Data-driven science and computation have advanced immensely to construct complex functional relationships using trainable parameters. However, efficiently discovering interpretable and accurate closed-form expressions from complex dataset remains a challenge. The article presents a novel approach called Explainable Hierarchical Deep Learning Neural Networks or Ex-HiDeNN that uses an accurate, frugal, fast, separable, and scalable neural architecture with symbolic regression to discover closed-form expressions from limited observation. The article presents the two-step Ex-HiDeNN algorithm with a separability checker embedded in it. The accuracy and efficiency of Ex-HiDeNN are tested on several benchmark problems, including discerning a dynamical system from data, and the outcomes are reported. Ex-HiDeNN generally shows outstanding approximation capability in these benchmarks, producing orders of magnitude smaller errors compared to reference data and traditional symbolic regression. Later, Ex-HiDeNN is applied to three engineering applications: a) discovering a closed-form fatigue equation, b) identification of hardness from micro-indentation test data, and c) discovering the expression for the yield surface with data. In every case, Ex-HiDeNN outperformed the reference methods used in the literature. The proposed method is built upon the foundation and published works of the authors on Hierarchical Deep Learning Neural Network (HiDeNN) and Convolutional HiDeNN. The article also provides a clear idea about the current limitations and future extensions of Ex-HiDeNN.

The U.S. Supreme Court's 2022 ruling in Dobbs v. Jackson Women's Health Organization marked a turning point in the national debate over reproductive rights. While the ideological divide over abortion is well documented, less is known about how gender and local sociopolitical contexts interact to shape public discourse. Drawing on nearly 10 million abortion-related posts on X (formerly T witter) from users with inferred gender, ideology and location, we show that gender significantly moderates abortion attitudes and emotional expression, particularly in conservative regions, and independently of ideology. This creates a gender gap in abortion attitudes that grows more pronounced in conservative regions. The leak of the Dobbs draft opinion further intensified online engagement, disproportionately mobilizing pro-abortion women in areas where access was under threat. These findings reveal that abortion discourse is not only ideologically polarized but also deeply structured by gender and place, highlighting the central the role of identity in shaping political expression during moments of institutional disruption. 1 Long a flashpoint in cultural and political battles, abortion debates have come to symbolize broader struggles over bodily autonomy, religious freedom, and gender equality. The 2022 Supreme Court ruling in Dobbs v. Jackson Women's Health Organization, which overturned nearly five decades of federal protections for abortion access established by Roe v. Wade, marked a seismic shift. It not only intensified existing partisan divides ( 1, 2), but also reshaped the legal and political terrain, triggering abrupt policy reversals in many states and catalyzing a realignment in the national debate over reproductive rights. A growing body of research has documented partisan cleavages in public attitudes toward reproductive rights ( 1, 3-7). However, less attention has been paid to the way in which gender and sociopolitical environment jointly shape both opinion formation and patterns of public expression. Recent surveys point to a widening gender gap in political orientation, particularly among younger voters. For example, in the 2024 U.S. presidential election, white men predominantly supported President Trump, while white women preferred Vice President Harris ( 8). Similarly, Gallup polling found a sharp increase in the share of young women identifying as politically liberal and supporting reproductive rights ( 9). While women consistently report higher support for abortion access, particularly in countries with less restrictive policy environments ( 10, 11), men, even those who identify as pro-choice, often show less engagement with the issue ( 11-13). Prior work has also documented gendered modes of engagement in online discourse around reproductive rights ( 1, 2).

In many reinforcement learning (RL) applications, augmenting the task rewards with heuristic rewards that encode human priors about how a task should be solved is crucial for achieving desirable performance. However, because such heuristics are usually not optimal, much human effort and computational resources are wasted in carefully balancing tasks and heuristic rewards. Theoretically rigorous ways of incorporating heuristics rely on the idea of \textit{policy invariance}, which guarantees that the performance of a policy obtained by maximizing heuristic rewards is the same as the optimal policy with respect to the task reward. However, in practice, policy invariance doesn't result in policy improvement, and such methods are known to empirically perform poorly. We propose a new paradigm to mitigate reward hacking and effectively use heuristics based on the practical goal of maximizing policy improvement instead of policy improvement. Our framework, Heuristic Enhanced Policy Optimization (HEPO), effectively leverages heuristics while avoiding the pitfall of prior methods for mitigating reward hacking. HEPO achieves superior performance on standard benchmarks with well-engineered reward functions. More surprisingly, HEPO allows policy optimization to achieve good performance even when heuristics are not well-engineered and designed by non-expert humans, showcasing HEPO's ability to reduce human effort in reward design. % HEPO is a plug-and-play optimization method for leveraging heuristics in reinforcement learning. Code is available at https://github.com/Improbable-AI/hepo.

--Pre-timed traffic signal control, common ly used for operatin g signalized intersections and coordinated arterials, requires tedious manual work for signaling plan creating and updating. When the time -of -day or day -of -week plan s are utilized, one intersection is often associated with multiple plans, leading to further repetitive manual plan parameter inputting. To enable a user-friendly traffic signal control plan management process, this study proposes Chat2SPaT, a method to convert users' semi - structured and ambiguous descriptions on the signal control plan to exact signal phase and timing (SPaT) results, which could further be transformed into structured stage-based or ring -based plans to interact with intelligent transportation system (ITS) software and traffic signal controllers. With curated prompts, Chat2SPaT first leverages large language models' (LLMs) capability of understanding users' plan descriptions and reformulate the plan as a combination of phase sequence and phase attribute results in the json format. Based on LLM outputs, python scripts are designed to locate phases in a cycle, address nuances of traffic signal control, and finally assemble the complete traffic signal control plan. Within a chat, the pipeline can be utilized iteratively to conduct further plan editing. Experiments show that Chat2SPaT can generate plans with an accuracy of over 94% for both English and Chinese cases, using a test dataset with over 300 plan descriptions. As the first benchmark for evaluating LLMs' capability of understanding traffic signal control plan descriptions, Chat2SPaT provides an easy -to -use plan management pipeline for traffic practitioners and researchers, serving as a potential new building block for a more accurate and versatile application of LLMs in the field of ITS. The source codes, prompts and test dataset are openly accessible at https://github.com/yuewangits/Ch Index Terms --Large language model, traffic signal control, signal phase and timing, prompt engineering, intelligent transportation system. Yue Wang, Miao Zhou, Rui Zhuo and Chao Yi are with Zhejiang Dahua Technology Company Ltd., Hangzhou 310053, China (e -mail: wang.yue3@northeastern.edu; Guijing Huang is with Hangzhou AliCloud Apsara Information Technology Co., Ltd., Hangzhou 310000, China (huanggjcs@126.com Zhenliang Ma is with the KTH Roy al Institute of Technology, 100 44 Stockholm, Sweden (e -mail: zhenliang.ma21@gmail.com).

The study assesses Hungary's National AI Strategy and its implementation through the analysis of strategic documents, publicly available financial records, and expert interviews with the Hungarian AI Coalition President and Chief Strategic Advisor to the Government Commissioner for AI. 22 goals from Hungary's strategy were evaluated through conceptual, governance, temporal, and financial dimensions before being benchmarked against Singapore's National AI Strategies (NAIS 1.0 and NAIS 2.0). Key findings include an estimated total of EUR 4.65 billion in AI-related public investment in Hungary. Openly available financial data was found for only half of the evaluated goals, and just three projects made up 98\% of all documented funding. The research also reveals Hungary's implementation challenges, including fragmented execution following ministerial reorganizations and the absence of designated biennial reviews since 2020. Furthermore, the paper provides targeted recommendations for Hungary's forthcoming AI strategy, drawing on Singapore's framework as a reference point. These include adapting to the era of large language models, restructuring the existing triple helix network to foster more effective dialogue and advocacy, and positioning the country as an East-West bridge for automotive AI experimentation.

Millions of Americans may soon face nearly a full month of power blackouts each year, disrupting daily life, businesses, and critical services across the country. White House officials warned on Monday that the retiring power plants and soaring electricity demand could push the US grid to its limits, triggering over 800 hours of power outages annually. From hospitals to data centers, the ripple effects of extended blackouts could impact nearly every part of daily life for US residents. Department of Energy (DOE) Secretary Chris Wright said: 'In the coming years, America's reindustrialization and the AI race will require a significantly larger supply of around-the-clock, reliable, and uninterrupted power. 'President Trump's administration is committed to advancing a strategy of energy addition, and supporting all forms of energy that are affordable, reliable, and secure.'

The story unfolds so rapidly that it can all seem, at a glance, preordained. After transferring to Columbia last fall, as Chungin "Roy" Lee tells it, he used AI to cheat his way through school, used AI to cheat his way through internship interviews at Amazon and Meta--he received offers from both--and in the winter broadcasted his tool on social media. He was placed on probation, suspended, and, more keen on AI than education, dropped out this spring to found a start-up.That start-up, Cluely, markets the ability to "cheat on everything" using an AI assistant that runs in the background during meetings or sales calls. Last month, it finished a 15 million fundraising round led by Andreessen Horowitz, the storied venture-capital firm. Lee unapologetically believes that the arrival of omniscient AI is inevitable, that bots will soon automate every job.

The incident was first revealed in the State Department cable that was dated 3 July and sent to "all diplomatic and consular posts," CBS News reported. The cable stated that a false Signal account was created in mid-June with the display name marco.rubio@state.gov. That account contacted at least five people. "The actor left voicemails on Signal for at least two targeted individuals, and in one instance, sent a text message inviting the individual to communicate on Signal," the cable stated, as reported by CBS. The cable did not identify the individuals that were contacted or what the AI-generated voice of Rubio said in those voicemails.

Palantir, a US data company that works with Israel's defence ministry, has accused British doctors of choosing "ideology over patient interest" after they attacked the firm's contract to process NHS data. Louis Mosley, Palantir's executive vice-president, hit back at the British Medical Association, which recently said the 330m deal to create a single platform for NHS data – ranging from patient data to bed availability – "threatens to undermine public trust in NHS data systems". In a formal resolution the doctors said last month this was because it was unclear how the sensitive data would be processed by Palantir, which was founded by the Trump donor Peter Thiel. They cited the firm's "track record of creating discriminatory policing software in the US" and its "close links to a US government which shows little regard for international law". But Mosley dismissed the attack when he gave evidence to MPs from the Commons science and technology committee on Tuesday. Palantir has also won contracts to handle mass data controlled by the Ministry of Defence, police and local authorities.

This week in tech news, Elon Musk and Donald Trump are back at it, warring over the passage of the president's sweeping tax bill and the Tesla CEO's threat to create a third political party. Whether the richest person in the world is successful in those efforts will largely depend on the recruitment of another star politician. In other news, we want to know if you use generative artificial intelligence to write your personal messages – in what circumstances, and how often? Email tech.editorial@theguardian.com to let us know. Elon Musk and Donald Trump have reignited their feud after the passage of the president's sweeping tax bill on 3 July.