First provide a summary of the paper, and then address the following criteria: Quality, clarity, originality and significance. Quality and originality: The main claims of the authors make intuitive sense. Specifically, figure 1 presents a generative model which separates note onsets from activation and spectral information. This is in keeping with the physics of a piano, where a pianist initiates a note onset by sending the hammer in free-flight. Those harmonics change over time based both on decay and on the piano's physical dampers.

AI chip giant Nvidia announced last week that it would invest $100 billion to help OpenAI, the front-runner in generative AI, build data centers. The staggering investments in artificial intelligence keep coming: Last week, AI chip giant Nvidia announced it would invest $100 billion to help OpenAI, the front-runner in generative AI, build data centers. How are these enormous sums possible when the returns on investments, at least for now, pale in comparison? AI-related spending is soaring worldwide, expected to reach approximately $1.5 trillion by 2025, according to U.S. research firm Gartner, and over $2 trillion in 2026 -- nearly 2% of global GDP. In a time of both misinformation and too much information, quality journalism is more crucial than ever.

Neural Information Processing Systems http://nips.cc/

In this work, we propose Glow-TTS, a flow-based generative model for parallel TTS that does not require any external aligner.

We study the problem of determining whether a piece of text has been authored by a human or by a large language model (LLM). Existing state of the art logits-based detectors make use of statistics derived from the log-probability of the observed text evaluated using the distribution function of a given source LLM. However, relying solely on log probabilities can be sub-optimal. In response, we introduce AdaDetectGPT -- a novel classifier that adaptively learns a witness function from training data to enhance the performance of logits-based detectors. We provide statistical guarantees on its true positive rate, false positive rate, true negative rate and false negative rate. Extensive numerical studies show AdaDetectGPT nearly uniformly improves the state-of-the-art method in various combination of datasets and LLMs, and the improvement can reach up to 58%. A python implementation of our method is available at https://github.com/Mamba413/AdaDetectGPT.

The paper asserts that emulating empathy in human-robot interaction is a key component to achieve satisfying social, trustworthy, and ethical robot interaction with older people. Following comments from older adult study participants, the paper identifies a gap. Despite the acceptance of robot care scenarios, participants expressed the poor quality of the social aspect. Current human-robot designs, to a certain extent, neglect to include empathy as a theorized design pathway. Using rhetorical theory, this paper defines the socio-cultural expectations for convincing empathetic relationships. It analyzes and then summarizes how society understands, values, and negotiates empathic interaction between human companions in discursive exchanges, wherein empathy acts as a societal value system. Using two public research collections on robots, with one geared specifically to gerontechnology for older people, it substantiates the lack of attention to empathy in public materials produced by robot companies. This paper contends that using an empathetic care vocabulary as a design pathway is a productive underlying foundation for designing humanoid social robots that aim to support older people's goals of aging-in-place. It argues that the integration of affective AI into the sociotechnical assemblages of human-socially assistive robot interaction ought to be scrutinized to ensure it is based on genuine cultural values involving empathetic qualities.

Schemas play a vital role in ensuring data quality and supporting usability in the Semantic Web and natural language processing. Traditionally, their creation demands substantial involvement from knowledge engineers and domain experts. Leveraging the impressive capabilities of large language models (LLMs) in tasks like ontology engineering, we explore schema generation using LLMs. To bridge the resource gap, we introduce two datasets: YAGO Schema and Wikidata EntitySchema, along with novel evaluation metrics. The LLM-based pipelines utilize local and global information from knowledge graphs (KGs) to generate schemas in Shape Expressions (ShEx). Experiments demonstrate LLMs' strong potential in producing high-quality ShEx schemas, paving the way for scalable, automated schema generation for large KGs. Furthermore, our benchmark introduces a new challenge for structured generation, pushing the limits of LLMs on syntactically rich formalisms.

We present BiasLab, a dataset of 300 political news articles annotated for perceived ideological bias. These articles were selected from a curated 900-document pool covering diverse political events and source biases. Each article is labeled by crowdworkers along two independent scales, assessing sentiment toward the Democratic and Republican parties, and enriched with rationale indicators. The annotation pipeline incorporates targeted worker qualification and was refined through pilot-phase analysis. We quantify inter-annotator agreement, analyze misalignment with source-level outlet bias, and organize the resulting labels into interpretable subsets. Additionally, we simulate annotation using schema-constrained GPT-4o, enabling direct comparison to human labels and revealing mirrored asymmetries, especially in misclassifying subtly right-leaning content. We define two modeling tasks: perception drift prediction and rationale type classification, and report baseline performance to illustrate the challenge of explainable bias detection. BiasLab's rich rationale annotations provide actionable interpretations that facilitate explainable modeling of political bias, supporting the development of transparent, socially aware NLP systems. We release the dataset, annotation schema, and modeling code to encourage research on human-in-the-loop interpretability and the evaluation of explanation effectiveness in real-world settings.

Road traffic accidents remain a major public safety and socio-economic issue in developing countries like Bangladesh. Existing accident data collection is largely manual, fragmented, and unreliable, resulting in underreporting and inconsistent records. This research proposes a fully automated system using Large Language Models (LLMs) and web scraping techniques to address these challenges. The pipeline consists of four components: automated web scraping code generation, news collection from online sources, accident news classification with structured data extraction, and duplicate removal. The system uses the multimodal generative LLM Gemini-2.0-Flash for seamless automation. The code generation module classifies webpages into pagination, dynamic, or infinite scrolling categories and generates suitable Python scripts for scraping. LLMs also classify and extract key accident information such as date, time, location, fatalities, injuries, road type, vehicle types, and pedestrian involvement. A deduplication algorithm ensures data integrity by removing duplicate reports. The system scraped 14 major Bangladeshi news sites over 111 days (Oct 1, 2024 - Jan 20, 2025), processing over 15,000 news articles and identifying 705 unique accidents. The code generation module achieved 91.3% calibration and 80% validation accuracy. Chittagong reported the highest number of accidents (80), fatalities (70), and injuries (115), followed by Dhaka, Faridpur, Gazipur, and Cox's Bazar. Peak accident times were morning (8-9 AM), noon (12-1 PM), and evening (6-7 PM). A public repository was also developed with usage instructions. This study demonstrates the viability of an LLM-powered, scalable system for accurate, low-effort accident data collection, providing a foundation for data-driven road safety policymaking in Bangladesh.