Is your favourite sci-fi novel included here, or have we forgotten it? Almost exactly a year ago, I asked our team of expert science writers here at New Scientist to name their favourite science fiction novels. Personal tastes meant we ended up with a wonderfully eclectic list, ranging from classics by the likes of Margaret Atwood and Octavia Butler to titles I'd not previously read (Jon Bois's 17776 was a particularly wild suggestion, from our US editor Chelsea Whyte – but it's well worth your time). We New Scientist staffers tend to be sci-fi nerds, and we realised we hadn't quite got all the greats yet. So here, for your reading pleasure, is our second take on our favourite sci-fi novels of all time, otherwise known as the ones we forgot. Again, we're not claiming this is a definitive list. It's just our top sci-fi reads, in no particular order, and we hope you'll discover some new favourites of your own in this line-up. We asked New Scientist staff to pick their favourite science fiction books. Here are the results, ranging from 19th-century classics to modern day offerings, and from Octavia E. Butler to Iain M. Banks And if we still haven't got them all, then come and tell us about it on Facebook.

Randy Travis and his wife Mary spoke with Fox News Digital at the ACMs last week about the AI technology that helped recreate Travis' voice after he suffered a stroke and why they hope people can see the good in its usage. Randy Travis is leaning into artificial intelligence (AI) to continue to produce new music, over a decade after his near-fatal stroke. In 2013, Travis' stroke left him with aphasia – which is the loss of ability to understand or express speech. With the help of AI and country musician James Dupré, Travis was able to produce two new songs since his stroke, "Where That Came From" in 2024 and now his latest single, "Horses in Heaven." He has been on his "More Life Tour" since last spring and recently extended dates through fall 2025.

Framing in media critically shapes public perception by selectively emphasizing some details while downplaying others. With the rise of large language models in automated news and content creation, there is growing concern that these systems may introduce or even amplify framing biases compared to human authors. In this paper, we explore how framing manifests in both out-of-the-box and fine-tuned LLM-generated news content. Our analysis reveals that, particularly in politically and socially sensitive contexts, LLMs tend to exhibit more pronounced framing than their human counterparts. In addition, we observe significant variation in framing tendencies across different model architectures, with some models displaying notably higher biases. These findings point to the need for effective post-training mitigation strategies and tighter evaluation frameworks to ensure that automated news content upholds the standards of balanced reporting.

-- We retarget video stitching to an emerging issue, named warping shake, which unveils the temporal content shakes induced by sequentially unsmooth warps when extending image stitching to video stitching. Even if the input videos are stable, the stitched video can inevitably cause undesired warping shakes and affect the visual experience. T o address this issue, we propose StabStitch++, a novel video stitching framework to realize spatial stitching and temporal stabilization with unsupervised learning simultaneously. First, different from existing learning-based image stitching solutions that typically warp one image to align with another, we suppose a virtual midplane between original image planes and project them onto it. Concretely, we design a differentiable bidirectional decomposition module to disentangle the homography transformation and incorporate it into our spatial warp, evenly spreading alignment burdens and projective distortions across two views. Then, inspired by camera paths in video stabilization, we derive the mathematical expression of stitching trajectories in video stitching by elaborately integrating spatial and temporal warps. Finally, a warp smoothing model is presented to produce stable stitched videos with a hybrid loss to simultaneously encourage content alignment, trajectory smoothness, and online collaboration. Compared with StabStitch that sacrifices alignment for stabilization, StabStitch++ makes no compromise and optimizes both of them simultaneously, especially in the online mode. T o establish an evaluation benchmark and train the learning framework, we build a video stitching dataset with a rich diversity in camera motions and scenes. NTRODUCTION Lang Nie, Chunyu Lin, and Y ao Zhao are with the Institute of Information Science, Beijing Jiaotong University, Beijing 100044, China, and also with Visual Intelligence +X International Cooperation Joint Laboratory of MOE, Beijing 100044, China (e-mail: nielang@bjtu.edu.cn, Kang Liao is with the School of Computing and Data Science, Nanyang Technological University, Singapore (e-mail: kang.liao@ntu.edu.sg). Y un Zhang is with the School of Media Engineering, Communication University of Zhejiang, Hangzhou 310018, China (e-mail: zhangyun@cuz.edu.cn). Shuaicheng Liu is with the School of Information and Communication Engineering, University of Electronic Science and Technology of China, Chengdu 611731, China (e-mail: liushuaicheng@uestc.edu.cn). This work was supported by the National Natural Science Foundation of China (NSFC) under Grants U2441242 and 62172032, as well as by the Open Fund of Zhejiang Key Laboratory of Film and TV Media Technology. IDEO stitching techniques are commonly employed to create panoramic or wide field-of-view (FoV) displays from different viewpoints with limited FoV .

Thanks to recent advancements in scalable deep architectures and large-scale pretraining, text-to-video generation has achieved unprecedented capabilities in producing high-fidelity, instruction-following content across a wide range of styles, enabling applications in advertising, entertainment, and education. However, these models' ability to render precise on-screen text, such as captions or mathematical formulas, remains largely untested, posing significant challenges for applications requiring exact textual accuracy. In this work, we introduce T2VTextBench, the first human-evaluation benchmark dedicated to evaluating on-screen text fidelity and temporal consistency in text-to-video models. Our suite of prompts integrates complex text strings with dynamic scene changes, testing each model's ability to maintain detailed instructions across frames. We evaluate ten state-of-the-art systems, ranging from open-source solutions to commercial offerings, and find that most struggle to generate legible, consistent text. These results highlight a critical gap in current video generators and provide a clear direction for future research aimed at enhancing textual manipulation in video synthesis.

This research introduces an innovative AI-driven multi-agent framework specifically designed for creating immersive audiobooks. Leveraging neural text-to-speech synthesis with FastSpeech 2 and VALL-E for expressive narration and character-specific voices, the framework employs advanced language models to automatically interpret textual narratives and generate realistic spatial audio effects. These sound effects are dynamically synchronized with the storyline through sophisticated temporal integration methods, including Dynamic Time Warping (DTW) and recurrent neural networks (RNNs). Diffusion-based generative models combined with higher-order ambisonics (HOA) and scattering delay networks (SDN) enable highly realistic 3D soundscapes, substantially enhancing listener immersion and narrative realism. This technology significantly advances audiobook applications, providing richer experiences for educational content, storytelling platforms, and accessibility solutions for visually impaired audiences. Future work will address personalization, ethical management of synthesized voices, and integration with multi-sensory platforms.

Despite the fact that popular text-to-image generation models cope well with international and general cultural queries, they have a significant knowledge gap regarding individual cultures. This is due to the content of existing large training datasets collected on the Internet, which are predominantly based on Western European or American popular culture. Meanwhile, the lack of cultural adaptation of the model can lead to incorrect results, a decrease in the generation quality, and the spread of stereotypes and offensive content. In an effort to address this issue, we examine the concept of cultural code and recognize the critical importance of its understanding by modern image generation models, an issue that has not been sufficiently addressed in the research community to date. We propose the methodology for collecting and processing the data necessary to form a dataset based on the cultural code, in particular the Russian one. We explore how the collected data affects the quality of generations in the national domain and analyze the effectiveness of our approach using the Kandinsky 3.1 text-to-image model. Human evaluation results demonstrate an increase in the level of awareness of Russian culture in the model.

--Ornamentations, embellishments, or microtonal inflections are essential to melodic expression across many musical traditions, adding depth, nuance, and emotional impact to performances. Recognizing ornamentations in singing voices is key to MIR, with potential applications in music pedagogy, singer identification, genre classification, and controlled singing voice generation. However, the lack of annotated datasets and specialized modeling approaches remains a major obstacle for progress in this research area. In this work, we introduce R aga Ornamentation Detection (ROD), a novel dataset comprising Indian classical music recordings curated by expert musicians. The dataset is annotated using a custom Human-in-the-Loop tool for six vocal ornaments marked as event-based labels. Using this dataset, we develop an ornamentation detection model based on deep time-series analysis, preserving ornament boundaries during the chunking of long audio recordings. We conduct experiments using different train-test configurations within the ROD dataset and also evaluate our approach on a separate, manually annotated dataset of Indian classical concert recordings. Our experimental results support the superior performance of our proposed approach over the baseline CRNN. USICAL ornaments are intentional variations around notes that bring expressiveness and complexity to a melody [1], [2]. They are essential elements across musical traditions, shaping the music's identity and emotional depth. Automatic detection of ornaments in any music piece has broad applications in music education, performance analysis, and computational musicology, yet it remains significantly understudied, with very few works like [3]-[5].

In Herman Hesse's novel "The Glass Bead Game," published in 1943, a future Europe is controlled by only two powers, the players of that mysterious game that uses math and musicology to utilize all of human historical knowledge, and the Roman Catholic Church. Though the actual rules and playing of the glass bead game are vague in the book, to the modern reader its use of prompts to generate truth from the archive of history looks incredibly similar to artificial intelligence, arguably the greatest challenge the non-fictional Pope Leo, the Roman Catholic Church's new pope, Pope Leo XIV, must navigate. In the course of European history, popes have had enormous influence on the development of science, sometimes in conflict, such as with Galileo and Pope Paul V, but also in vital partnership by creating all of the continent's first universities. Indeed, today's Catholic catechism pronounces that science and faith are complementary not in conflict, it reads in part, "…methodical research in all branches of knowledge, provided it is carried out in a truly scientific manner and does not override moral laws, can never conflict with the faith, because the things of the world and the things of faith derive from the same God." Newly elected Pope Leo XIV appears at the balcony of St. Peter's Basilica at the Vatican on Thursday.

Looking Glass is transforming how we interact with 3D visuals. You can now gather around a screen and see digital objects come to life in true three dimensions; no headsets, no glasses, just your eyes and a shared experience with others. That's exactly what the new, 27-inch light-field display from Looking Glass offers. This innovative technology is transforming how we interact with 3D visuals, making immersive experiences more natural and accessible for businesses, educators and creators alike. Join the FREE "CyberGuy Report": Get my expert tech tips, critical security alerts and exclusive deals, plus instant access to my free "Ultimate Scam Survival Guide" when you sign up!