June's new science fiction includes a space opera from Megan E. O'Keefe Do you like your world ravaged by unstoppable and deadly viruses or technologies? If so, then June is your month, because we have everything from a contagion that makes people lustful to a neural chip that lets us turn off sleep. We've also got an environmental apocalypse from Inga Simpson in The Thinning, and I'm definitely in the mood for a slice of feminist body horror from E.K. Sathue pitched as American Psycho meets The Substance. Elsewhere, we have Megan E. O'Keefe's new space opera, which sounds intriguing, and Taylor Jenkins Reid's look at the 1980s space shuttle programme, Atmosphere. Those dastardly scientists are at it again, this time developing a neural chip that allows you to turn off sleep.

Democratic strategist James Carville said on Wednesday he doesn't buy it when wealthy Jewish donors tell him they're ditching the Democratic Party because of antisemitism among its members. He says they're doing it for a "f------ tax cut." There are two truths about presidential candidates. One: There is no such thing as a perfect candidate. Two: It is very difficult to convince party elites that there are no perfect candidates.

At least four people were killed and 20 were wounded in multiple Russian missile and drone attacks overnight on the Ukrainian capital, Kyiv, local officials have said. Kyiv mayor Vitali Klitschko said on Friday morning search and rescue operations were continuing in several locations. Among the wounded, 16 were admitted to hospital. Ukrainian authorities said Russian forces launched 407 drones and 45 missiles, including cruise and ballistic missiles, of which they succeeded in destroying, respectively, around 200 and 30. "It was a very frightening night. We heard some of the drones go over this area in central Kyiv, giant explosions ringing out across the city, some so loud that they were shaking the glass here of our hotel, we've seen pictures of people who took shelter in the metro stations underground and underground car parks," said Al Jazeera's Charles Stratford, reporting from the Ukrainian capital.

Russia mounted an intense missile and drone barrage of the Ukrainian capital overnight, killing four people, Ukrainian officials said, as powerful explosions reverberated across the city. The attack followed a warning from Russian President Vladimir Putin, conveyed via U.S. leader Donald Trump, that the Kremlin would hit back after Ukrainian drones destroyed several strategic bomber aircraft in attacks deep inside Russia. Kyiv mayor Vitali Klitschko said 20 people were injured, 16 of them in hospital, in addition to the four deaths. The city's metro transport system was disrupted by a Russian strike that hit and damaged a train between stations, Kyiv's military administration said. In the Solomenskiy district, a Russian drone slammed into the side of apartment building, leaving a gaping hole and burn marks, a Reuters photographer at the scene said.

The progress of AI systems such as large language models (LLMs) raises increasingly pressing concerns about their safe deployment. This paper examines the value alignment problem for LLMs, arguing that current alignment strategies are fundamentally inadequate to prevent misuse. Despite ongoing efforts to instill norms such as helpfulness, honesty, and harmlessness in LLMs through fine-tuning based on human preferences, they remain vulnerable to adversarial attacks that exploit conflicts between these norms. I argue that this vulnerability reflects a fundamental limitation of existing alignment methods: they reinforce shallow behavioral dispositions rather than endowing LLMs with a genuine capacity for normative deliberation. Drawing from on research in moral psychology, I show how humans' ability to engage in deliberative reasoning enhances their resilience against similar adversarial tactics. LLMs, by contrast, lack a robust capacity to detect and rationally resolve normative conflicts, leaving them susceptible to manipulation; even recent advances in reasoning-focused LLMs have not addressed this vulnerability. This ``shallow alignment'' problem carries significant implications for AI safety and regulation, suggesting that current approaches are insufficient for mitigating potential harms posed by increasingly capable AI systems.

Maintaining an active lifestyle is vital for quality of life, yet challenging for wheelchair users. For instance, powered wheelchairs face increasing risks of obesity and deconditioning due to inactivity. Conversely, manual wheelchair users, who propel the wheelchair by pushing the wheelchair's handrims, often face upper extremity injuries from repetitive motions. These challenges underscore the need for a mobility system that promotes activity while minimizing injury risk. Maintaining optimal exertion during wheelchair use enhances health benefits and engagement, yet the variations in individual physiological responses complicate exertion optimization. To address this, we introduce PulseRide, a novel wheelchair system that provides personalized assistance based on each user's physiological responses, helping them maintain their physical exertion goals. Unlike conventional assistive systems focused on obstacle avoidance and navigation, PulseRide integrates real-time physiological data-such as heart rate and ECG-with wheelchair speed to deliver adaptive assistance. Using a human-in-the-loop reinforcement learning approach with Deep Q-Network algorithm (DQN), the system adjusts push assistance to keep users within a moderate activity range without under- or over-exertion. We conducted preliminary tests with 10 users on various terrains, including carpet and slate, to assess PulseRide's effectiveness. Our findings show that, for individual users, PulseRide maintains heart rates within the moderate activity zone as much as 71.7 percent longer than manual wheelchairs. Among all users, we observed an average reduction in muscle contractions of 41.86 percent, delaying fatigue onset and enhancing overall comfort and engagement. These results indicate that PulseRide offers a healthier, adaptive mobility solution, bridging the gap between passive and physically taxing mobility options.

Large language models (LLMs) hold promise for sustainable manufacturing, but often hallucinate industrial codes and emission factors, undermining regulatory and investment decisions. We introduce CircuGraphRAG, a retrieval-augmented generation (RAG) framework that grounds LLMs outputs in a domain-specific knowledge graph for the circular economy. This graph connects 117,380 industrial and waste entities with classification codes and GWP100 emission data, enabling structured multi-hop reasoning. Natural language queries are translated into SPARQL and verified subgraphs are retrieved to ensure accuracy and traceability. Compared with Standalone LLMs and Naive RAG, CircuGraphRAG achieves superior performance in single-hop and multi-hop question answering, with ROUGE-L F1 scores up to 1.0, while baseline scores below 0.08. It also improves efficiency, halving the response time and reducing token usage by 16% in representative tasks. CircuGraphRAG provides fact-checked, regulatory-ready support for circular economy planning, advancing reliable, low-carbon resource decision making.

Fine-tuning large language models (LLMs) to adapt to evolving safety policies is costly and impractical. Mechanistic interpretability enables inference-time control through latent activation steering, yet its potential for precise, customizable safety adjustments remains largely untapped. This paper investigates an approach called SafeSteer for guiding the outputs of LLMs by: (i) leveraging category-specific steering vectors for more precise control, (ii) employing a simple, gradient-free unsupervised method to enhance safety steering while preserving text quality, topic relevance, and without explicit refusal, and (iii) accomplishing this without a hard requirement of contrastive pairwise safe data. We also highlight that our method, being simple and effective, aligns with recent studies suggesting that simple techniques often outperform more complex ones in activation steering. We showcase the effectiveness of our approach across various LLMs, datasets, and risk categories, demonstrating its ability to provide precise control, prevent blanket refusals, and guide models toward generating safe content while maintaining topic relevance.

In this short article, I leverage the National Crime Victimization Survey from 1992 to 2022 to examine how income, education, employment, and key demographic factors shape the type of crime victims experience (violent vs property). Using balanced classification splits and logistic regression models evaluated by F1-score, there is an isolation of the socioeconomic drivers of victimization "Group A" models and then an introduction of demographic factors such as age, gender, race, and marital status controls called "Group B" models. The results consistently proves that higher income and education lower the odds of violent relative to property crime, while men younger individuals and racial minorities face disproportionately higher violentcrime risks. On the geographic spectrum, the suburban models achieve the strongest predictive performance with an accuracy of 0.607 and F1 of 0.590, urban areas benefit from adding education and employment predictors and crime in rural areas are still unpredictable using these current factors. The patterns found in this study shows the need for specific interventions like educational investments in metropolitan settings economic support in rural communities and demographicaware prevention strategies.

Medical image classification is crucial for diagnosis and treatment, benefiting significantly from advancements in artificial intelligence. The paper reviews recent progress in the field, focusing on three levels of solutions: basic, specific, and applied. It highlights advances in traditional methods using deep learning models like Convolutional Neural Networks and Vision Transformers, as well as state-of-the-art approaches with Vision Language Models. These models tackle the issue of limited labeled data, and enhance and explain predictive results through Explainable Artificial Intelligence.