Drone strikes on the central Sudanese city of el-Obeid have killed up to 23 people, officials and a rights group have reported. Both sources reported on Thursday that overnight attacks had killed several people across the key hub in the southern Kordofan region. The reports concerned the latest in a series of attacks using unmanned aircraft, illustrating that drone warfare has become an increasingly prominent feature in the conflict, which erupted in April 2023 between the military government and paramilitary Rapid Support Forces (RSF). Health officials at el-Obeid Hospital said that 15 were killed and more than 10 wounded in the attacks, which hit residential areas, a funeral gathering and a truck carrying food supplies, as well as areas near army positions. Emergency Lawyers blamed the attack on the RSF, which did not immediately claim responsibility.

The evolution of the human brain has led to the development of complex synaptic plasticity, enabling dynamic adaptation to a constantly evolving world.

Ultrasound (US) is a widely used medical imaging modality due to its real-time capabilities, non-invasive nature, and cost-effectiveness. By reducing operator dependency and enhancing access to complex anatomical regions, robotic ultrasound can help improve workflow efficiency. Recent studies have demonstrated the potential of deep reinforcement learning (DRL) and imitation learning (IL) to enable more autonomous and intelligent robotic ultrasound navigation. However, the application of learning-based robotic ultrasound to computer-assisted surgical tasks, such as anatomy reconstruction and surgical guidance, remains largely unexplored. A key bottleneck for this is the lack of realistic and efficient simulation environments tailored to these tasks.

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Reinforcement learning (RL) for collaborative agents capable of cooperating with humans to accomplish tasks has long been a central goal in the RL community. While prior approaches have made progress in adapting collaborative agents to diverse human partners, they often focus solely on optimizing task performance and overlook human preferences--despite the fact that such preferences often diverge from the reward-maximization objective of the environment. Addressing this discrepancy poses significant challenges: humans typically provide only a small amount of offline, preference-related feedback and are unable to engage in online interactions, resulting in a distributional mismatch between the agent's online learning process and the offline human data. To tackle this, we formulate the problem as an online&offline reinforcement learning problem that jointly integrates online generalization and offline preference learning, entirely under an offline training regime. We propose a simple yet effective training framework built upon existing RL algorithms that alternates between offline preference learning and online generalization recovery, ensuring the stability and alignment of both learning objectives. We evaluate our approach on a benchmark built upon the Overcooked environment--a standard environment for human-agent collaboration--and demonstrate remarkable performance across diverse preference styles and cooperative scenarios.

Grandparent scams cost victims over $5 million in 2025, according to the FBI. AI voice-cloning tools now mimic grandchildren's voices to make fake emergencies more convincing.

In computational neuroscience, models representing single-neuron in-vivo activity have become essential for understanding the functional identities of individual neurons. These models, such as implicit representation methods based on Transformer architectures, contrastive learning frameworks, and variational autoencoders, aim to capture the invariant and intrinsic computational features of single neurons. The learned single-neuron computational role representations should remain invariant across changing environment and are affected by their molecular expression and location. Thus, the representations allow for in vivo prediction of the molecular cell types and anatomical locations of single neurons, facilitating advanced closed-loop experimental designs. However, current models face the problem of limited generalizability.

A joint congressional report describes a spam operation that turned tens of thousands of fake podcasts into search-engine bait for illegal pharmacy and scam sites. For the past year, Spotify has been quietly purging tens of thousands of podcasts that advertised illegal online pharmacies. A report released Thursday by Senator Maggie Hassan, ranking member of the Joint Economic Committee, faults the company for acting only after news outlets exposed the content and her office spent nearly a year pressing for answers. None of what it removed was sent to law enforcement, the report says. Spotify reportedly removed more than 57,000 podcast episodes and 3,000 shows, and took enforcement action against 3,500 accounts, all pushing links to illegal online pharmacies advertising opioids, benzodiazepines, and stimulants for sale without a prescription.

The process is 75 percent more energy efficient--and makes a great cup of joe. More information Adding us as a Preferred Source in Google by using this link indicates that you would like to see more of our content in Google News results. The new method makes espresso in less than three minutes. Breakthroughs, discoveries, and DIY tips sent six days a week. By signing up, you confirm you are 16+, will receive newsletters and promotional content and agree to our Terms of Use and acknowledge the data practices in our Privacy Policy .

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