A fundamental problem in the texturing of 3D meshes using pre-trained text-to-image models is to ensure multi-view consistency. State-of-the-art approaches typically use diffusion models to aggregate multi-view inputs, where common issues are the blurriness caused by the averaging operation in the aggregation step or inconsistencies in local features. This paper introduces an optimization framework that proceeds in four stages to achieve multi-view consistency. Specifically, the first stage generates an over-complete set of 2D textures from a predefined set of viewpoints using an MV-consistent diffusion process. The second stage selects a subset of views that are mutually consistent while covering the underlying 3D model. We show how to achieve this goal by solving semi-definite programs. The third stage performs non-rigid alignment to align the selected views across overlapping regions. The fourth stage solves an MRF problem to associate each mesh face with a selected view. In particular, the third and fourth stages are iterated, with the cuts obtained in the fourth stage encouraging non-rigid alignment in the third stage to focus on regions close to the cuts. Experimental results show that our approach significantly outperforms baseline approaches both qualitatively and quantitatively.

The rapid spread of misinformation through social media platforms has raised concerns regarding its impact on public opinion. While misinformation is prevalent in other languages, the majority of research in this field has concentrated on the English language. Hence, there is a scarcity of datasets for other languages, including Turkish. To address this concern, we have introduced the FCTR dataset, consisting of 3238 real-world claims. This dataset spans multiple domains and incorporates evidence collected from three Turkish fact-checking organizations. Additionally, we aim to assess the effectiveness of cross-lingual transfer learning for low-resource languages, with a particular focus on Turkish. We demonstrate in-context learning (zero-shot and few-shot) performance of large language models in this context. The experimental results indicate that the dataset has the potential to advance research in the Turkish language.

Instruction-finetuned Large Language Models inherit clear political leanings that have been shown to influence downstream task performance. We expand this line of research beyond the two-party system in the US and audit Llama Chat in the context of EU politics in various settings to analyze the model's political knowledge and its ability to reason in context. We adapt, i.e., further fine-tune, Llama Chat on speeches of individual euro-parties from debates in the European Parliament to reevaluate its political leaning based on the EUandI questionnaire. Llama Chat shows considerable knowledge of national parties' positions and is capable of reasoning in context. The adapted, party-specific, models are substantially re-aligned towards respective positions which we see as a starting point for using chat-based LLMs as data-driven conversational engines to assist research in political science.

The objective of this study is to characterize inequality in infrastructure quality across urban areas. While a growing of body of literature has recognized the importance of characterizing infrastructure inequality in cities and provided quantified metrics to inform urban development plans, the majority of the existing approaches focus primarily on measuring the quantity of infrastructure, assuming that more infrastructure is better. Also, the existing research focuses primarily on index-based approaches in which the status of infrastructure provision in urban areas is determined based on assumed subjective weights. The focus on infrastructure quantity and use of indices obtained from subjective weights has hindered the ability to properly examine infrastructure inequality as it pertains to urban inequality and environmental justice considerations. Recognizing this gap, we propose a machine learning-based approach in which infrastructure features that shape environmental hazard exposure are identified and we use the weights obtained by the model to calculate an infrastructure quality provision for spatial areas of cities and accordingly, quantify the extent of inequality in infrastructure quality. The implementation of the model in five metropolitan areas in the U.S. demonstrates the capability of the proposed approach in characterizing inequality in infrastructure quality and capturing city-specific differences in the weights of infrastructure features. The results also show that areas in which low-income populations reside have lower infrastructure quality provision, suggesting the lower infrastructure quality provision as a determinant of urban disparities. Accordingly, the proposed approach can be effectively used to inform integrated urban design strategies to promote infrastructure equity and environmental justice based on data-driven and machine intelligence-based insights.

Uncertainty is critical to reliable decision-making with machine learning. Conformal prediction (CP) handles uncertainty by predicting a set on a test input, hoping the set to cover the true label with at least $(1-\alpha)$ confidence. This coverage can be guaranteed on test data even if the marginal distributions $P_X$ differ between calibration and test datasets. However, as it is common in practice, when the conditional distribution $P_{Y|X}$ is different on calibration and test data, the coverage is not guaranteed and it is essential to measure and minimize the coverage loss under distributional shift at \textit{all} possible confidence levels. To address these issues, we upper bound the coverage difference at all levels using the cumulative density functions of calibration and test conformal scores and Wasserstein distance. Inspired by the invariance of physics across data distributions, we propose a physics-informed structural causal model (PI-SCM) to reduce the upper bound. We validated that PI-SCM can improve coverage robustness along confidence level and test domain on a traffic speed prediction task and an epidemic spread task with multiple real-world datasets.

The broad agreement builds on past international AI agreements. Last year, the United States, China, the European Union, Britain and more than 20 other countries signed onto the so-called Bletchley Declaration, which sought to avoid the existential safety risks of the technology and promote international cooperation on research. However, following criticism that developing nations had been left out of other international AI agreements, the Biden administration pursued a new agreement with the United Nations.

Artificial intelligence has stoked fears and security concerns for years but a new global resolution to safeguard the public may provide much-needed reassurance. The United Nations General Assembly approved a resolution to shield personal data, monitor AI for potential risks including scams, and protect human rights. The resolution - proposed by the US - is non-binding but was agreed upon by all 193 UN member nations and co-sponsored by non-members from 123 countries, including China and Russia. The resolution comes as high-profile tech moguls have expressed concerns about AI's reliability, including Geoffrey Hinton - the'Godfather of AI' - who said last year that he regretted creating the technology and worries that machines could take over. All 193 UN members signed an AI resolution to protect people's data and human rights The White House called the resolution a'historic step' and Vice President Kamala Harris said she and President Joe Biden are committed to establishing AI safeguards The White House praised the resolution, calling it a'historic step' to ensuring'trustworthy' advancements in AI.

The United Nations General Assembly has unanimously adopted the first global resolution on artificial intelligence to encourage the protection of personal data, the monitoring of AI for risks, and the safeguarding of human rights. The resolution, sponsored by the United States and co-sponsored by 123 countries, was adopted by consensus with a bang of the gavel and without a vote on Thursday, meaning it has the support of all 193 UN member nations. "This resolution establishes a path forward on AI where every country can both seize the promise and manage the risks of AI," US Vice President Kamala Harris said in a statement. The resolution is the latest in a series of initiatives by governments around the world to shape AI's development amid fears it could be used to disrupt democratic processes, increase fraud or lead to dramatic job losses, among other harms. "The improper or malicious design, development, deployment and use of artificial intelligence systems … pose risks that could … undercut the protection, promotion and enjoyment of human rights and fundamental freedoms," the measure says.

Beth Barnes and three of her colleagues sit cross-legged in a semicircle on a damp lawn on the campus of the University of California, Berkeley. They are describing their attempts to interrogate artificial intelligence chatbots. "They are, in some sense, these vast alien intelligences," says Barnes, 26, who is the founder and CEO of Model Evaluation and Threat Research (METR), an AI-safety nonprofit. "They know so much about whether the next word is going to be'is' versus'was.' We're just playing with a tiny bit on the surface, and there's all this, miles and miles underneath," she says, gesturing at the potentially immense depths of large language models' capabilities. Researchers at METR look a lot like Berkeley students--the four on the lawn are in their twenties and dressed in jeans or sweatpants.

Rep. Jay Obernolte was selected to lead the House task force on AI. Fox News Digital speaks with the California Republican about his goals for the panel and his own thoughts about the rapidly advancing technology. AI fakery is quickly becoming one of the biggest problems confronting us online. Deceptive pictures, videos and audio are proliferating as a result of the rise and misuse of generative artificial intelligence tools. With AI deepfakes cropping up almost every day, depicting everyone from Taylor Swift to Donald Trump, it's getting harder to tell what's real from what's not.