We present self-speculative masked diffusions, a new class of masked diffusion generative models for discrete data that require significantly fewer function evaluations to generate samples. Standard masked diffusion models predict factorized logits over currently masked positions. A number of masked positions are then sampled, however, the factorization approximation means that sampling too many positions in one go leads to poor sample quality. As a result, many simulation steps and therefore neural network function evaluations are required to generate high-quality data. We reduce the computational burden by generating non-factorized predictions over masked positions. This is achieved by modifying the final transformer attention mask from non-causal to causal, enabling draft token generation and parallel validation via a novel, model-integrated speculative sampling mechanism. This results in a non-factorized predictive distribution over masked positions in a single forward pass. We apply our method to GPT2 scale text modelling and protein sequences generation, finding that we can achieve a ~2x reduction in the required number of network forward passes relative to standard masked diffusion models.

The introduction of machine learning methods has led to significant advances in automation, optimization, and discoveries in various fields of science and technology. However, their widespread application faces a fundamental limitation: the transfer of models between data domains generally lacks a rigorous mathematical justification. The key problem is the lack of formal criteria to guarantee that a model trained on one type of data will retain its properties on another.This paper proposes a solution to this problem by formalizing the concept of analogy between data sets and models using first-order logic and Hoare logic.We formulate and rigorously prove a theorem that sets out the necessary and sufficient conditions for analogy in the task of knowledge transfer between machine learning models. Practical verification of the analogy theorem on model data obtained using the Monte Carlo method, as well as on MNIST and USPS data, allows us to achieving F1 scores of 0.84 and 0.88 for convolutional neural networks and random forests, respectively.The proposed approach not only allows us to justify the correctness of transfer between domains but also provides tools for comparing the applicability of models to different types of data.The main contribution of the work is a rigorous formalization of analogy at the level of program logic, providing verifiable guarantees of the correctness of knowledge transfer, which opens new opportunities for both theoretical research and the practical use of machine learning models in previously inaccessible areas.

Conventional construction safety inspection methods are often inefficient as they require navigating through large volume of information. Recent advances in large vision-language models (LVLMs) provide opportunities to automate safety inspections through enhanced visual and linguistic understanding. However, existing applications face limitations including irrelevant or unspecific responses, restricted modal inputs and hallucinations. Utilisation of Large Language Models (LLMs) for this purpose is constrained by availability of training data and frequently lack real-time adaptability. This study introduces SiteShield, a multi-modal LVLM-based Retrieval-Augmented Generation (RAG) framework for automating construction safety inspection reports by integrating visual and audio inputs. Using real-world data, SiteShield outperformed unimodal LLMs without RAG with an F1 score of 0.82, hamming loss of 0.04, precision of 0.76, and recall of 0.96. The findings indicate that SiteShield offers a novel pathway to enhance information retrieval and efficiency in generating safety reports.

In electricity markets around the world, the ability to anticipate price movements with precision can be the difference between profit and loss, especially for fast-acting assets like battery energy storage systems (BESS). As grid volatility increases due to renewables and market decentralisation, operators and forecasters alike face growing pressure to transform prediction into strategy. Yet while forecast data is abundant, especially in advanced markets like Australia's National Electricity Market (NEM), its practical value in driving real-world BESS trading decisions remains largely unexplored. This thesis dives into that gap. This work addresses a key research question: Can the accuracy of the Australian Energy Market Operator (AEMO) energy price forecasts be systematically leveraged to develop a reliable and profitable battery energy storage system trading algorithm? Despite the availability of AEMO price forecasts, no existing framework evaluates their reliability or incorporates them into practical BESS trading strategies. By analysing patterns in forecast accuracy based on time of day, forecast horizon, and regional variations, this project creates a novel, forecast-informed BESS trading model to optimise arbitrage financial returns. The performance of this forecast-driven algorithm is benchmarked against a basic trading algorithm with no knowledge of forecast data. The study further explores the potential of machine learning techniques to predict future energy prices by enhancing AEMO forecasts to govern a more advanced trading strategy. The research outcomes will inform future improvements in energy market trading models and promote more efficient BESS integration into market operations.

New Supreme Court term will reshape Trump's powers The US Supreme Court begins its new term on Monday with a docket already full of potentially significant cases that could define the scope of Donald Trump's presidential authority - and the prospect of more to come. In the eight months that Trump has been back in the White House, he has tested the limits of executive power, unilaterally implementing new policies, slashing federal budgets and workforce, and attempting to bring previously independent agencies and institutions more directly under his control. The latest brewing legal battle comes from the president's attempts to take control of state National Guard units and deploy them in cities where he claims there is public unrest and rampant crime - over the objection of local and state officials. In Oregon, a federal judge has issued orders blocking Trump's deployment of troops to Portland. An appeals court is set to review the move in the coming days.

The tiny shark and a ghost-like crab are two of the latest species uncovered in a yearslong expedition. Breakthroughs, discoveries, and DIY tips sent every weekday. Oceanographers scouring the waters off of Western Australia have discovered two new deep-sea oddities . On October 6, Australia's Commonwealth Scientific and Industrial Research Organization (CSIRO) showcased these new species originally collected in 2022: a bioluminescent lanternshark and a tiny, semi-translucent porcelain crab . The team revealed two of its initial finds--the painted hornshark and the ridged-egg catshark --in 2023.

British microcomputers were among more than 100,000 foreign-made parts contained in Russian missiles and drones used in Sunday's deadly strikes on Ukraine, Volodymyr Zelensky has said. The Ukrainian president called for further effective sanctions after saying parts originating in allied countries including Germany, Japan and the US have been identified in Russian weapons. The Department for Business and Trade (DBT) said it had recently undertaken efforts to crack down on UK firms whose products have continued to make their way into Russia's military supply chain. We take reports of goods from UK companies being found in Russian weaponry incredibly seriously, a government spokesperson said. The spokesperson said the government had banned the export of thousands of goods to Russia including every battlefield item Ukraine has brought to our attention, adding that they have imposed the most the most severe package of sanctions. What are the sanctions on Russia and are they working?

OpenAI and the chipmaker AMD announced on Monday that they had signed a multibillion-dollar chip deal that would also give the ChatGPT creator the option to buy a large stake in the chipmaker. The deal offers OpenAI an opportunity to buy 10% in AMD and marks a major vote of confidence in the company's AI chips and software. Shares of AMD surged more than 30% and added about $80bn to its market capitalization after the announcement. "We view this deal as certainly transformative, not just for AMD, but for the dynamics of the industry," said Forrest Norrod, AMD's executive vice-president. The latest deal, among a string of investment commitments, is a testament to OpenAI and the broader AI industry's voracious appetite for computing power as companies race toward developing AI technology that meets or exceeds human intelligence.

Research reveals that those diagnosed with autism early show distinct genetic and developmental profiles from those diagnosed later. New research from the University of Cambridge suggests that autism should not be understood as a homogeneous condition with a single cause. Scientists found that people diagnosed in early childhood often have a different genetic profile than those diagnosed later in life, broadening the understanding of how the condition develops. The study analyzed the behavior of autistic people during childhood and adolescence in the United Kingdom and Australia. It also evaluated genetic data of more than 45,000 patients with the condition from diverse cohorts in Europe and the United States.

Meghan Daley is a NASA project manager who leads teams to develop and integrate simulations for robotic operations to prepare astronauts on the ISS and beyond. We'll be spotlighting five honorees each week throughout October