Green indicates factual, red indicates nonfactual, and striked text indicates repetition. So ci ety es ti mates that more than 228,000 peo ple will be di ag nosed with lung can cer in the United... That would make an oxygen mask one of the more popular treatments for this devastating disease. It helps ease breathing and give patients back their strength. The symptoms of lung cancer may resemble those of a bad cold or pneumonia.

Major Depressive Disorder is one of the leading causes of disability worldwide, yet its diagnosis still depends largely on subjective clinical assessments. Integrating Artificial Intelligence (AI) holds promise for developing objective, scalable, and timely diagnostic tools. In this paper, we present a comprehensive survey of state-of-the-art AI methods for depression detection and diagnosis, based on a systematic review of 55 key studies. We introduce a novel hierarchical taxonomy that structures the field by primary clinical task (diagnosis vs. prediction), data modality (text, speech, neuroimaging, multimodal), and computational model class (e.g., graph neural networks, large language models, hybrid approaches). Our in-depth analysis reveals three major trends: the predominance of graph neural networks for modeling brain connectivity, the rise of large language models for linguistic and conversational data, and an emerging focus on multimodal fusion, explainability, and algorithmic fairness. Alongside methodological insights, we provide an overview of prominent public datasets and standard evaluation metrics as a practical guide for researchers. By synthesizing current advances and highlighting open challenges, this survey offers a comprehensive roadmap for future innovation in computational psychiatry.

Game-theoretic interactions between agents with Large Language Models (LLMs) have revealed many emergent capabilities, yet the linguistic diversity of these interactions has not been sufficiently quantified. In this paper, we present the Conversational Robustness Evaluation Score: CORE, a metric to quantify the effectiveness of language use within multi-agent systems across different game-theoretic interactions. CORE integrates measures of cluster entropy, lexical repetition, and semantic similarity, providing a direct lens of dialog quality. We apply CORE to pairwise LLM dialogs across competitive, cooperative, and neutral settings, further grounding our analysis in Zipf's and Heaps' Laws to characterize word frequency distributions and vocabulary growth. Our findings show that cooperative settings exhibit both steeper Zipf distributions and higher Heap exponents, indicating more repetition alongside greater vocabulary expansion. In contrast, competitive interactions display lower Zipf and Heaps exponents, reflecting less repetition and more constrained vocabularies. These results provide new insights into how social incentives influence language adaptation, and highlight CORE as a robust diagnostic for measuring linguistic robustness in multi-agent LLM systems. Our code is available at https://github.com/psyonp/core.

Business interview preparation demands both solid theoretical grounding and refined soft skills, yet conventional classroom methods rarely deliver the individualized, culturally aware practice employers currently expect. This paper introduces SimInterview, a large language model (LLM)-based simulated multilingual interview training system designed for business professionals entering the AI-transformed labor market. Our system leverages an LLM agent and synthetic AI technologies to create realistic virtual recruiters capable of conducting personalized, real-time conversational interviews. The framework dynamically adapts interview scenarios using retrieval-augmented generation (RAG) to match individual resumes with specific job requirements across multiple languages. Built on LLMs (OpenAI o3, Llama 4 Maverick, Gemma 3), integrated with Whisper speech recognition, GPT-SoVITS voice synthesis, Ditto diffusion-based talking head generation model, and ChromaDB vector databases, our system significantly improves interview readiness across English and Japanese markets. Experiments with university-level candidates show that the system consistently aligns its assessments with job requirements, faithfully preserves resume content, and earns high satisfaction ratings, with the lightweight Gemma 3 model producing the most engaging conversations. Qualitative findings revealed that the standardized Japanese resume format improved document retrieval while diverse English resumes introduced additional variability, and they highlighted how cultural norms shape follow-up questioning strategies. Finally, we also outlined a contestable AI design that can explain, detect bias, and preserve human-in-the-loop to meet emerging regulatory expectations.

"I don't feel particularly unhappy about my work being used to train AI," says Japanese novelist Rie Qudan. "Even if it is copied, I feel confident there's a part of me that will remain, which nobody can copy." The 34-year old author is talking to me via Zoom from her home near Tokyo, ahead of the publication of the English-language translation of her fourth novel, Sympathy Tower Tokyo. The book attracted controversy in Japan when it won a prestigious prize, despite being partly written by ChatGPT. At the heart of Sympathy Tower Tokyo is a Japanese architect, Sara Machina, who has been commissioned to build a new tower to house convicted criminals. It will be a representation of what one character – not without irony – calls "the extraordinary broadmindedness of the Japanese people", in that the tower will house offenders in compassionate comfort.

People looking for precursors to artificial intelligence often point to science fiction by authors like Isaac Asimov or thought experiments like the Turing test. But an equally important, if surprising and less appreciated, forerunner is Skinner's research with pigeons in the middle of the 20th century. Skinner believed that association--learning, through trial and error, to link an action with a punishment or reward--was the building block of every behavior, not just in pigeons but in all living organisms, including human beings. His "behaviorist" theories fell out of favor with psychologists and animal researchers in the 1960s but were taken up by computer scientists who eventually provided the foundation for many of the artificial-intelligence tools from leading firms like Google and OpenAI. These companies' programs are increasingly incorporating a kind of machine learning whose core concept--reinforcement--is taken directly from Skinner's school of psychology and whose main architects, the computer scientists Richard Sutton and Andrew Barto, won the 2024 Turing Award, an honor widely considered to be the Nobel Prize of computer science.

Breakthroughs, discoveries, and DIY tips sent every weekday. Any pet parent knows that our furry friends can get into accidents. While some like rolling around in the mud are mainly a nuisance, ingesting something that they shouldn't can be very dangerous. In a study published August 18 in the journal Frontiers in Veterinary Science, Doctor Jake Johnson, a cardiology resident at North Carolina State University's College of Veterinary Medicine, presents a case study of a chihuahua that accidentally ingested cocaine. Ahead of the study's publication, the team at Frontiers conducted this Q&A with Dr. Johnson.

Desination will be a red barn on the right 1. Continued on next page 18 Use Case Example rewrite Rewrite the following text to be more light-hearted: -- {very formal text} -- chat The following is a conversation with an AI assistant.

Theorem 1. Suppose that ˆ X In DB models, the commonly used p is either 1 or 2. When p = 2, DURA takes the form as the one in Equation (8) in the main text. If p = 1, we cannot expand the squared score function of the associated DB models as in Equation (4). Therefore, we choose p = 2 . 2 Table 2: Hyperparameters found by grid search. Suppose that k is the number of triplets known to be true in the knowledge graph, n is the embedding dimension of entities. That is to say, the computational complexity of weighted DURA is the same as the weighted squared Frobenius norm regularizer.

Causal traces show that the last token of the subject name is not always decisive.