Breakthroughs, discoveries, and DIY tips sent every weekday. Any pet parent knows that our furry friends can get into accidents. While some like rolling around in the mud are mainly a nuisance, ingesting something that they shouldn't can be very dangerous. In a study published August 18 in the journal Frontiers in Veterinary Science, Doctor Jake Johnson, a cardiology resident at North Carolina State University's College of Veterinary Medicine, presents a case study of a chihuahua that accidentally ingested cocaine. Ahead of the study's publication, the team at Frontiers conducted this Q&A with Dr. Johnson.

Desination will be a red barn on the right 1. Continued on next page 18 Use Case Example rewrite Rewrite the following text to be more light-hearted: -- {very formal text} -- chat The following is a conversation with an AI assistant.

Theorem 1. Suppose that ˆ X In DB models, the commonly used p is either 1 or 2. When p = 2, DURA takes the form as the one in Equation (8) in the main text. If p = 1, we cannot expand the squared score function of the associated DB models as in Equation (4). Therefore, we choose p = 2 . 2 Table 2: Hyperparameters found by grid search. Suppose that k is the number of triplets known to be true in the knowledge graph, n is the embedding dimension of entities. That is to say, the computational complexity of weighted DURA is the same as the weighted squared Frobenius norm regularizer.

Causal traces show that the last token of the subject name is not always decisive.

Dear reviewers, thank you for your time to thoroughly read and review our paper. Y ou said "the addressed problem is relevant and timely" (R4), "useful in practice with many applications" (R1), with Quantifying for the first time the gap between train and test losses in the approach of Ballé et al. (see below) The proposed approach only marginally improves PSNR for hyperprior models. Note that in compression lightweight models are very relevant in practice . Secondly, our empirical results allow us for the first time to quantify the gap between training and test losses . Finally, universal quantization has the potential to lead to much bigger improvements in the future .

Patients who are at clinical high risk (CHR) for schizophrenia need close monitoring of their symptoms to inform appropriate treatments. The Brief Psychiatric Rating Scale (BPRS) is a validated, commonly used research tool for measuring symptoms in patients with schizophrenia and other psychotic disorders; however, it is not commonly used in clinical practice as it requires a lengthy structured interview. Here, we utilize large language models (LLMs) to predict BPRS scores from clinical interview transcripts in 409 CHR patients from the Accelerating Medicines Partnership Schizophrenia (AMP-SCZ) cohort. Despite the interviews not being specifically structured to measure the BPRS, the zero-shot performance of the LLM predictions compared to the true assessment (median concordance: 0.84, ICC: 0.73) approaches human inter- and intra-rater reliability. We further demonstrate that LLMs have substantial potential to improve and standardize the assessment of CHR patients via their accuracy in assessing the BPRS in foreign languages (median concordance: 0.88, ICC: 0.70), and integrating longitudinal information in a one-shot or few-shot learning approach.

The large spread of disinformation across digital platforms creates significant challenges to information integrity. This paper presents a multi-agent system that uses relation extraction to detect disinformation in news articles, focusing on titles and short text snippets. The proposed Agentic AI system combines four agents: (i) a machine learning agent (logistic regression), (ii) a Wikipedia knowledge check agent (which relies on named entity recognition), (iii) a coherence detection agent (using LLM prompt engineering), and (iv) a web-scraped data analyzer that extracts relational triplets for fact checking. The system is orchestrated via the Model Context Protocol (MCP), offering shared context and live learning across components. Results demonstrate that the multi-agent ensemble achieves 95.3% accuracy with an F1 score of 0.964, significantly outperforming individual agents and traditional approaches. The weighted aggregation method, mathematically derived from individual agent misclassification rates, proves superior to algorithmic threshold optimization. The modular architecture makes the system easily scalable, while also maintaining details of the decision processes.

Can you imagine yourself having a conversation with Windows about what your PC is doing? Microsoft's Windows chief can, and is trying to build a future where those interactions are the norm. In an interview with Microsoft AI product manager Christiaan Brinkhoff, the chief of Microsoft's Windows Devices group, Pavan Davuluri, explained that the company is trying to work toward a future where you can access Windows pretty much anywhere via the cloud, then use AI to fine-tune what you're trying to accomplish. Microsoft described the conversation as "the next chapter of Windows," with an eye toward delivering the changes within the next few years. Davuluri described what he hoped the Windows team could accomplish from a strategic level, without targeting any future version of Windows with these goals in mind.

"Noise" protection can be added to content before it's uploaded online. A new technique developed by Australian researchers could stop unauthorised artificial intelligence (AI) systems learning from photos, artwork and other image-based content. Developed by CSIRO, Australia's national science agency, in partnership with the Cyber Security Cooperative Research Centre (CSCRC) and the University of Chicago, the method subtly alters content to make it unreadable to AI models while remaining unchanged to the human eye. This could help artists, organisations and social media users protect their work and personal data from being used to train AI systems or create deepfakes. For example, a social media user could automatically apply a protective layer to their photos before posting, preventing AI systems from learning facial features for deepfake creation.

Reasoning-augmented search agents such as Search-R1, trained via reinforcement learning with verifiable rewards (RLVR), demonstrate remarkable capabilities in multi-step information retrieval from external knowledge sources. These agents address the limitations of their parametric memory by dynamically gathering relevant facts to address complex reasoning tasks. However, existing approaches suffer from a fundamental architectural limitation: they process search queries strictly sequentially, even when handling inherently parallelizable and logically independent comparisons. This sequential bottleneck significantly constrains computational efficiency, particularly for queries that require multiple entity comparisons. To address this critical limitation, we propose ParallelSearch, a novel reinforcement learning framework that empowers large language models (LLMs) to recognize parallelizable query structures and execute multiple search operations concurrently. Our approach introduces dedicated reward functions that incentivize the identification of independent query components while preserving answer accuracy through jointly considering correctness, query decomposition quality, and parallel execution benefits. Comprehensive experiments demonstrate that ParallelSearch outperforms state-of-the-art baselines by an average performance gain of 2.9% across seven question-answering benchmarks. Notably, on parallelizable questions, our method achieves a 12.7% performance improvement while requiring only 69.6% of the LLM calls compared to sequential approaches.