Get ready for the return of the annual energy summit in Berlin on October 10. Returning for its second edition this October in Berlin, the WIRED & Octopus Energy Tech Summit is bringing together Europe's leading experts and visionaries in the green energy sector to explore how to accelerate the creation of a fully carbon-free energy system. Last year's summit focused on the urgent need for green technology in the wake of the energy crisis. Audiences heard from business leaders, startup founders, politicians, inventors, and even an astronaut. This year, energy leaders from across the EU will meet to carve the path to a rapid global energy transition.

Bird flu has been spreading in dairy cows in the US--and the scale is likely to be far worse than it looks. In addition, 14 human cases have been reported in the US since March. Both are worrying developments, say virologists, who fear that the country's meager response to the virus is putting the entire world at risk of another pandemic. Infections in dairy cattle, first reported back in March, brought us a step closer to human spread. Since then, the situation has only deteriorated. The virus appears to have passed from cattle to poultry on multiple occasions, and worse, this form of bird flu that is now spreading among cattle could find its way back into migrating birds.

'You can harness the power of death in a controlled way.' Breakthroughs, discoveries, and DIY tips sent every weekday. While only about 10 percent of the roughly 4,000 known snake species have venom that can harm a human, using genetics to determine which snakes could be deadly could speed up developing better treatments for bites. A new tool called VenomCap can help scientists hone in on venom at a genetic level, so we can know which ones are likely carrying deadly toxins. The method is detailed in a study published September 19 in the journal Molecular . "We've developed a tool that can tell us which venom-producing genes are present across an entire snake family in one fell swoop," Sara Ruane, a study co-author and the Assistant Curator of Herpetology at the Field Museum in Chicago, said in a statement .

A group that includes sex workers, sex tech businesses, and sex educators has demanded a seat at the table to shape AI regulations that they say could lead to discrimination against them. A group of sex industry professionals and advocates issued an open letter to EU regulators on Thursday, claiming that their views are being overlooked in vital discussions on policing AI technology despite also being implicated in AI's momentous rise. In response to European internet regulations, a collective of adult industry members--including sex workers, erotic filmmakers, sex tech enterprises, and sex educators--urged the European Commission to include them in future negotiations shaping AI regulations, according to the letter, seen by WIRED. The group includes erotic filmmaker Erika Lust's company as well as the European Sex Workers' Rights Alliance campaign group, and is signed the Open Mind AI initiative. The group aims to alert the commission of what it says is a "critical gap" in discussions on AI regulation.

Palmer Luckey Is Bringing Anduril Smarts to Microsoft's Military Headset The founder of Oculus VR is returning to headsets--this time for the battlefield. When Palmer Luckey was hacking together virtual reality headsets at his startup Oculus VR in the mid-2010s, he would sometimes imagine a future in which US soldiers used the technology to sharpen their battlefield senses. That vision is now virtually a reality after a deal that will bring software from his defense startup, Anduril, to a US Army head-mounted display developed by Microsoft. "The idea is to enhance soldiers," Luckey tells WIRED over Zoom from his home in Newport Beach, California. "Their visual perception, audible perception--basically to give them all the vision that Superman has, and then some, and make them more lethal."

Recently, Large Language Models (LLMs) have been widely studied by researchers for their roles in various downstream NLP tasks. As a fundamental task in the NLP field, Chinese Grammatical Error Correction (CGEC) aims to correct all potential grammatical errors in the input sentences. Previous studies have shown that LLMs' performance as correctors on CGEC remains unsatisfactory due to its challenging task focus. To promote the CGEC field to better adapt to the era of LLMs, we rethink the roles of LLMs in the CGEC task so that they can be better utilized and explored in CGEC. Considering the rich grammatical knowledge stored in LLMs and their powerful semantic understanding capabilities, we utilize LLMs as explainers to provide explanation information for the CGEC small models during error correction to enhance performance. We also use LLMs as evaluators to bring more reasonable CGEC evaluations, thus alleviating the troubles caused by the subjectivity of the CGEC task. In particular, our work is also an active exploration of how LLMs and small models better collaborate in downstream tasks. Extensive experiments and detailed analyses on widely used datasets verify the effectiveness of our thinking intuition and the proposed methods.

AI-based vascular segmentation is becoming increasingly common in enhancing the screening and treatment of ophthalmic diseases. Deep learning structures based on U-Net have achieved relatively good performance in vascular segmentation. However, small blood vessels and capillaries tend to be lost during segmentation when passed through the traditional U-Net downsampling module. To address this gap, this paper proposes a novel Kalman filter based Linear Deformable Diffusion (KLDD) model for retinal vessel segmentation. Our model employs a diffusion process that iteratively refines the segmentation, leveraging the flexible receptive fields of deformable convolutions in feature extraction modules to adapt to the detailed tubular vascular structures. More specifically, we first employ a feature extractor with linear deformable convolution to capture vascular structure information form the input images. To better optimize the coordinate positions of deformable convolution, we employ the Kalman filter to enhance the perception of vascular structures in linear deformable convolution. Subsequently, the features of the vascular structures extracted are utilized as a conditioning element within a diffusion model by the Cross-Attention Aggregation module (CAAM) and the Channel-wise Soft Attention module (CSAM). These aggregations are designed to enhance the diffusion model's capability to generate vascular structures. Experiments are evaluated on retinal fundus image datasets (DRIVE, CHASE_DB1) as well as the 3mm and 6mm of the OCTA-500 dataset, and the results show that the diffusion model proposed in this paper outperforms other methods.

In societies valuing freedom of expression, individuals now frequently express and share their opinions, integrating this practice as a natural part of their routines. Unfortunately, this new social and informational landscape has favored an unprecedented amplification of cyber threats such as hate speech and disinformation, posing significant risks to democratic systems Office of Science and Technology of the Congress of Deputies (Office C) (2023). This situation has intensified and drawn substantial attention from the research community, governmental bodies, and the general public, particularly following extensive disinformation campaigns associated with recent events, including the COVID-19 pandemic Kim and Kesari (2021), the Russia-Ukraine war Pierri et al. (2022), and the Israel-Palestine conflict Aljazeera (2024). Consequently, a structured model encapsulating the key actors, dynamics, and resulting societal impacts is proposed to understand and contextualize the environment being worked on. Figure 1 illustrates our threat model with three main components. In blue, the media and audience as actors in the model, providing the information environment with online news and social network posts that people can read, react to, and comment on. In orange, the content is considered potentially harmful due to intrinsic hateful narratives of today's ecosystem (particularly, public reactions that will be the focus of this research work). In red, the online situation leads to polarization, extremism, and heightened tension, creating a vulnerable environment for society OSMUNDSEN et al. (2021); Cinelli et al. (2021); Pastor-Galindo et al. (2021). In fact, this agitated context serves as a vector for disinformation to become more effective Kim and Kesari (2021).

This paper presents Text2Traj2Text, a novel learning-by-synthesis framework for captioning possible contexts behind shopper's trajectory data in retail stores. Our work will impact various retail applications that need better customer understanding, such as targeted advertising and inventory management. The key idea is leveraging large language models to synthesize a diverse and realistic collection of contextual captions as well as the corresponding movement trajectories on a store map. Despite learned from fully synthesized data, the captioning model can generalize well to trajectories/captions created by real human subjects. Our systematic evaluation confirmed the effectiveness of the proposed framework over competitive approaches in terms of ROUGE and BERT Score metrics.

We introduce jina-embeddings-v3, a novel text embedding model with 570 million parameters, achieves state-of-the-art performance on multilingual data and long-context retrieval tasks, supporting context lengths of up to 8192 tokens. The model includes a set of task-specific Low-Rank Adaptation (LoRA) adapters to generate high-quality embeddings for query-document retrieval, clustering, classification, and text matching. Evaluation on the MTEB benchmark shows that jina-embeddings-v3 outperforms the latest proprietary embeddings from OpenAI and Cohere on English tasks, while achieving superior performance compared to multilingual-e5-large-instruct across all multilingual tasks. With a default output dimension of 1024, users can flexibly reduce the embedding dimensions to as low as 32 without compromising performance, enabled by Matryoshka Representation Learning.