As artificial intelligence (AI) continues to advance--particularly in generative models--an open question is whether these systems can replicate foundational models of human social perception. A well-established framework in social cognition suggests that social judgments are organized along two primary dimensions: valence (e.g., trustworthiness, warmth) and dominance (e.g., power, assertiveness). This study examines whether multimodal generative AI systems can reproduce this valence-dominance structure when evaluating facial images and how their representations align with those observed across world regions. Through principal component analysis (PCA), we found that the extracted dimensions closely mirrored the theoretical structure of valence and dominance, with trait loadings aligning with established definitions. However, many world regions and generative AI models also exhibited a third component, the nature and significance of which warrant further investigation. These findings demonstrate that multimodal generative AI systems can replicate key aspects of human social perception, raising important questions about their implications for AI-driven decision-making and human-AI interactions.

In Natural Language Generation (NLG), important information is sometimes omitted in the output text. To better understand and analyse how this type of mistake arises, we focus on RDF-to-Text generation and explore two methods of probing omissions in the encoder output of BART (Lewis et al, 2020) and of T5 (Raffel et al, 2019): (i) a novel parameter-free probing method based on the computation of cosine similarity between embeddings of RDF graphs and of RDF graphs in which we removed some entities and (ii) a parametric probe which performs binary classification on the encoder embeddings to detect omitted entities. We also extend our analysis to distorted entities, i.e. entities that are not fully correctly mentioned in the generated text (e.g. misspelling of entity, wrong units of measurement). We found that both omitted and distorted entities can be probed in the encoder's output embeddings. This suggests that the encoder emits a weaker signal for these entities and therefore is responsible for some loss of information. This also shows that probing methods can be used to detect mistakes in the output of NLG models.

Research data sets are growing to unprecedented sizes and network modeling is commonly used to extract complex relationships in diverse domains, such as genetic interactions involved in disease, logistics, and social communities. As the number of nodes increases in a network, an increasing sparsity of edges is a practical limitation due to memory restrictions. Moreover, many of these sparse networks exhibit very large numbers of nodes with no adjacent edges, as well as disjoint components of nodes with no edges connecting them. A prevalent aim in network modeling is the identification of clusters, or communities, of nodes that are highly interrelated. Several definitions of strong community structure have been introduced to facilitate this task, each with inherent assumptions and biases. We introduce an intuitive objective function for quantifying the quality of clustering results in large sparse networks. We utilize a two-step method for identifying communities which is especially well-suited for this domain as the first step efficiently divides the network into the disjoint components, while the second step optimizes clustering of the produced components based on the new objective. Using simulated networks, optimization based on the new objective function consistently yields significantly higher accuracy than those based on the modularity function, with the widest gaps appearing for the noisiest networks. Additionally, applications to benchmark problems illustrate the intuitive correctness of our approach. Finally, the practicality of our approach is demonstrated in real-world data in which we identify complex genetic interactions in large-scale networks comprised of tens of thousands of nodes. Based on these three different types of trials, our results clearly demonstrate the usefulness of our two-step procedure and the accuracy of our simple objective.

Global Navigation Satellite Systems (GNSS)-based positioning plays a crucial role in various applications, including navigation, transportation, logistics, mapping, and emergency services. Traditional GNSS positioning methods are model-based and they utilize satellite geometry and the known properties of satellite signals. However, model-based methods have limitations in challenging environments and often lack adaptability to uncertain noise models. This paper highlights recent advances in Machine Learning (ML) and its potential to address these limitations. It covers a broad range of ML methods, including supervised learning, unsupervised learning, deep learning, and hybrid approaches. The survey provides insights into positioning applications related to GNSS such as signal analysis, anomaly detection, multi-sensor integration, prediction, and accuracy enhancement using ML. It discusses the strengths, limitations, and challenges of current ML-based approaches for GNSS positioning, providing a comprehensive overview of the field.

Various methods have been proposed to secure access to sensitive information over time, such as the many cryptographic methods in use to facilitate secure communications on the internet. But other methods like steganography have been overlooked which may be more suitable in cases where the act of transmission of sensitive information itself should remain a secret. Multiple techniques that are commonly discussed for such scenarios suffer from low capacity and high distortion in the output signal. This research introduces a novel steganographic approach for concealing a confidential portable document format (PDF) document within a host image by employing the Hybrid Firefly algorithm (HFA) proposed to select the pixel arrangement. This algorithm combines two widely used optimization algorithms to improve their performance. The suggested methodology utilizes the HFA algorithm to conduct a search for optimal pixel placements in the spatial domain. The purpose of this search is to accomplish two main goals: increasing the host image's capacity and reducing distortion. Moreover, the proposed approach intends to reduce the time required for the embedding procedure. The findings indicate a decrease in image distortion and an accelerated rate of convergence in the search process. The resultant embeddings exhibit robustness against steganalytic assaults, hence rendering the identification of the embedded data a formidable undertaking.

Fox News medical contributor Dr. Marc Siegel joins'Fox & Friends' to discuss the benefits of artificial intelligence in the medical industry if used with caution. About 10% of all infants born in the U.S. in 2021 were preterm -- which means they were delivered earlier than 37 weeks of pregnancy, per the Centers for Disease Control and Prevention (CDC). Preterm births also make up about 16% of infant deaths. Now, researchers from Washington University in St. Louis, Missouri, are looking to improve those odds through the use of artificial intelligence. They developed a deep learning model that can predict preterm births by analyzing electrical activity in the woman's uterus during pregnancy -- then they tested the model in a study that was published in the medical journal PLOS One.

Take a seat on the rocket ship and join us as a summer intern within our technology department. We're a global team of builders, listeners and problem-solvers who are relentlessly focused on making life simple, so our customers can get back to growing their business, engaging consumers and doing what they love. At Xplor, the Central Technology Team has one main purpose: to enable and complement the business strategies and goals while solving real problems for our customers and users. We have dozens of applications in our everyday-life verticals that all have their technology uniqueness and their individual purpose. We also use some of the latest technology in Microsoft Azure, AWS, and Containers and are constantly looking to find innovative new ways to meet the challenges of running a unique global business.

The exceptional ability of ChatGPT to engage in human-like conversations and generate outputs ranging from code to music has caused it to gain widespread popularity as an online phenomenon in recent months.However, despite the extensive discussions and attention the chatbot has received, people know relatively little about the individual who created it – Sam Altman, who is also a co-founder of OpenAI.Thus, even as ChatGPT continues to capture the public's imagination, the question of who Sam Altman is remains unanswered. Sam Altman, who is currently 37 years old, was born in 1985 in Chicago, Illinois. He spent his childhood in St. Louis, Missouri. When he was just eight years old, he got gift as Macintosh computer. He quickly learned to program and disassemble due to his precociousness and efficiency.

Just a year ago, Chukurah Ali had fulfilled a dream of owning her own bakery -- Coco's Desserts in St. Louis, Mo. Ali, a single mom, supported her daughter and mother by baking recipes she learned from her beloved grandmother. But last February, all that fell apart, after a car accident left Ali hobbled by injury, from head to knee. "I could barely talk, I could barely move," she says, sobbing. "I felt like I was worthless because I could barely provide for my family."

Vancouver, BC, December 22, 2022--(T-Net)--infinitii ai inc. "Core & Main's Core solutions are leading edge, high value digital platforms for Smart Cities, and we're honored to be an integral part of them," said Jean Charles Phaneuf, CEO of infinitii ai. "Core & Main is a national player in the water and wastewater industry in the U.S. operating over 300 branches nationwide, making them an extraordinary strategic partner for us." Based in St. Louis, Mo., Core & Main is a leading specialized distributor of water, wastewater, storm drainage, treatment plant, geosynthetics and fire protection products, and related services, to municipalities, private water companies and professional contractors across municipal, non-residential and residential end markets nationwide. "Our national metering team has been actively engaged with infinitii ai over the past 14 months and we're pleased to have chosen infinitii ai's flowworks software for our Core wastewater solutions," said Brad Cowles, president of Core & Main.