In the era of artificial intelligence (AI), pedestrian privacy is increasingly at risk. In research areas such as autonomous driving, computer vision, and surveillance, large datasets are often collected in public spaces, capturing pedestrians without consent or anonymization. These datasets are used to train systems that can identify, track, and analyze individuals, often without their knowledge. Although various technical methods and regional regulations have been proposed to address this issue, existing solutions are either insufficient to protect privacy or compromise data utility, thereby limiting their effectiveness for research. In this paper, we argue that more effort is needed to protect pedestrian privacy in the era of AI while maintaining data utility. We call on the AI and computer vision communities to take pedestrian privacy seriously and to rethink how pedestrian data are collected and anonymized. Collaboration with experts in law and ethics will also be essential for the responsible development of AI. Without stronger action, it will become increasingly difficult for individuals to protect their privacy, and public trust in AI may decline.

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The rise of parallel computing hardware has made it increasingly important to understand which nonlinear state space models can be efficiently parallelized.

A panther, a leopard and a Canadian lynx are among the reported sightings of big cats in Wales, according to a Freedom of Information (FOI) request. Fifteen big cats were reported to authorities in Wales between January 2020 and July 2025, the FOI to the Welsh government found. The apparent spottings were made in areas ranging from Pembrokeshire to Ceredigion, Powys, Swansea, Denbighshire and Carmarthenshire. One reporter described seeing what they believed was a panther jumping over a hedge onto the road in front of them while they were driving. A leopard sighting was reported to Dyfed-Powys Police in Cwmtwrch, Swansea, on 16 January 2023, when the reporter saw a leopard with spots walking around the garden when their dog was let out.

The UK government is introducing a ban on social media for children and a minimum age for some chatbots in an attempt to shield young people from dangerous corners of the web. UK prime minister Keir Starmer has been leading the charge on under-16 social media regulation. Children under the age of 16 will be banned from social media platforms in the UK, under new measures announced by prime minister Keir Starmer on Monday. "The need for action could not be clearer. Social media is making our children unhappy and unsafe," said Starmer, in an X post .

Causality in time series can be challenging to determine, especially in the presence of non-linear dependencies. Granger causality helps analyze potential relationships between variables, thereby offering a method to determine whether one time series can predict--Granger cause--future values of another.

Large language models (LLMs) demonstrate impressive generalization abilities, yet adapting them effectively across multiple heterogeneous domains remains challenging due to inter-domain interference. To overcome this challenge, we propose a partition-based multi-stage fine-tuning framework designed to exploit inter-domain synergies while minimizing negative transfer.

SHAP (SHapley Additive exPlanations) values are a widely used method for local feature attribution in interpretable and explainable AI. We propose an efficient two-stage algorithm for computing SHAP values in both black-box setting and tree-based models. We assume the black-box predictor or tree model accepts binary (zero-one) features.

Artificial intelligence (AI) now permeates critical infrastructures and decisionmaking systems where failures produce social, economic, and democratic harm. This position paper challenges the entrenched belief that regulation and innovation are opposites. As evidenced by analogies from aviation, pharmaceuticals, and welfare systems and recent cases of synthetic misinformation, bias and unaccountable decision-making, the absence of well-designed regulation has already created immeasurable damage. Regulation, when thoughtful and adaptive, is not a brake on innovation--it is its foundation. The present position paper examines the EU AIAct as a model of risk-based, responsibility-driven regulation that addresses the Collingridge Dilemma: acting early enough to prevent harm, yet flexibly enough to sustain innovation. Its adaptive mechanisms--regulatory sandboxes, small and medium enterprises (SMEs) support, real-world testing, fundamental rights impact assessment (FRIA)--demonstrate how regulation can accelerate responsibly, rather than delay, technological progress. The position paper summarises how governance tools transform perceived burdens into tangible advantages: legal certainty, consumer trust, and ethical competitiveness.

Large language models (LLMs) and vision-language models (VLMs) have demonstrated remarkable capabilities, driving significant advancements across a wide range of applications. These models are typically fine-tuned to align with specific objectives, such as being "helpful and harmless" [39]. However, recent work on adversarial attacks has demonstrated that carefully crafted inputs can bypass these alignment safeguards [65, 10, 4, 26, 52]. While such adversarial attacks can elicit harmful responses, the output is usually constrained to text that is not directly actionable, limiting the scope of possible harm. While malicious text outputs are concerning, it remains unclear whether the associated risks exceed those posed by information already accessible through the internet [18].