We present VIBE, a two-stage Transformer that fuses multi-modal video, audio, and text features to predict fMRI activity. Representations from open-source models (Qwen2.5, BEATs, Whisper, SlowFast, V-JEPA) are merged by a modality-fusion transformer and temporally decoded by a prediction transformer with rotary embeddings. Trained on 65 hours of movie data from the CNeuroMod dataset and ensembled across 20 seeds, VIBE attains mean parcel-wise Pearson correlations of 0.3225 on in-distribution Friends S07 and 0.2125 on six out-of-distribution films. An earlier iteration of the same architecture obtained 0.3198 and 0.2096, respectively, winning Phase-1 and placing second overall in the Algonauts 2025 Challenge.

This is a post-peer-review version of an article published in Applied Soft Computing . This manuscript is made available under the Elsevier user license. Published in: Applied Soft Computing, 2025. Abstract Over the past decade, recommender systems have experienced a surge in popularity. Despite notable progress, they grapple with challenging issues, such as high data dimensionality and sparseness. Representing users and items as low-dimensional embeddings learned via neural networks has become a leading solution. However, while recent studies show promising results, many approaches rely on complex architectures or require content data, which may not always be available. This paper presents Interact2Vec, a novel neural network-based model that simultaneously learns distributed embeddings for users and items while demanding only implicit feedback. The model employs state-of-the-art strategies that natural language processing models commonly use to optimize the training phase and enhance the final embeddings. Two types of experiments were conducted regarding the extrinsic and intrinsic quality of the model. In the former, we benchmarked the recommendations generated by Interact2Vec's embeddings in a top-N ranking problem, comparing them with six other recommender algorithms. The model achieved the second or third-best results in 30% of the datasets, being competitive with other recommenders, and has proven to be very efficient with an average training time reduction of 274% compared to other embedding-based models. Later, we analyzed the intrinsic quality of the embeddings through similarity tables. Our findings suggest that Interact2Vec can achieve promising results, especially on the extrinsic task, and is an excellent embedding-generator model for scenarios of scarce computing resources, enabling the learning of item and user embeddings simultaneously and efficiently. Keywords: recommender systems, collaborative filtering, distributed vector representation, embeddings1. Introduction As technology advances and content becomes increasingly accessible, a growing volume of data is generated and shared daily. While this has led to numerous advancements in the modern world, the sheer magnitude of information means that only a fraction is relevant to individual users.

The growing popularity of large language models has raised concerns regarding the potential to misuse AI-generated text (AIGT). It becomes increasingly critical to establish an excellent AIGT detection method with high generalization and robustness. However, existing methods either focus on model generalization or concentrate on robustness. The unified mechanism, to simultaneously address the challenges of generalization and robustness, is less explored. In this paper, we argue that robustness can be view as a specific form of domain shift, and empirically reveal an intrinsic mechanism for model generalization of AIGT detection task. Then, we proposed a novel AIGT detection method (DP-Net) via dynamic perturbations introduced by a reinforcement learning with elaborated reward and action. Experimentally, extensive results show that the proposed DP-Net significantly outperforms some state-of-the-art AIGT detection methods for generalization capacity in three cross-domain scenarios. Meanwhile, the DP-Net achieves best robustness under two text adversarial attacks. The code is publicly available at https://github.com/CAU-ISS-Lab/AIGT-Detection-Evade-Detection/tree/main/DP-Net.

Fox News anchor Bret Baier examines the U.S. power supply on'Special Report.' A new study shows that a third of American teenagers prefer chatting with artificial intelligence companions over having real friends. Common Sense Media's report, titled "Talk, Trust, and Trade-Offs: How and Why Teens Use AI Companions," revealed that the most widespread uses of AI are aged 13-17. The report explained further that the "use of AI companions is not a niche interest, but rather mainstream teen behavior" and that teens "find conversations with AI companions to be as satisfying or more satisfying than those with real-life friends." Common Sense Media's report, titled "Talk, Trust, and Trade-Offs: How and Why Teens Use AI Companions," revealed that the most widespread uses of AI are aged 13-17.

Psychedelic rock band The Velvet Sundown has over a million monthly listeners on Spotify and earns thousands of dollars every month. However, the catch is that it's not a traditional band at all. It's mostly made by artificial intelligence. Their Spotify bio confirms that the group is a synthetic music project, guided by human creative direction but composed, voiced, and visualized using AI. This is a sign of where music may be headed.

Breakthroughs, discoveries, and DIY tips sent every weekday. Since its debut in 2007, the Amazon Kindle has changed reading habits for millions of people. E-readers aren't for everyone, but they mean you can take hundreds of books with you on one device, look up words instantly, get new reading material in seconds, and take advantage of all the other benefits of digital reading. The Amazon Kindle Store is stocked with titles you can purchase, but if you'd rather not spend any money to expand your library, you don't have to. Here are some ways you can load up your Amazon Kindle with free e-books.

MikeroweWorks Foundation founder Mike Rowe joins'The Brian Kilmeade Show' to discuss how AI and robots threaten white-collar jobs, as the nation faces a need for blue-collar workers. 'UNDENIABLE': Mike Rowe is sounding the alarm about the future of white and blue-collar jobs, and is urging young Americans to rethink their career choices due to threats from artificial intelligence. 'ALL IN': President Donald Trump is going all in on artificial intelligence, with a top Meta executive voicing strong support for his bold strategy. Speaking at a tech summit in Washington, Trump outlined his vision for a future driven by American innovation and secured by global artificial intelligence leadership. INNOVATION BOOST: Nvidia CEO Jensen Huang said in an interview Wednesday that the Trump administration's artificial intelligence plan is poised to boost innovation and AI deployment in the U.S. IMMINENT CRISIS: OpenAI CEO Sam Altman warned Wall Street executives that bad actors could exploit digital voice ID authentication to defraud consumers by enabling large money transfers, creating what he describes as an imminent fraud crisis. STARGATE OPENS: Oracle and OpenAI have inked an agreement to further develop the Stargate project as part of a broader pledge to expand Artificial Intelligence (AI) infrastructure in the United States.

We may earn revenue from the products available on this page and participate in affiliate programs. A wireless soundbar can change the entire feel of your home theater. No matter what kind of content you consume, sound quality makes a world of difference, and these wireless systems allow for impeccable quality without pro installation. Most systems include a soundbar as well as satellite speakers and a subwoofer, all of which communicate wirelessly with no delay. We've tested some of the most popular and powerful wireless soundbars and systems on the market and came up with these recommendations for any type of viewer. Still, the Samsung HW-990F reigns as the best overall wireless soundbar, but the competition gets closer all the time. I [Markkus Rovito] have previewed and reviewed products--both in the pro audio and home audio realms--on and off throughout most of the 21st century for outlets including Mix, Maximum Tech, DJ Tech Tools, Bob Vila, and some defunct publications printed on paper called magazines. Between the PopSci staff [Stan Horaczek, Tony Ware, Brandt Ranj], we've tested variations of these wireless surround sound systems firsthand. Beyond our own favorable experiences, these wireless surround sound systems have all proved popular with expert reviewers. Each of these systems offers something a little different from the others, but they all include rear satellite speakers because we wanted to limit this list to systems that actually surround you with speakers.

Online news outlets operate predominantly on an advertising-based revenue model, compelling journalists to create headlines that are often scandalous, intriguing, and provocative -- commonly referred to as clickbait. Automatic detection of clickbait headlines is essential for preserving information quality and reader trust in digital media and requires both contextual understanding and world knowledge. For this task, particularly in less-resourced languages, it remains unclear whether fine-tuned methods or in-context learning (ICL) yield better results. In this paper, we compile CLIC, a novel dataset for clickbait detection of Croatian news headlines spanning a 20-year period and encompassing mainstream and fringe outlets. We fine-tune the BERTić model on this task and compare its performance to LLM-based ICL methods with prompts both in Croatian and English. Finally, we analyze the linguistic properties of clickbait. We find that nearly half of the analyzed headlines contain clickbait, and that finetuned models deliver better results than general LLMs.

The volatility and unpredictability of emerging technologies, such as artificial intelligence (AI), generate significant uncertainty, which is widely discussed on social media. This study examines anticipatory discourse surrounding technological futures by analysing 1.5 million posts from 400 key opinion leaders (KOLs) published on the X platform (from 2021 to 2023). Using advanced text mining techniques, including BERTopic modelling, sentiment, emotion, and attitude analyses, the research identifies 100 distinct topics reflecting anticipated tech-driven futures. Our findings emphasize the dual role of KOLs in framing \textit{present futures} -- optimistic visions of transformative technologies like AI and IoT -- and influencing \textit{future presents}, where these projections shape contemporary societal and geopolitical debates. Positive emotions such as Hope dominate, outweighing Anxiety, particularly in topics like ``Machine Learning, Data Science, and Deep Learning,'' while discussions around ``Climate Change'' and ``War, Ukraine, and Trump People'' elicit \textit{Anxiety}. By framing technologies as solutions to societal challenges, KOLs act as mediators of societal narratives, bridging imagined futures and current realities. These insights underscore their pivotal role in directing public attention with emerging technologies during periods of heightened uncertainty, advancing our understanding of anticipatory discourse in technology-mediated contexts.