In this white paper, we present AlphaEvolve, an evolutionary coding agent that substantially enhances capabilities of state-of-the-art LLMs on highly challenging tasks such as tackling open scientific problems or optimizing critical pieces of computational infrastructure. AlphaEvolve orchestrates an autonomous pipeline of LLMs, whose task is to improve an algorithm by making direct changes to the code. Using an evolutionary approach, continuously receiving feedback from one or more evaluators, AlphaEvolve iteratively improves the algorithm, potentially leading to new scientific and practical discoveries. We demonstrate the broad applicability of this approach by applying it to a number of important computational problems. When applied to optimizing critical components of large-scale computational stacks at Google, AlphaEvolve developed a more efficient scheduling algorithm for data centers, found a functionally equivalent simplification in the circuit design of hardware accelerators, and accelerated the training of the LLM underpinning AlphaEvolve itself. Furthermore, AlphaEvolve discovered novel, provably correct algorithms that surpass state-of-the-art solutions on a spectrum of problems in mathematics and computer science, significantly expanding the scope of prior automated discovery methods (Romera-Paredes et al., 2023). Notably, AlphaEvolve developed a search algorithm that found a procedure to multiply two $4 \times 4$ complex-valued matrices using $48$ scalar multiplications; offering the first improvement, after 56 years, over Strassen's algorithm in this setting. We believe AlphaEvolve and coding agents like it can have a significant impact in improving solutions of problems across many areas of science and computation.

A suspected Israeli drone attack on a car in southern Lebanon has killed a commander from a coalition of Palestinian armed groups as tensions remain high along the Israel-Lebanon border. The attack targeted a car in the city of Sidon on Wednesday morning, killing Khalil al-Maqdah, a senior officer of Al-Aqsa Martyrs Brigades. A Hamas commander was also killed in the same region earlier this month. Meanwhile, the Israeli army launched a series of overnight air raids targeting what it said were ammunition depots belonging to Lebanon's Hezbollah group in the country's Bekaa region, killing one person and wounding at least 20 others. Hezbollah said it launched dozens of rockets towards northern Israel and the occupied Golan Heights.

Transformers are vital assets for the reliable and efficient operation of power and energy systems. They support the integration of renewables to the grid through improved grid stability and operation efficiency. Monitoring the health of transformers is essential to ensure grid reliability and efficiency. Thermal insulation ageing is a key transformer failure mode, which is generally tracked by monitoring the hotspot temperature (HST). However, HST measurement is complex and expensive and often estimated from indirect measurements. Existing computationally-efficient HST models focus on space-agnostic thermal models, providing worst-case HST estimates. This article introduces an efficient spatio-temporal model for transformer winding temperature and ageing estimation, which leverages physics-based partial differential equations (PDEs) with data-driven Neural Networks (NN) in a Physics Informed Neural Networks (PINNs) configuration to improve prediction accuracy and acquire spatio-temporal resolution. The computational efficiency of the PINN model is improved through the implementation of the Residual-Based Attention scheme that accelerates the PINN model convergence. PINN based oil temperature predictions are used to estimate spatio-temporal transformer winding temperature values, which are validated through PDE resolution models and fiber optic sensor measurements, respectively. Furthermore, the spatio-temporal transformer ageing model is inferred, aiding transformer health management decision-making and providing insights into localized thermal ageing phenomena in the transformer insulation. Results are validated with a distribution transformer operated on a floating photovoltaic power plant.

Quantum Neural Networks (QNNs) are a popular approach in Quantum Machine Learning due to their close connection to Variational Quantum Circuits, making them a promising candidate for practical applications on Noisy Intermediate-Scale Quantum (NISQ) devices. A QNN can be expressed as a finite Fourier series, where the set of frequencies is called the frequency spectrum. We analyse this frequency spectrum and prove, for a large class of models, various maximality results. Furthermore, we prove that under some mild conditions there exists a bijection between classes of models with the same area $A = RL$ that preserves the frequency spectrum, where $R$ denotes the number of qubits and $L$ the number of layers, which we consequently call spectral invariance under area-preserving transformations. With this we explain the symmetry in $R$ and $L$ in the results often observed in the literature and show that the maximal frequency spectrum depends only on the area $A = RL$ and not on the individual values of $R$ and $L$. Moreover, we extend existing results and specify the maximum possible frequency spectrum of a QNN with arbitrarily many layers as a function of the spectrum of its generators. If the generators of the QNN can be further decomposed into 2-dimensional sub-generators, then this specification follows from elementary number-theoretical considerations. In the case of arbitrary dimensional generators, we extend existing results based on the so-called Golomb ruler and introduce a second novel approach based on a variation of the turnpike problem, which we call the relaxed turnpike problem.

A general strike has been called across the cities of the occupied West Bank in protest against the assassination of seven members of Hamas, including the deputy head of its political bureau, Saleh al-Arouri. The strike was called by Palestinian armed groups that asked people to stay home on Wednesday and only leave to march in demonstrations against the drone attack on the outskirts of Beirut. The slain men are Saleh al-Arouri, who was also the commander of the Qassam Brigades in the occupied West Bank; Samir Fendi, who commanded the Qassam Brigades in Lebanon; Azzam al-Aqraa, who commanded the Qassam Brigades in southern Lebanon; and members Mahmoud Shaheen, Mohammed al-Rayes, Mohammed Bashasha and Ahmed Hamoud. All seven will be buried in Lebanon. Funerals will be held for Hamoud and Shaheen on Wednesday in the Burj al-Barajneh camp for Palestinian refugees and Taalbaya, respectively.

Detection of rolling-element bearing faults is crucial for implementing proactive maintenance strategies and for minimizing the economic and operational consequences of unexpected failures. However, many existing techniques are developed and tested under strictly controlled conditions, limiting their adaptability to the diverse and dynamic settings encountered in practical applications. This paper presents an efficient real-time convolutional neural network (CNN) for diagnosing multiple bearing faults under various noise levels and time-varying rotational speeds. Additionally, we propose a novel Fisher-based spectral separability analysis (SSA) method to elucidate the effectiveness of the designed CNN model. We conducted experiments on both healthy bearings and bearings afflicted with inner race, outer race, and roller ball faults. The experimental results show the superiority of our model over the current state-of-the-art approach in three folds: it achieves substantial accuracy gains of up to 15.8%, it is robust to noise with high performance across various signal-to-noise ratios, and it runs in real-time with processing durations five times less than acquisition. Additionally, by using the proposed SSA technique, we offer insights into the model's performance and underscore its effectiveness in tackling real-world challenges.

Fault detection and diagnosis of electrical motors are of utmost importance in ensuring the safe and reliable operation of several industrial systems. Detection and diagnosis of faults at the incipient stage allows corrective actions to be taken in order to reduce the severity of faults. The existing data-driven deep learning approaches for machine fault diagnosis rely extensively on huge amounts of labeled samples, where annotations are expensive and time-consuming. However, a major portion of unlabeled condition monitoring data is not exploited in the training process. To overcome this limitation, we propose a foundational model-based Active Learning framework that utilizes less amount of labeled samples, which are most informative and harnesses a large amount of available unlabeled data by effectively combining Active Learning and Contrastive Self-Supervised Learning techniques. It consists of a transformer network-based backbone model trained using an advanced nearest-neighbor contrastive self-supervised learning method. This approach empowers the backbone to learn improved representations of samples derived from raw, unlabeled vibration data. Subsequently, the backbone can undergo fine-tuning to address a range of downstream tasks, both within the same machines and across different machines. The effectiveness of the proposed methodology has been assessed through the fine-tuning of the backbone for multiple target tasks using three distinct machine-bearing fault datasets. The experimental evaluation demonstrates a superior performance as compared to existing state-of-the-art fault diagnosis methods with less amount of labeled data.

The Legend of Zelda, one of the most successful and beloved gaming franchises of all time, is being made into a live-action film – and with such iconic characters as Link, Princess Zelda, the demonic Ganon and that one superhot half-fish prince everyone was in love earlier in the year, it's no wonder that the internet has absolutely exploded with people suggesting which actors should play them. So, we here at the Guardian thought we would put together our own dream cast for the upcoming flick. Tom Holland trends as fans discuss the casting of Link in upcoming live-action'Legend of Zelda' movie. However, given the wild success of the two most recent iterations of the series, Breath of the Wild and Tears of the Kingdom, it seems likely that that film will borrow heavily from their roster of characters. Although there are actually four great fairies in BotW and TotK, an amalgamation of these powerful and delightfully camp guardians would make a great cameo when our hero, Link, is in need of healing and magical help.

This article presents a comprehensive overview of the digital twin technology and its capability levels, with a specific focus on its applications in the wind energy industry. It consolidates the definitions of digital twin and its capability levels on a scale from 0-5; 0-standalone, 1-descriptive, 2-diagnostic, 3-predictive, 4-prescriptive, 5-autonomous. It then, from an industrial perspective, identifies the current state of the art and research needs in the wind energy sector. The article proposes approaches to the identified challenges from the perspective of research institutes and offers a set of recommendations for diverse stakeholders to facilitate the acceptance of the technology. The contribution of this article lies in its synthesis of the current state of knowledge and its identification of future research needs and challenges from an industry perspective, ultimately providing a roadmap for future research and development in the field of digital twin and its applications in the wind energy industry.