The United States on Tuesday imposed a new round of sanctions against 10 entities and four individuals for their involvement in procuring materials for the production of drones in Iran. The sanctions target a network spanning Iran, Malaysia, Hong Kong, and Indonesia led by Hossein Hatefi Ardakani, according to the U.S. State and Treasury Department. Ardakani and Gary Lam, who worked for a Chinese company, and their co-conspirators were named as defendants in a Justice Department press release. Unmanned aerial vehicles (UAV) drill held by Iranian army in Semnan, Iran on January 5, 2021. The U.S. said these individuals and entities were involved in the procurement of sensitive goods, including U.S.-origin electronic components, for one-way attack drones produced by the Islamic Revolutionary Guard Corps Aerospace Force Self Sufficiency Jihad Organization and its drone program.

Fox News Flash top headlines are here. Check out what's clicking on Foxnews.com. Vice President Kamala Harris is emphasizing abortion as a key issue in the run-up to the 2024 election, preparing numerous rallies around the administration's pro-choice message. Harris rolled out the Fight for Our Reproductive Freedoms Tour this week as President Biden's team ramps up efforts for the upcoming election year. "I will continue to fight for our fundamental freedoms while bringing together those throughout America who agree that every woman should have the right to make decisions about her own body -- not the government," Harris said in a statement.

Ireland senator weighs in on bill to'restrict' speech for the'common good.' (Credit: Houses of the Oireachtas, June 13, 2023) Ireland's government condemned the recent burning of a hotel meant to house 70 migrants outside of Galway in the west of the country as a suspected arson attack. "I am deeply concerned about recent reports of suspected criminal damage at a number of properties around the country which have been earmarked for accommodating those seeking international protection here, including in County Galway last night," Prime Minister Leo Varadkar said in a statement Sunday. "There is no justification for violence, arson or vandalism in our Republic. Garda [police] investigations are underway." The statement came in response to a fire that erupted Saturday night at the Ross Lake House Hotel in Rosscahill, County Galway, in the west of Ireland, destroying the building.

Here's what 2023 taught me: The year started with Big Tech going all in on generative AI. The runaway success of OpenAI's ChatGPT prompted every major tech company to release its own version. This year might go down in history as the year we saw the most AI launches: Meta's LLaMA 2, Google's Bard chatbot and Gemini, Baidu's Ernie Bot, OpenAI's GPT-4, and a handful of other models, including one from a French open-source challenger, Mistral. But despite the initial hype, we haven't seen any AI applications become an overnight success. Microsoft and Google pitched powerful AI-powered search, but it turned out to be more of a dud than a killer app.

A New York City councilwoman-elect has admitted to using artificial intelligence to communicate with voters and answer media inquiries. Susan Zhuang, a Brooklyn Democrat who won her race to represent a district in the southern portion of the New York City borough, acknowledged her use of popular AI platforms such as ChatGPT after being confronted about the issue by the New York Post, according to a report from the outlet this week. The acknowledgment came after the New York Post sent Zhuang an inquiry asking what "makes someone a New Yorker," with the councilwoman-elect reportedly replying with a 101-word response that made the publication suspicious. "New York City, the concrete jungle where dreams come true. Being a New Yorker means having an unstoppable hustle, unbreakable resilience and unrivaled independence," the response said.

In the new research and innovation (R&I) paradigm, aimed at a transformation towards more sustainable, inclusive and fair pathways to address societal and environmental challenges, and at generating new patterns of specialisation and new trajectories for socioeconomic development, it is essential to provide monitoring systems and tools to map and understand the contribution of R&I policies and projects. To address this transformation, we present the RIS3-MCAT platform, the result of a line of work aimed at exploring the potential of open data, semantic analysis, and data visualisation, for monitoring challenge-oriented smart specialisation in Catalonia. RIS3-MCAT is an interactive platform that facilitates access to R&I project data in formats that allow for sophisticated analyses of a large volume of texts, enabling the detailed study of thematic specialisations and challenges beyond classical classification systems. Its conceptualisation, development framework and use are presented in this paper. Keywords: open data, research and innovation policy, smart specialisation strategies, text mining, data visualisation, scientometrics 1. INTRODUCTION The challenges posed by globalisation, technology, climate change, and the COVID-19 pandemic require significant changes in our way of living. Although large transition costs are associated with a successful attainment of all those challenges, the potential opportunities brought about are enormous (Bigas et al., 2021).

Given the success of ChatGPT, LaMDA and other large language models (LLMs), there has been an increase in development and usage of LLMs within the technology sector and other sectors. While the level in which LLMs has not reached a level where it has surpassed human intelligence, there will be a time when it will. Such LLMs can be referred to as advanced LLMs. Currently, there are limited usage of ethical artificial intelligence (AI) principles and guidelines addressing advanced LLMs due to the fact that we have not reached that point yet. However, this is a problem as once we do reach that point, we will not be adequately prepared to deal with the aftermath of it in an ethical and optimal way, which will lead to undesired and unexpected consequences. This paper addresses this issue by discussing what ethical AI principles and guidelines can be used to address highly advanced LLMs.

GeoAI, or geospatial artificial intelligence, is an exciting new area that leverages artificial intelligence (AI), geospatial big data and massive computing power to solve problems in high automation and intelligence (Li 2020; 2021). The term was first coined at an Association for Computing Machinery (ACM) workshop in 2017 and then quickly picked up by industry giants Microsoft and Esri for providing new ways of analyzing geospatial data in a cloud environment. The rapid advances of GeoAI in both academia and industry are attributed to three factors: (1) the proliferation of geospatial big data has provided abundant information for researchers to study the environment and society; (2) the recent breakthrough in AI and machine learning (especially deep learning) has better positioned AI for complex and realworld problems; and (3) the fast developments in computing technology, such as Graphics Processing Unit computing, have made it possible to run compute-intensive models using big data. GeoAI evolves as AI evolves, but it is not simply an application of AI in geography. Instead, GeoAI is an interdisciplinary field that injects spatial theories and concepts to make AI more powerful and suitable for tackling geospatial problems.

Recently decades have witnessed the empirical success of framing Knowledge Graph (KG) embeddings via language models. However, language model-based KG embeddings are usually deployed as static artifacts, making them difficult to modify post-deployment without re-training after deployment. To address this issue, we propose a new task of editing language model-based KG embeddings in this paper. This task is designed to facilitate rapid, data-efficient updates to KG embeddings without compromising the performance of other aspects. We build four new datasets: E-FB15k237, A-FB15k237, E-WN18RR, and A-WN18RR, and evaluate several knowledge editing baselines demonstrating the limited ability of previous models to handle the proposed challenging task. We further propose a simple yet strong baseline dubbed KGEditor, which utilizes additional parametric layers of the hypernetwork to edit/add facts. Our comprehensive experimental results reveal that KGEditor excels in updating specific facts without impacting the overall performance, even when faced with limited training resources. Code and datasets are available in https://github.com/zjunlp/PromptKG/tree/main/deltaKG.

AI algorithms at the edge demand smaller model sizes and lower computational complexity. To achieve these objectives, we adopt a green learning (GL) paradigm rather than the deep learning paradigm. GL has three modules: 1) unsupervised representation learning, 2) supervised feature learning, and 3) supervised decision learning. We focus on the second module in this work. In particular, we derive new discriminant features from proper linear combinations of input features, denoted by x, obtained in the first module. They are called complementary and raw features, respectively. Along this line, we present a novel supervised learning method to generate highly discriminant complementary features based on the least-squares normal transform (LNT). LNT consists of two steps. First, we convert a C-class classification problem to a binary classification problem. The two classes are assigned with 0 and 1, respectively. Next, we formulate a least-squares regression problem from the N-dimensional (N-D) feature space to the 1-D output space, and solve the least-squares normal equation to obtain one N-D normal vector, denoted by a1. Since one normal vector is yielded by one binary split, we can obtain M normal vectors with M splits. Then, Ax is called an LNT of x, where transform matrix A in R^{M by N} by stacking aj^T, j=1, ..., M, and the LNT, Ax, can generate M new features. The newly generated complementary features are shown to be more discriminant than the raw features. Experiments show that the classification performance can be improved by these new features.