The number of unauthorised Gypsy and traveller sites is on the rise, with developments often carried out over Bank Holiday weekends. Since April, sites in Hertfordshire, Essex, Cambridgeshire, Bedfordshire and elsewhere have been transformed, prompting legal battles and local dismay. In January, the number of unauthorised traveller caravans - those without planning permission - across England stood at 4,950, a 73% increase on January 2020's figure of 2,860. The summer count also shows a rise, of 44% between July 2019 and July 2025, from 3,113 to 4,471. A senior councillor in one affected area warns there has been a loss of control of the planning system, while villagers living near one site likened its rapid construction to a military operation .

Jason Arday, the former University of Cambridge professor at the centre of a plagiarism row, has been found dead. Emergency services said a man was found unresponsive at an address in Battersea, south London, on Friday afternoon. Metropolitan Police officers were called by the London Ambulance Service. A 41-year-old man was pronounced dead at the scene and his next of kin have been informed, the force said. Arday resigned as a Cambridge professor of sociology of education last week after allegations of plagiarism and questions about some of his achievements.

What does it take for a robot to hold a conversation with a room, not just a person? That was one of the questions at the heart of my last week (20th-24th July) at the Imperial Robotics Summer School, hosted at Imperial College, London; a week that sharpened my thinking and pushed me to look at robotics problems from angles I don't usually get to in my day-to-day work. The summer school brought together emerging researchers, academics, and industry professionals from across the robotics community for an intensive, hands-on programme. I came away having learned a huge amount, from expert-led lectures on robot kinematics, dynamics, sensing and control, and robot learning, through to specialised sessions on aerial robotics, robot intelligence, surgical robot vision, bio-inspired sensing and control, and personal assistive robotics. One of the standout parts of the week was getting into Imperial's robotics labs themselves, seeing research up close across adaptive and intelligent robotics, aerial robotics, robotic surgery, manipulation and touch, and assistive robotics.

Image caption, Prof Sarah Dillon studied Alan Turing's short story, Pryce's Buoy A story written by World War Two codebreaker Alan Turing suggests he was a man with playful humour embracing his homosexuality, according to new analysis. The six-page hand-written short story, Pryce's Buoy, has been transcribed in full for the first time and studied by Sarah Dillon, professor of literature at the University of Cambridge. Dillon said the story offered fresh insights that challenge the view of Turing as an isolated, lonely genius. We've distilled a version of Turing that is a two-dimensional stereotype, she added. Turing played a crucial role in the Allies' victory over Nazi Germany in World War Two by helping to crack codes and deciphering the infamous Enigma machine at Bletchley Park.

OpenAI says its AI went rogue and launched'unprecedented' cyber-attack OpenAI has revealed some of its most advanced AI models went rogue and hacked a start-up after it lost control of them during a security test. The ChatGPT-maker said its agents - AI bots which can operate alone after some human instruction - were being tested in a controlled environment, but found vulnerabilities and managed to escape. They targeted Hugging Face, one of the world's largest hubs for sharing AI models, gaining access to some internal company systems. OpenAI said the incident was unprecedented, external, and it was working with Hugging Face to investigate what happened and strengthen safeguards. Gina Neff, head of the Minderoo Centre for Technology and Democracy at the University of Cambridge, told BBC Radio 4's Today programme that the security tests - called sandboxes - are supposed to be secure environments where you can see what the models are capable of. In this case, it looks like OpenAI didn't make a secure enough sandbox, she added.

I'll just do that later, this thing can wait, maybe a short break first... We've all been there: putting off certain tasks then being left with essays up to the wire, loads of messages unread and that bedroom sort-out that just never happens. A fifth of us are guilty of regularly procrastinating but the type of procastinator we are can reveal something deeper about us, say researchers. Are you a dreamer or rebel? What does it all mean, and can you fix it? The cause can be hidden or buried, says Dr Itamar Shatz, a lecturer at Cambridge University who is publishing a book on the subject this week.

We derive non-asymptotic spectral bands that bound the squared InfoNCE gradient norm via alignment, temperature, and batch spectrum, recovering the 1/τ2 law and closely tracking batch-mean gradients on synthetic data and ImageNet.

We derive and investigate two DPO variants that explicitly model the possibility of declaring a tie in pair-wise comparisons. We replace the Bradley-Terry model in DPO with two well-known modeling extensions, by Rao and Kupper and by Davidson, that assign probability to ties as alternatives to clear preferences. Our experiments in neural machine translation and summarization show that explicitly labeled ties can be added to the datasets for these DPO variants without the degradation in task performance that is observed when the same tied pairs are presented to DPO. We find empirically that the inclusion of ties leads to stronger regularization with respect to the reference policy as measured by KL divergence, and we see this even for DPO in its original form. We provide a theoretical explanation for this regularization effect using ideal DPO policy theory.

We study channel simulation and distributed matching, two fundamental problems with several applications to machine learning, using a recently introduced generalization of the standard rejection sampling (RS) algorithm known as Ensemble Rejection Sampling (ERS). For channel simulation, we propose a new coding scheme based on ERS that achieves a near-optimal coding rate. In this process, we demonstrate that standard RS can also achieve a near-optimal coding rate and generalize the result of Braverman and Garg (2014) to the continuous alphabet setting. Next, as our main contribution, we present a distributed matching lemma for ERS, which serves as the rejection sampling counterpart to the Poisson Matching Lemma (PML) introduced by Li and Anantharam (2021). Our result also generalizes a recent work on importance matching lemma (Phan et al, 2024) and, to our knowledge, is the first result on distributed matching in the family of rejection sampling schemes where the matching probability is close to PML. We demonstrate the practical significance of our approach over prior works by applying it to distributed compression. The effectiveness of our proposed scheme is validated through experiments involving synthetic Gaussian sources and distributed image compression using the MNIST dataset.

Engineered robust losses such as Huber, Student-$t$, and generalised cross-entropy make supervised models tolerant of contamination but cannot answer which observations are corrupted. We introduce Neural Bayesian Anomaly Mitigation (NBAM), a general-purpose drop-in loss derived from a Bayesian latent-switch mixture model: the marginal likelihood defines a robust supervised loss, and the associated posterior defines an unsupervised contamination classifier. Like Huber or Student-$t$, NBAM can replace the standard training loss in any supervised pipeline; unlike them, it additionally learns a structured contamination model and returns a calibrated per-sample contamination posterior. A learned input-dependent prior $π_ϕ(x)$ captures the spatial locality of contamination, so that samples near known corruptions are more likely to be flagged, while an Occam penalty emerges automatically and regularises against over-flagging. On CIFAR-10 with asymmetric label contamination, NBAM recovers the structure of the corruption process without supervision: the contamination posterior separates clean from corrupted samples, and the learned anomaly head identifies the direction of every label-flip pair. Alongside these capabilities, NBAM outperforms the four robust-loss baselines considered here at contamination rates 0.2-0.6.