The largest and most varied such publicly available dataset, at Airbus SPOT 6/7 satellites' high resolution of up to 1.5 m/pixel, empowered by European Space Agency's (ESA) Phi-Lab as part of the ESA-funded

Numerical simulations of seismic wave propagation are crucial for investigating velocity structures and improving seismic hazard assessment. However, standard methods such as finite difference or finite element are computationally expensive. Recent studies have shown that a new class of machine learning models, called neural operators, can solve the elastodynamic wave equation orders of magnitude faster than conventional methods. Full waveform inversion is a prime beneficiary of the accelerated simulations. Neural operators, as end-to-end differentiable operators, combined with automatic differentiation, provide an alternative approach to the adjoint-state method. Since neural operators do not involve the Born approximation, when used for full waveform inversion they have the potential to include additional phases and alleviate cycle-skipping problems present in traditional adjoint-state formulations. In this study, we demonstrate the application of neural operators for full waveform inversion on a real seismic dataset, which consists of several nodal transects collected across the San Gabriel, Chino, and San Bernardino basins in the Los Angeles metropolitan area.

The battle for AI leadership is on, with OpenAI in the United States and DeepSeek in China as key contenders. In response to these global trends, the Spanish government has proposed ALIA, a public and transparent AI infrastructure incorporating small language models designed to support Spanish and co-official languages such as Basque. This paper presents the results of Red Teaming sessions, where ten participants applied their expertise and creativity to manually test three of the latest models from these initiatives$\unicode{x2013}$OpenAI o3-mini, DeepSeek R1, and ALIA Salamandra$\unicode{x2013}$focusing on biases and safety concerns. The results, based on 670 conversations, revealed vulnerabilities in all the models under test, with biased or unsafe responses ranging from 29.5% in o3-mini to 50.6% in Salamandra. These findings underscore the persistent challenges in developing reliable and trustworthy AI systems, particularly those intended to support Spanish and Basque languages.

Nestled between an original Donkey Kong arcade machine, a mint condition OutRun racing simulation game and booths wired up with GameCubes and Nintendo 64s, the engineer Luke Malpass works away dismantling a broken Nintendo Wii. There has been a steady stream of people bringing in their old game consoles for repairs or modifications, on the house, to Four Quarters, a retro games arcade in Elephant and Castle, which has been transformed into a games clinic for two days. Gabriella Rosenau, 35, brought in her broken Wii that had been in the garage "for years". "I still play my brother's old Nintendo 64 and I love it, but I'd really love to get [the Wii] fixed." "I've done the odd bit of Call of Duty and the PlayStation stuff, but I have more of an interest in the retro games," she adds. Rosenau is part of a growing community who are ditching contemporary video games and picking up the consoles from their childhood, or even before their time.

Fact-checking is necessary to address the increasing volume of misinformation. Traditional fact-checking relies on manual analysis to verify claims, but it is slow and resource-intensive. This study establishes baseline comparisons for Automated Fact-Checking (AFC) using Large Language Models (LLMs) across multiple labeling schemes (binary, three-class, five-class) and extends traditional claim verification by incorporating analysis, verdict classification, and explanation in a structured setup to provide comprehensive justifications for real-world claims. We evaluate Llama-3 models of varying sizes (3B, 8B, 70B) on 17,856 claims collected from PolitiFact (2007-2024) using evidence retrieved via restricted web searches. We utilize TIGERScore as a reference-free evaluation metric to score the justifications. Our results show that larger LLMs consistently outperform smaller LLMs in classification accuracy and justification quality without fine-tuning. We find that smaller LLMs in a one-shot scenario provide comparable task performance to fine-tuned Small Language Models (SLMs) with large context sizes, while larger LLMs consistently surpass them. Evidence integration improves performance across all models, with larger LLMs benefiting most. Distinguishing between nuanced labels remains challenging, emphasizing the need for further exploration of labeling schemes and alignment with evidences. Our findings demonstrate the potential of retrieval-augmented AFC with LLMs.

Surface wave dispersion curve inversion is essential for estimating subsurface Shear-wave velocity ($v_s$), yet traditional methods often struggle to balance computational efficiency with inversion accuracy. While deep learning approaches show promise, previous studies typically require large amounts of labeled data and struggle with real-world datasets that have varying period ranges, missing data, and low signal-to-noise ratios. This study proposes DispFormer, a transformer-based neural network for inverting the $v_s$ profile from Rayleigh-wave phase and group dispersion curves. DispFormer processes dispersion data at each period independently, thereby allowing it to handle data of varying lengths without requiring network modifications or alignment between training and testing data. The performance is demonstrated by pre-training it on a global synthetic dataset and testing it on two regional synthetic datasets using zero-shot and few-shot strategies. Results indicate that zero-shot DispFormer, even without any labeled data, produces inversion profiles that match well with the ground truth, providing a deployable initial model generator to assist traditional methods. When labeled data is available, few-shot DispFormer outperforms traditional methods with only a small number of labels. Furthermore, real-world tests indicate that DispFormer effectively handles varying length data, and yields lower data residuals than reference models. These findings demonstrate that DispFormer provides a robust foundation model for dispersion curve inversion and is a promising approach for broader applications.

Deep learning techniques have been used to build velocity models (VMs) for seismic traveltime tomography and have shown encouraging performance in recent years. However, they need to generate labeled samples (i.e., pairs of input and label) to train the deep neural network (NN) with end-to-end learning, and the real labels for field data inversion are usually missing or very expensive. Some traditional tomographic methods can be implemented quickly, but their effectiveness is often limited by prior assumptions. To avoid generating labeled samples, we propose a novel method by integrating deep learning and dictionary learning to enhance the VMs with low resolution by using the traditional tomography-least square method (LSQR). We first design a type of shallow and simple NN to reduce computational cost followed by proposing a two-step strategy to enhance the VMs with low resolution: (1) Warming up. An initial dictionary is trained from the estimation by LSQR through dictionary learning method; (2) Dictionary optimization. The initial dictionary obtained in the warming-up step will be optimized by the NN, and then it will be used to reconstruct high-resolution VMs with the reference slowness and the estimation by LSQR. Furthermore, we design a loss function to minimize traveltime misfit to ensure that NN training is label-free, and the optimized dictionary can be obtained after each epoch of NN training. We demonstrate the effectiveness of the proposed method through numerical tests.

For activist Issa Amro, the latest revelations from human rights group Amnesty International about Israel's ever-growing use of facial recognition technology against Palestinians come as no surprise. My people are suffering from it," he told Al Jazeera from Hebron. On May 2, Amnesty published a report titled Automated Apartheid, detailing the workings of Israel's Red Wolf programme – a facial recognition technology used to track Palestinians since last year that is believed to be linked to similar, earlier programmes known as Blue Wolf and Wolf Pack. The technology has been deployed at checkpoints in the city of Hebron and other parts of the occupied West Bank – scanning the faces of Palestinians and comparing them against existing databases. Palestinians, like anyone else, have the right to live in a world that upholds equality and dignity. Help dismantle Israel's apartheid and call for an end to the supply of facial recognition technologies used in the Occupied Palestinian ...

Tredence, the Data Science and AI Solutions company, announced it has raised USD 175 million in Series B funding from Advent International (Advent) to accelerate data-fueled growth and AI value realization for industries. Advent is one of the largest and most experienced global private equity investors. The full financial terms of the agreement have not been disclosed. Advent will acquire a minority stake in Tredence with the $175 million investment. Advent has significant investment experience in the technology services and software sectors.

It’s an age-old problem in procurement: Corporate buyers lack the time to negotiate fully with all suppliers. Historically this has left untapped value on the table for both buyers and suppliers. To address this challenge, Walmart deployed AI-powered negotiations software with a text-based interface (i.e., a chatbot) to connect with suppliers. So far, the chatbot is negotiating and closing agreements with 68% of suppliers approached, with each side gaining something it values. This article offers four lessons to deliver results from automated procurement negotiations: move quickly to a production pilot, start with indirect spend categories with pre-approved suppliers, decide on acceptable negotiation trade-offs, and scale by extending geographies, categories, and use cases.