In response to an executive order, President Donald Trump's team will present him with a plan for creating the Golden Dome, a missile defense shield meant to guard against attacks that are increasingly difficult to defeat. This effort will demand innovative thinking, collective will and rapid action. Since my tenure as director of the Missile Defense Agency in the early 2000s, an integrated network of sensors based in space, land and sea paired with ground-based interceptors has effectively deterred rudimentary missile attacks on our homeland from Iran, North Korea and others. But as they continue to improve their capabilities and as we look at a resurgent Russia and aggressive China, we need to build our next-generation missile defense. The window to defeat ballistic missiles heading to targets in the US is less than 40 minutes and can be as brief as 10 or 15 minutes if launched from a submarine closer to its target.

Before we know it summer will be here, but don't wait for the hotter days to arrive to realize that your pool is in a state of disarray. The Aiper Scuba X1, priced at 1,399, is an ideal solution to help get your pool ready for action. The Scuba X1 is essentially the full package when it comes to cleaning your pool, taking care of everything for you. Aiper's robotic pool cleaner can provide everyday maintenance, saving you countless hours and money spent on other solutions. Each morning or evening, you can automate the Aiper Scuba X1 to scrub the pool bottom and walls free of algae and dirt, and scrub the waterline for any hair, leaves, sticks, and any other grime that blew into or otherwise ended up in the water.

Kulim, Malaysia – When tech giant AT&S decided a few years ago that it needed to ramp up production to keep pace with the artificial intelligence (AI) boom, it did not look to its largest manufacturing facilities in China. The Austrian firm's plants in Chongqing and Shanghai – opened in 2022 and 2016, respectively – employ some 9,000 workers between them, churning out high-end components used in everything from consumer electronics to cars. But AT&S was at the same time coming to grips with the risks of concentrating production in one country. Like many tech firms grappling with the disruption of the COVID-19 pandemic and the trade war salvoes between the United States and China, AT&S decided it needed to diversify its supply chains. Malaysia quickly emerged at the top of the company's list of potential locations for its next plant.

With anti-government protests sweeping across Turkey, the authorities have used all technological means to try to curb them, from restricting internet access to using facial recognition to identify protesters, who have been forced to adapt. Amid a ban on protests, nearly 2,000 people have been arrested in connection with the demonstrations that erupted on March 19 following the detention of Istanbul's mayor Ekrem Imamoglu on graft charges. As well as those apprehended in the streets, many others have been arrested in predawn raids at their homes after being identified from footage or photos taken by the police during the demonstrations.

AI may be a dirty word in Hollywood, but Mr Mooser says their version of the technology is "clean." "Artists should be at the table," he says, adding that it's better to build the tool for filmmakers rather than get "rolled over by big tech companies". Artificial Intelligence has long been depicted as a villain in Hollywood. In "The Terminator," AI used by the US military decides it must destroy everyone on Earth. But it's AI's creators, and not the technology itself, that has received the brunt of real-life criticism.

AI tools will soon be in use in classrooms across England, but the education secretary, Bridget Phillipson, has one big question she wants answered: will they save time? Attending a Department for Education-sponsored hackathon in central London last week, Phillipson listened as developers explained how their tools could compile pupil reports, improve writing samples and even assess the quality of soldering done by trainee electrical engineers. After listening to one developer extol their AI writing analysis tool as "superhuman", able to aggregate all the writing a pupil had ever done, Phillipson asked bluntly: "Do you know how much time it will have saved?" That will be our next step, the developer admitted, less confidently. In an interview with the Guardian, Phillipson said her interest in AI was less futuristic and more practical.

Has Apple, the biggest company in the world, bungled its artificial intelligence strategy? Doubts blew out into the open when one of the company's closest observers, tech analyst John Gruber, earlier this month gave a blistering critique in a blog post titled "Something Is Rotten in the State of Cupertino," referring to the home of Apple's headquarters. The respected analyst and Apple enthusiast said he was furious for not being more skeptical when the company announced last June that its Siri chatbot would be getting a major generative AI upgrade.

We introduce Entropy-Guided Sequence Weighting (EGSW), a novel approach that enhances the exploration-exploitation tradeoff by dynamically assigning weights to generated outputs based on their advantage and entropy for Reinforcement Learning-based Large Language Model fine-tuning. EGSW integrates entropy regularization with advantage-based weighting to balance policy updates, enabling efficient exploration in high-dimensional state spaces. By employing temperature-scaled softmax weighting over sequences, EGSW prioritizing high-reward, high-uncertainty steps while maintaining training stability. Although originally developed to improve Group Relative Policy Optimization (GRPO) during large language model (LLM) fine-tuning, EGSW is generalizable to other reinforcement learning (RL) algorithms and can be implemented in both step-wise and trajectory-wise settings. Empirical evaluations demonstrate that EGSW enhances GRPO reasoning ability, yielding improvements in sample efficiency. Future work will explore the application of EGSW to advanced RL methodologies.

-- We introduce the sequential multi-object robotic grasp sampling algorithm SeqGrasp that can robustly synthesize stable grasps on diverse objects using the robotic hand's partial Degrees of Freedom (DoF). We use SeqGrasp to construct the large-scale Allegro Hand sequential grasping dataset SeqDataset and use it for training the diffusion-based sequential grasp generator SeqDiffuser . We experimentally evaluate SeqGrasp and SeqDiffuser against the state-of-the-art non-sequential multi-object grasp generation method Multi-Grasp in simulation and on a real robot. Furthermore, SeqDiffuser is approximately 1000 times faster at generating grasps than SeqGrasp and MultiGrasp. Generation of dexterous grasps has been studied for a long time, both from a technical perspective on generating grasps on robots [1]-[11] and understanding human grasping [12]- [15]. Most of these methods rely on bringing the robotic hand close to the object and then simultaneously enveloping it with all fingers. While this strategy often results in efficient and successful grasp generation, it simplifies dexterous grasping to resemble parallel-jaw grasping, thereby underutilizing the many DoF of multi-fingered robotic hands [10]. In contrast, grasping multiple objects with a robotic hand, particularly in a sequential manner that mirrors human-like dexterity, as shown in Figure 1, is still an unsolved problem. In this work, we introduce SeqGrasp, a novel hand-agnostic algorithm for generating sequential multi-object grasps.

The discovery of novel small molecule drugs remains a critical scientific challenge with far-reaching implications for treating diseases and advancing human health. Traditional drug development--especially for small molecule therapeutics--is a highly complex, resource-intensive, and time-consuming process that requires multidisciplinary collaboration. Recent breakthroughs in artificial intelligence (AI), particularly the rise of large language models (LLMs), present a transformative opportunity to streamline and accelerate this process. In this paper, we introduce PharmAgents, a virtual pharmaceutical ecosystem driven by LLM-based multi-agent collaboration. PharmAgents simulates the full drug discovery workflow--from target discovery to preclinical evaluation--by integrating explainable, LLM-driven agents equipped with specialized machine learning models and computational tools. Through structured knowledge exchange and automated optimization, PharmAgents identifies potential therapeutic targets, discovers promising lead compounds, enhances binding affinity and key molecular properties, and performs in silico analyses of toxicity and synthetic feasibility. Additionally, the system supports interpretability, agent interaction, and self-evolvement, enabling it to refine future drug designs based on prior experience. By showcasing the potential of LLM-powered multi-agent systems in drug discovery, this work establishes a new paradigm for autonomous, explainable, and scalable pharmaceutical research, with future extensions toward comprehensive drug lifecycle management.