Science Ask Us Anything The most eco-friendly burial option isn't cremation or human composting With more options than ever, we break down which one's best for the planet. Cemeteries are increasingly running out of space. Are there greener options we ought to turn to? Breakthroughs, discoveries, and DIY tips sent six days a week. Perhaps one of life's hardest tasks is deciding what to do with a loved one's--or even your own--bodily remains. Do you go the cremation route? If you want your last act on Earth to also be good for the Earth, what do you do?

Ouranalysis include examination of the cross-attention maps of the learned prompts at individual time steps of the denoising process.

Whenever I fantasise about a couple of hours of uninterrupted relaxation during the chilly winter months, my mind immediately conjures up images of curling up on the sofa with a deliciously good book. And when summer eventually comes around, just swap the location to a sun lounger in the back garden (or somewhere more exotic). So with 2026 nearly upon us, join me for an eclectic taste of a few literary delights worth feasting upon over the next 12 months. It's the final instalment of Oseman's hit graphic novel series which has followed the lives of Nick and Charlie, two teenage boys who fall for each other at school. Along with their friends, we've followed all the ups and downs of their relationship as they navigated family drama, homophobia and mental health issues, alongside the joy of first love.

To benchmark a model's long-form factuality in open domains, we first use GPT -4 to generate LongFact, a prompt set comprising thousands of questions spanning 38 topics. We then propose that LLM agents can be used as automated evaluators for long-form factuality through a method which we call Search-Augmented Factuality Evaluator (SAFE).

Speech Large Language Models (SpeechLLMs) have achieved breakthroughs in multilingual speech-to-text translation (S2TT). However, existing approaches often overlook semantic commonalities across source languages, leading to biased translation performance. In this work, we propose \textbf{POTSA} (Parallel Optimal Transport for Speech Alignment), a new framework based on cross-lingual parallel speech pairs and Optimal Transport (OT), designed to bridge high- and low-resource translation gaps. First, we introduce a Bias Compensation module to coarsely align initial speech representations across languages. Second, we impose token-level OT constraints on a Q-Former using parallel speech pairs to establish fine-grained consistency of representations. Then, we apply a layer scheduling strategy to focus OT constraints on the most semantically beneficial layers. Experiments on the FLEURS dataset show that our method achieves SOTA performance, with +0.93 BLEU on average over five common languages and +5.05 BLEU on zero-shot languages, using only 10 hours of parallel speech per source language.

ABSTRACT In this work, we propose a streaming speech recognition framework for Amdo Tibetan, built upon a hybrid CTC/Atten-tion architecture with a context-aware dynamic chunking mechanism. The proposed strategy adaptively adjusts chunk widths based on encoding states, enabling flexible receptive fields, cross-chunk information exchange, and robust adaptation to varying speaking rates, thereby alleviating the context truncation problem of fixed-chunk methods. To further capture the linguistic characteristics of Tibetan, we construct a lexicon grounded in its orthographic principles, providing linguistically motivated modeling units. During decoding, an external language model is integrated to enhance semantic consistency and improve recognition of long sentences. Experimental results show that the proposed framework achieves a word error rate (WER) of 6.23% on the test set, yielding a 48.15% relative improvement over the fixed-chunk baseline, while significantly reducing recognition latency and maintaining performance close to global decoding.

Recently, prompt learning has emerged as the state-of-the-art (SOT A) for fair text-to-image (T2I) generation.

OSAKA - Emperor Naruhito and Empress Masako on Monday visited the World Expo in the city of Osaka for the second time. At the Future of Life pavilion, which envisions a society 50 years from now, the Imperial couple viewed exhibits featuring androids and robots. The emperor showed his fascination at an android modeled after Natsume Soseki (1867-1916), saying that the Japanese novelist continues to live on. The emperor and the empress commented that the androids looked very human, according to Japanese roboticist Hiroshi Ishiguro, a professor at the University of Osaka, who guided the couple. At the U.N. Pavilion, run by the United Nations, the Imperial couple watched a video introducing the world body with solemn expressions.