Many problems in navigation and tracking require increasingly accurate characterizations of the evolution of uncertainty in nonlinear systems. Nonlinear uncertainty propagation approaches based on Gaussian mixture density approximations offer distinct advantages over sampling based methods in their computational cost and continuous representation. State-of-the-art Gaussian mixture approaches are adaptive in that individual Gaussian mixands are selectively split into mixtures to yield better approximations of the true propagated distribution. Despite the importance of the splitting process to accuracy and computational efficiency, relatively little work has been devoted to mixand selection and splitting direction optimization. The first part of this work presents splitting methods that preserve the mean and covariance of the original distribution. Then, we present and compare a number of novel heuristics for selecting the splitting direction. The choice of splitting direction is informed by the initial uncertainty distribution, properties of the nonlinear function through which the original distribution is propagated, and a whitening based natural scaling method to avoid dependence of the splitting direction on the scaling of coordinates. We compare these novel heuristics to existing techniques in three distinct examples involving Cartesian to polar coordinate transformation, Keplerian orbital element propagation, and uncertainty propagation in the circular restricted three-body problem.

Veteran and former intel officer Don Bramer joined Fox & Friends First to discuss his reaction to Trump tapping Keith Kellogg to be his Ukraine-Russia envoy and the Biden admin working with the Trump team on peace in the Middle East. Following an overnight missile and drone attack by Russia targeting Ukraine's key energy infrastructure, Russian President Vladimir Putin now says that government buildings in Kyiv could be targeted next using a new hypersonic missile that could also potentially reach the U.S. Russian attacks have not so far struck "decision-making centers" in the Ukrainian capital as Kyiv is heavily protected by air defenses. But Putin says Russia's Oreshnik hypersonic missile, which it fired for the first time at a Ukrainian city last week, is incapable of being intercepted. Russia fired the Oreshnik at the Ukrainian city of Dnipro on Nov. 21, striking a weapons production plant. This was in retaliation against Ukrainian strikes on a Russian military facility in Bryansk two days earlier with U.S. made long-range missiles called ATACMS, after President Biden had given Ukrainian President Volodymyr Zelenskyy permission to do so.

Businesses should be using more robots instead of hiring low-paid migrants, the shadow home secretary has said. The Conservative MP Chris Philp says other countries "use a lot more automation" for tasks such as picking fruit and vegetables "rather than simply importing a lot of low-wage migrant labour". Speaking on BBC Breakfast, he called for more investment in technology to reduce the UK's net migration figures. Philp said: "To give an example, in Australia and New Zealand, they are rolling out robotic and automated fruit- and vegetable-picking equipment, in South Korea they use nine times the number of robots in manufacturing processes compared to us, in America they use a lot more modular construction which is much faster and much more efficient. "There's a lot of things British industry can do to grow without needing to import large numbers of low-wage migrants." At an impromptu press conference on Wednesday, Kemi Badenoch, the Conservative leader, said her party had got it wrong on immigration. She promised a review of "every policy, treaty and part of our legal framework" including the role of the European convention on human rights (ECHR) and the Human Rights Act. Get the day's headlines and highlights emailed direct to you every morning She said her party still believed in a "deterrent" to irregular migration but did not commit to restoring the Rwanda scheme scrapped by Labour, even though Philp called for it to be reinstated two weeks ago. He said on Thursday that Labour had "cancelled the Rwanda scheme before it even started". Philp was asked about reports that under the Conservatives, ministers had been examining using a giant wave machine to deter Channel crossings. He told the BBC: "I don't recall ever having seriously looked at that idea.

Third of NI adults visit porn sites, Ofcom finds Getty ImagesA new Ofcom report finds over 430,000 adults in Northern Ireland visited "pornographic content services" online in May 2024 Adults in Northern Ireland are more likely to look at pornography online than those in any other part of the UK. That is according to new research published by the communications regulator Ofcom. It said that more than 430,000 adults in Northern Ireland visited "pornographic content services" online in May 2024 - more than one third of the adult population. That was higher than the proportion of adults viewing similar content in Wales, Scotland and England. The figures come from Ofcom's Online Nation report for 2024, which looks into the UK's digital habits.

The abundance of complex and interconnected healthcare data offers numerous opportunities to improve prediction, diagnosis, and treatment. Graph-structured data, which includes entities and their relationships, is well-suited for capturing complex connections. Effectively utilizing this data often requires strong and efficient learning algorithms, especially when dealing with limited labeled data. It is increasingly important for downstream tasks in various domains to utilize self-supervised learning (SSL) as a paradigm for learning and optimizing effective representations from unlabeled data. In this paper, we thoroughly review SSL approaches specifically designed for graph-structured data in healthcare applications. We explore the challenges and opportunities associated with healthcare data and assess the effectiveness of SSL techniques in real-world healthcare applications. Our discussion encompasses various healthcare settings, such as disease prediction, medical image analysis, and drug discovery. We critically evaluate the performance of different SSL methods across these tasks, highlighting their strengths, limitations, and potential future research directions. Ultimately, this review aims to be a valuable resource for both researchers and practitioners looking to utilize SSL for graph-structured data in healthcare, paving the way for improved outcomes and insights in this critical field. To the best of our knowledge, this work represents the first comprehensive review of the literature on SSL applied to graph data in healthcare.

The widespread use of generative AI has led to multiple applications of AI-generated text and media to potentially enhance learning outcomes. However, there are a limited number of well-designed experimental studies investigating the impact of learning gains and affective feedback from AI-generated media compared to traditional media (e.g., text from documents and human recordings of video). The current study recruited 500 participants to investigate adult learners recall and recognition performances as well as their affective feedback on the AI-generated synthetic video, using a mixed-methods approach with a pre-and post-test design. Specifically, four learning conditions, AI-generated framing of human instructor-generated text, AI-generated synthetic videos with human instructor-generated text, human instructor-generated videos, and human instructor-generated text frame (baseline), were considered. The results indicated no statistically significant difference amongst conditions on recall and recognition performance. In addition, the participants affective feedback was not statistically significantly different between the two video conditions. However, adult learners preferred to learn from the video formats rather than text materials.

We introduce Orthus, an autoregressive (AR) transformer that excels in generating images given textual prompts, answering questions based on visual inputs, and even crafting lengthy image-text interleaved contents. Unlike prior arts on unified multimodal modeling, Orthus simultaneously copes with discrete text tokens and continuous image features under the AR modeling principle. The continuous treatment of visual signals minimizes the information loss for both image understanding and generation while the fully AR formulation renders the characterization of the correlation between modalities straightforward. The key mechanism enabling Orthus to leverage these advantages lies in its modality-specific heads -- one regular language modeling (LM) head predicts discrete text tokens and one diffusion head generates continuous image features conditioning on the output of the backbone. We devise an efficient strategy for building Orthus -- by substituting the Vector Quantization (VQ) operation in the existing unified AR model with a soft alternative, introducing a diffusion head, and tuning the added modules to reconstruct images, we can create an Orthus-base model effortlessly (e.g., within mere 72 A100 GPU hours). Orthus-base can further embrace post-training to better model interleaved images and texts. Empirically, Orthus surpasses competing baselines including Show-o and Chameleon across standard benchmarks, achieving a GenEval score of 0.58 and an MME-P score of 1265.8 using 7B parameters. Orthus also shows exceptional mixed-modality generation capabilities, reflecting the potential for handling intricate practical generation tasks.

This short paper gives an overview of the XCSP3 solver implemented in Picat. Picat provides several constraint modules, and the Picat XCSP3 solver uses the sat module. The XCSP3 solver mainly consists of a parser implemented in Picat, which converts constraints from XCSP3 format to Picat. The solver demonstrates the strengths of Picat, a logic-based language, in parsing, modeling, and encoding constraints into SAT. The high performance of the solver in recent XCSP competitions demonstrates the viability of using a SAT solver to solve general constraint satisfaction and optimization problems.

Smart contracts are essential for managing digital assets in blockchain networks, highlighting the need for effective security measures. This paper introduces SmartLLMSentry, a novel framework that leverages large language models (LLMs), specifically ChatGPT with in-context training, to advance smart contract vulnerability detection. Traditional rule-based frameworks have limitations in integrating new detection rules efficiently. In contrast, SmartLLMSentry utilizes LLMs to streamline this process. We created a specialized dataset of five randomly selected vulnerabilities for model training and evaluation. Our results show an exact match accuracy of 91.1% with sufficient data, although GPT-4 demonstrated reduced performance compared to GPT-3 in rule generation. This study illustrates that SmartLLMSentry significantly enhances the speed and accuracy of vulnerability detection through LLMdriven rule integration, offering a new approach to improving Blockchain security and addressing previously underexplored vulnerabilities in smart contracts.

Large language models (LLMs) have demonstrated exceptional performance across a wide variety of domains. Nonetheless, generalist LLMs continue to fall short in reasoning tasks necessitating specialized knowledge. Prior investigations into specialized LLMs focused on domain-specific training, which entails substantial efforts in domain data acquisition and model parameter fine-tuning. To address these challenges, this paper proposes the Way-to-Specialist (WTS) framework, which synergizes retrieval-augmented generation with knowledge graphs (KGs) to enhance the specialized capability of LLMs in the absence of specialized training. In distinction to existing paradigms that merely utilize external knowledge from general KGs or static domain KGs to prompt LLM for enhanced domain-specific reasoning, WTS proposes an innovative "LLM$\circlearrowright$KG" paradigm, which achieves bidirectional enhancement between specialized LLM and domain knowledge graph (DKG). The proposed paradigm encompasses two closely coupled components: the DKG-Augmented LLM and the LLM-Assisted DKG Evolution. The former retrieves question-relevant domain knowledge from DKG and uses it to prompt LLM to enhance the reasoning capability for domain-specific tasks; the latter leverages LLM to generate new domain knowledge from processed tasks and use it to evolve DKG. WTS closes the loop between DKG-Augmented LLM and LLM-Assisted DKG Evolution, enabling continuous improvement in the domain specialization as it progressively answers and learns from domain-specific questions. We validate the performance of WTS on 6 datasets spanning 5 domains. The experimental results show that WTS surpasses the previous SOTA in 4 specialized domains and achieves a maximum performance improvement of 11.3%.