors extending beyond an existing, comprehensive harm taxonomy.

Meta just announced an intriguing tool that uses AI to automatically dub Reels into other languages, complete with lip-sync. This feature was revealed at the annual Meta Connect livestream event and was introduced by CEO Mark Zuckerberg. Zuckerberg showed this off during the keynote, and everything seemed to work flawlessly. The technology not only translates the content, according to Meta, but will also "simulate the speaker's voice in another language and sync their lips to match." It's worth noting, however, that this didn't appear to be a live demo, but was still pretty impressive.

Zelenskyy rejected the China-Brazil six-point plan to end Russia's war and questioned'true' intent. Ukrainian President Volodymyr Zelenskyy took to the podium at the 79th United Nations General Assembly (UNGA) for the third time since Russia's deadly invasion began more than two and half years ago, though this time he took direct aim at nations aiding Moscow: China, North Korea and Iran. Zelenskyy – who has long toed the line when it comes to maintaining murky geopolitical relations amid the war – for the first time called out not only the nations supplying direct arms to Moscow, but those who have remained complacent in their refusal to back Ukraine's demands that Russian President Vladimir Putin withdraw his troops. "We need to make it clear the war is over. This is the peace formula – what part of this could be unacceptable to anyone who upholds the U.N. Charter?" he questioned.

Over the last year, Meta has made its AI assistant so ubiquitous in its apps it's almost hard to believe that Meta AI is only a year old. But, one year after its launch at the last Connect, the company is infusing Meta AI with a load of new features in the hopes that more people will find its assistant useful. One of the biggest changes is that users will be able to have voice chats with Meta AI. Up till now, the only way to speak with Meta AI was via the Ray-Ban Meta smart glasses. And like last year's Meta AI launch, the company tapped a group of celebrities for the change.

As world wide as the web is, language barriers still often limit how much of a site people can explore. Well, Reddit is using AI in an attempt to lessen this issue. The company announced Redditors across more than 35 countries will soon be able to automatically translate their entire feeds. The tool first launched in France earlier this year. The machine learning-powered feature is now available in Brazil and Spain, where Redditors can click a translate icon displayed in the overflow menu.

Japanese scientists have used artificial intelligence to uncover 303 new etchings in Peru's Nazca desert -- doubling the amount of known geoglyphs made some 2,000 years ago by a pre-Inca civilization. The fabled Nazca lines, a series of massive incisions on the desert floor depicting animals, plants, imaginary beings and geometric figures, have fascinated scientists ever since they were first discovered around a century ago. Best viewed from the air, the lines situated some 350 kilometers south of Lima are one of Peru's top tourist attractions.