A common challenge for decision makers is selecting actions whose rewards are unknown and evolve over time based on prior policies. For instance, repeated use may reduce an action's effectiveness (habituation), while inactivity may restore it (recovery). These nonstationarities are captured by the Reducing or Gaining Unknown Efficacy (ROGUE) bandit framework, which models real-world settings such as behavioral health interventions. While existing algorithms can compute sublinear regret policies to optimize these settings, they may not provide sufficient exploration due to overemphasis on exploitation, limiting the ability to estimate population-level effects. This is a challenge of particular interest in micro-randomized trials (MRTs) that aid researchers in developing just-in-time adaptive interventions that have population-level effects while still providing personalized recommendations to individuals. In this paper, we first develop ROGUE-TS, a Thompson Sampling algorithm tailored to the ROGUE framework, and provide theoretical guarantees of sublinear regret. We then introduce a probability clipping procedure to balance personalization and population-level learning, with quantified trade-off that balances regret and minimum exploration probability. Validation on two MRT datasets concerning physical activity promotion and bipolar disorder treatment shows that our methods both achieve lower regret than existing approaches and maintain high statistical power through the clipping procedure without significantly increasing regret. This enables reliable detection of treatment effects while accounting for individual behavioral dynamics. For researchers designing MRTs, our framework offers practical guidance on balancing personalization with statistical validity.

Retrieval-Augmented Generation (RAG) systems empower large language models (LLMs) with external knowledge, yet struggle with efficiency-accuracy trade-offs when scaling to large knowledge graphs. Existing approaches often rely on monolithic graph retrieval, incurring unnecessary latency for simple queries and fragmented reasoning for complex multi-hop questions. To address these challenges, this paper propose SPLIT-RAG, a multi-agent RAG framework that addresses these limitations with question-driven semantic graph partitioning and collaborative subgraph retrieval. The innovative framework first create Semantic Partitioning of Linked Information, then use the Type-Specialized knowledge base to achieve Multi-Agent RAG. The attribute-aware graph segmentation manages to divide knowledge graphs into semantically coherent subgraphs, ensuring subgraphs align with different query types, while lightweight LLM agents are assigned to partitioned subgraphs, and only relevant partitions are activated during retrieval, thus reduce search space while enhancing efficiency. Finally, a hierarchical merging module resolves inconsistencies across subgraph-derived answers through logical verifications. Extensive experimental validation demonstrates considerable improvements compared to existing approaches.

This paper presents the first empirical demonstration of controllable locality in transformer language models, a novel architectural framework that enables continuous control over the degree of representation localization through a tunable locality dial parameter. Unlike traditional language models that rely exclusively on distributed representations, our approach allows dynamic interpolation between highly interpretable localist encodings and efficient distributed representations without requiring model retraining. We conducted experiments on the WikiText corpus using a two-layer transformer architecture, systematically varying the locality parameter λ across the full spectrum from 1.0 (fully localist) to 0.0 (fully distributed). Our results demonstrate that localist configurations achieve dramatically lower attention entropy, with λ = 1.0 yielding 5.36 bits compared to 7.18 bits at λ = 0.0, while maintaining substantially higher pointer fidelity scores reflecting stronger alignment with rule-specified targets. Prediction experiments reveal that intermediate locality values optimize the tradeoff between interpretability and performance, with λ = 0.6 achieving test perplexity of 4.65 and accuracy of 84.7%. These findings establish that localist language models provide a practical framework for applications in regulated domains requiring both transparency and capability, offering precise mathematical control over the interpretability-performance spectrum through explicit penalty thresholds and information-theoretic design principles.

In an effort to counter the increasing IoT botnet-based attacks, state-of-the-art deep learning methods have been proposed and have achieved impressive detection accuracy. However, their computational intensity restricts deployment on resource-constrained IoT devices, creating a critical need for lightweight detection models. A common solution to this challenge is model compression via quantization. This study proposes a VAE-MLP model framework where an MLP-based classifier is trained on 8-dimensional latent vectors derived from the high-dimensional train data using the encoder component of a pretrained variational autoencoder (VAE). Two widely used quantization strategies--Quantization-Aware Training (QAT) and Post-Training Quantization (PTQ)--are then systematically evaluated in terms of their impact on detection performance, storage efficiency, and inference latency using two benchmark IoT botnet datasets--N-BaIoT and CICIoT2022. The results revealed that, with respect to detection accuracy, the QAT strategy experienced a more noticeable decline,whereas PTQ incurred only a marginal reduction compared to the original unquantized model. Furthermore, PTQ yielded a 6x speedup and 21x reduction in size, while QAT achieved a 3x speedup and 24x compression, demonstrating the practicality of quantization for device-level IoT botnet detection.

Artificial Intelligence (AI) and Large Language Models (LLMs) are revolutionizing today's business practices; however, their adoption within small and medium-sized enterprises (SMEs) raises serious trust, ethical, and technical issues. In this perspective paper, we introduce a structured, multi-phased framework, "SME-TEAM" for the secure and responsible use of these technologies in SMEs. Based on a conceptual structure of four key pillars, i.e., Data, Algorithms, Human Oversight, and Model Architecture, SME-TEAM bridges theoretical ethical principles with operational practice, enhancing AI capabilities across a wide range of applications in SMEs. Ultimately, this paper provides a structured roadmap for the adoption of these emerging technologies, positioning trust and ethics as a driving force for resilience, competitiveness, and sustainable innovation within the area of business analytics and SMEs.

Jensen Huang, the head of the world's most valuable company Nvidia, says King Charles III personally handed him a copy of a speech he delivered in 2023 that included a warning about the dangers of artificial intelligence. He said, there's something I want to talk to you about. And he handed me a letter, Huang told the BBC, speaking after receiving the 2025 Queen Elizabeth Prize for Engineering in a ceremony at St James's Palace. The letter was a copy of the speech delivered by the King in 2023 at the world's first AI Summit, held at Bletchley Park . In it the monarch said that the risks of AI needed to be tackled with a sense of urgency, unity and collective strength.

New POV video shows a mutually beneficial relationship between remoras and the gentle giants. Breakthroughs, discoveries, and DIY tips sent every weekday. In addition to its pod, the sizable cetacean generally hosts dozens of remoras. Also known as a suckerfish, these evolutionary wonders in the family hitch rides on whales in order to make a meal of the sea lice and other crustaceans that also make a home on the marine mammal's skin. To accomplish this, the remora possesses a distinctive, oval dorsal fin that functions like an adapted suction cup.

How doctor's husband died the day he was due to go to prison for killing daughter, 2, in hot car - as eldest daughter, 17, vows to continue lawsuit against widow Rising Republican's future now in tatters as he's blamed for electoral bloodbath I keep hearing the same mortifying whisper about Meghan and Harry... their American dream is about to come crumbling down: MEGYN KELLY Baby girl with horrifying side effects, mom couldn't breathe and dad seriously sick... after simple error turned dream home into a death trap I was an undercover seductress ensnaring the world's most powerful men. Famous American writer's son, 19, arrested over alleged plot to bomb Detroit gay bars in ISIS terror attack Trump's Ice Maiden steps in to save knifed billionaire's NASA nomination: 'Knock it off' Why screaming female migrant who shouted'Help me, I have papers!' was arrested by ICE at Salt Lake City airport Inside Kate and William's forever home: Princess is kitting out Forest Lodge in her preferred'classic contemporary style' to create a'lovely but absolutely inoffensive' look Somali-American who said protecting illegal migrants from Trump was top priority LOSES bid to become America's wokest mayor Emotional Christina Applegate reflects on boyfriend's shock death from drug overdose in rare interview My girlfriend's new body modification is repulsive. She says she did it for me... This Leftist election landslide was caused by the same vile disease that's triggered a GOP civil war. The murder that haunts the Kennedys: Martha Moxley's loved ones reveal their truth in the FREE The Crime Desk newsletter... as accused cousin cleared in killing breaks cover Taylor Momsen admits Gossip Girl role was'killing' her during'long battle' to quit hit series Inside Zohran Mamdani's woke, celebrity filled victory party after socialist won NYC mayoral election Warning to tourists as world's first ban on smoking cigarettes is enforced The Maldives has become the first nation in the world to impose a generational ban on smoking.

A planet is about to be destroyed by the collapse of a binary star system in Slow Gods, Claire North's first venture into classic science fiction. It's bad luck for those living on Adjumir, which is set to be obliterated Claire North is a successful and prolific novelist, writing under three separate names, but this is their first shift into classic science fiction, i.e. a novel with spaceships in it. I loved the title of this book, Slow Gods, and I loved the cover art. All of which is to say that I went in with high hopes. It begins: "My name is Mawukana na-Vdnaze, and I am a very poor copy of myself."

In addition to affordability, New York City's mayor-elect will be forced to reckon with the NYPD's sweeping mass surveillance operations. New York City mayor-elect Zohran Mamdani may have an ambitious policy agenda, but overhauling the self-governing and deeply dysfunctional behemoth that is the New York City Police Department is not on the list. Mamdani surprised supporters by asking current Police Commissioner Jessica Tisch to stay on after his inauguration early next year. Tisch, a technocrat heir to a vast real estate fortune, clashes with Mamdani on several fronts, including policy (she believes New York State's bail reforms caused rising crime) and the geopolitics that inevitably make their way into New York City's streets. One area where Mamdani is guaranteed to clash with Tisch is on the NYPD's massive technical surveillance apparatus and intelligence-gathering methods, which have metastasized since 9/11 to levels that rival the capabilities of a midsize country.