In this study we analyzed average sleep durations across 61 countries to examine the impact of Daylight Saving Time (DST) practices. Key metrics influencing sleep were identified, and statistical correlation analysis was applied to explore relationships among these factors. Countries were grouped based on DST observance, and visualizations compared sleep patterns between DST and non-DST regions. Results show that, on average, countries observing DST tend to report longer sleep durations than those that do not. A more detailed pattern emerged when accounting for latitude: at lower latitudes, DST-observing countries reported shorter sleep durations compared to non-DST countries, while at higher latitudes, DST-observing countries reported longer average sleep durations. These findings suggest that the influence of DST on sleep may be moderated by geographical location.

Radio remains a pervasive medium for mass information dissemination, with AM/FM stations reaching more Americans than either smartphone-based social networking or live television. Increasingly, radio broadcasts are also streamed online and accessed over the Internet. We present WavePulse, a framework that records, documents, and analyzes radio content in real-time. While our framework is generally applicable, we showcase the efficacy of WavePulse in a collaborative project with a team of political scientists focusing on the 2024 Presidential Elections. We use WavePulse to monitor livestreams of 396 news radio stations over a period of three months, processing close to 500,000 hours of audio streams. These streams were converted into time-stamped, diarized transcripts and analyzed to track answer key political science questions at both the national and state levels. Our analysis revealed how local issues interacted with national trends, providing insights into information flow. Our results demonstrate WavePulse's efficacy in capturing and analyzing content from radio livestreams sourced from the Web. Code and dataset can be accessed at \url{https://wave-pulse.io}.

One thing seems for sure: people are not ready for "digital workers" just yet. That's the lesson learned by Sarah Franklin, the CEO of Lattice, a human resources and performance management platform that offers performance coaching, talent reviews, onboarding automation, compensation management and a host of other HR tools to more than 5,000 organizations around the world. What is a digital employee? According to Franklin, it's avatars like Devin the engineer, Harvey the lawyer, Einstein the service agent and Piper the sales agent who have "entered the workforce and become our colleagues". But these are not real workers.

Large language models have been widely adopted in natural language processing, yet they face the challenge of generating unreliable content. Recent works aim to reduce misinformation and hallucinations by resorting to attribution as a means to provide evidence (i.e., citations). However, current attribution methods usually focus on the retrieval stage and automatic evaluation that neglect mirroring the citation mechanisms in human scholarly writing to bolster credibility. In this paper, we address these challenges by modelling the attribution task as preference learning and introducing an Automatic Preference Optimization (APO) framework. First, we create a curated collection for post-training with 6,330 examples by collecting and filtering from existing datasets. Second, considering the high cost of labelling preference data, we further propose an automatic method to synthesize attribution preference data resulting in 95,263 pairs. Moreover, inspired by the human citation process, we further propose a progressive preference optimization method by leveraging fine-grained information. Extensive experiments on three datasets (i.e., ASQA, StrategyQA, and ELI5) demonstrate that APO achieves state-of-the-art citation F1 with higher answer quality.

The potential of using a large language model (LLM) as a knowledge base (KB) has sparked significant interest. To manage the knowledge acquired by LLMs, we need to ensure that the editing of learned facts respects internal logical constraints, which are known as dependency of knowledge. Existing work on editing LLMs has partially addressed the issue of dependency, when the editing of a fact should apply to its lexical variations without disrupting irrelevant ones. However, they neglect the dependency between a fact and its logical implications. We propose an evaluation protocol with an accompanying question-answering dataset, DepEdit, that provides a comprehensive assessment of the editing process considering the above notions of dependency. Our protocol involves setting up a controlled environment in which we edit facts and monitor their impact on LLMs, along with their implications based on If-Then rules. Extensive experiments on DepEdit show that existing knowledge editing methods are sensitive to the surface form of knowledge, and that they have limited performance in inferring the implications of edited facts.

This paper introduces Track Mix, a personalized playlist generation system released in 2022 on the music streaming service Deezer. Track Mix automatically generates "mix" playlists inspired by initial music tracks, allowing users to discover music similar to their favorite content. To generate these mixes, we consider a Transformer model trained on millions of track sequences from user playlists. In light of the growing popularity of Transformers in recent years, we analyze the advantages, drawbacks, and technical challenges of using such a model for mix generation on the service, compared to a more traditional collaborative filtering approach. Since its release, Track Mix has been generating playlists for millions of users daily, enhancing their music discovery experience on Deezer.

Artificial intelligence (AI) is becoming ubiquitous. It provides directions while we drive, answers our questions, offers music recommendations and powers a growing number of business processes in the workplace. In fact, AI is working its way into so many aspects of our personal and professional lives that my company has begun to refer to it as "everyday AI." Soon, I'd argue, it will become as ubiquitous -- and necessary -- as electricity. Yet, despite the progress, we've only scratched the surface in the potential ways that AI can, and no doubt will, change business and the world. Gartner has forecast that it will take until 2025 for half of organizations worldwide to reach what Gartner's AI maturity model describes as the "stabilization stage" of AI maturity or beyond.

Artificial intelligence (AI) is becoming ubiquitous. It provides directions while we drive, answers our questions, offers music recommendations and powers a growing number of business processes in the workplace. In fact, AI is working its way into so many aspects of our personal and professional lives that my company has begun to refer to it as "everyday AI." Soon, I'd argue, it will become as ubiquitous -- and necessary -- as electricity. Yet, despite the progress, we've only scratched the surface in the potential ways that AI can, and no doubt will, change business and the world. Gartner has forecast that it will take until 2025 for half of organizations worldwide to reach what Gartner's AI maturity model describes as the "stabilization stage" of AI maturity or beyond.

On a May evening in 1959, C.P. Snow, a popular novelist and former research scientist, gave a lecture before a gathering of dons and students at the University of Cambridge, his alma mater. He called his talk "The Two Cultures and the Scientific Revolution." Snow declared that a gulf of mutual incomprehension divided literary intellectuals and scientists. "The non-scientists have a rooted impression that the scientists are shallowly optimistic, unaware of man's condition," Snow said. "On the other hand, the scientists believe that the literary intellectuals are totally lacking in foresight, peculiarly unconcerned with their brother men, in a deep sense anti-intellectual, anxious to restrict both art and thought to the existential moment." Snow didn't expect much of his talk.

That's what often happens when people take on debt. Shakespeare famously warned us about the dangers of going into debt, stating that "neither a borrower nor a lender is; for loan doth oft lose both itself and friend." For some people that become swamped by debt, they find themselves sinking into an all-encompassing abyss. The debt impacts them, their loved ones, their friends and acquaintances, and otherwise bodes for indubitably rotten tidings. Sometimes, a person is oblivious to their mounting debt. I know that seems nearly impossible to imagine. What can happen is that the person starts making zillions of charges on their credit cards, and they meanwhile delude themselves into not realizing there will ultimately be unfavorable consequences. The piper, though, will eventually need to be paid. Credit card agencies often make life especially easy for those that wantonly are apt to spend.