The U.S. military conducted "self-defense strikes" against Houthi missiles and a launcher prepared to fire from Yemen toward the Red Sea on Wednesday, U.S. Central Command announced. Between 12 a.m. and 6:45 p.m. local time on Wednesday, four self-defense strikes were launched in response to seven mobile Houthi anti-ship cruise missiles and one mobile anti-ship ballistic missile launcher aimed at the Red Sea, the agency said. Also, in an act of self-defense, CENTCOM said its forces shot down a one-way attack unmanned aircraft system. U.S. Central Command announced more "self-defense strikes" against Houthi terrorists in Yemen after American forces located missiles and a launcher prepared to fire toward the Red Sea. The missiles, launchers and the unmanned aircraft system were all determined to have originated from Houthi-controlled areas of Yemen.

Yemen's Houthi rebels say they have targeted what they claim to be an Israeli cargo ship, the MSC Silver, in the Gulf of Aden near the entrance to the Red Sea with a number of missiles. Houthi military spokesperson Yahya Sarea did not elaborate, but in a statement on Tuesday said the group had also used drones to target a number of United States warships in the Red Sea and Arabian Sea as well as sites in the southern Israeli resort town of Eilat. However, the British maritime security firm Ambrey said the container ship targeted by the Houthis on Tuesday was Liberia-flagged and headed for Somalia. The operator was publicly listed as [in] cooperation with ZIM and regularly called [at] Israeli ports," the Ambrey advisory note said. Zim Integrated Shipping Services Ltd, commonly known as ZIM, is a publicly held Israeli international cargo shipping company based in Israel.

Former Acting Defense Secretary Chris Miller joined'Fox & Friends' to discuss the latest on the escalation in the Middle East as the U.S. continues to strike Iranian proxies. Despite a month of U.S.-led airstrikes, Yemen's Iran-backed Houthi rebels remain capable of launching significant attacks -- just this week, they seriously damaged a ship in a crucial strait and apparently downed an American drone worth tens of millions of dollars. The continued assaults by the Houthis on shipping through the crucial Red Sea corridor -- the Bab el-Mandeb Strait -- against the backdrop of Israel's war on Hamas in the Gaza Strip underscore the challenges in trying to stop the guerrilla-style attacks that have seen them hold onto Yemen's capital and much of the war-ravaged country's north since 2014. Meanwhile, the campaign has boosted the rebels' standing in the Arab world, despite their own human rights abuses in a yearslong stalemated war with several of America's allies in the region. And the longer their attacks go on, analysts warn the greater the risk that disruptions to international shipping will begin to weigh down on the global economy.

If the Houthis' claims are confirmed, this will have been the second time the group has shot down an American drone since the Oct. 7 Hamas attack on Israel, and Israel's response, plunged the region into crisis. The downing of a Reaper drone, the mainstay of the American military's aerial surveillance fleet, is another escalation of violence between the United States and Iran-backed groups in Yemen, Iraq and Syria. The episodes have intensified over the past two months, underscoring the risk that the conflict between Israel and Hamas could spiral into a wider war. The United States struck five Houthi military targets, including an undersea drone, in Houthi-controlled areas of Yemen on Saturday, according to a statement from the military's Central Command. The use of the underwater drone is believed to have been the first time that the Houthis have employed such a weapon since they began their campaign against ships in the Red Sea and the Gulf of Aden on Oct. 23, the statement said.

It has been said, arguably, that causality analysis should pave a promising way to interpretable deep learning and generalization. Incorporation of causality into artificial intelligence (AI) algorithms, however, is challenged with its vagueness, non-quantitiveness, computational inefficiency, etc. During the past 18 years, these challenges have been essentially resolved, with the establishment of a rigorous formalism of causality analysis initially motivated from atmospheric predictability. This not only opens a new field in the atmosphere-ocean science, namely, information flow, but also has led to scientific discoveries in other disciplines, such as quantum mechanics, neuroscience, financial economics, etc., through various applications. This note provides a brief review of the decade-long effort, including a list of major theoretical results, a sketch of the causal deep learning framework, and some representative real-world applications in geoscience pertaining to this journal, such as those on the anthropogenic cause of global warming, the decadal prediction of El Niño Modoki, the forecasting of an extreme drought in China, among others. Keywords: Causality, Liang-Kleeman information flow, Causal artificial intelligence, Fuzzy cognitive map, Interpretability, Frobenius-Perron operator, Weather/Climate forecasting 1. Introduction Causality analysis is a fundamental problem in scientific research, as commented by Einstein in 1953 in response to a question on the status quo of science in China at that time (cf. the historical record in Hu, 2005).The recent rush in artificial intelligence (AI) has stimulated enormous interest in causal inference, partly due to the realization that it may take the field to the next level to approach human intelligence (see Pearl, 2018; Bengio, 2019; Schölkopf, 2022). In the fields pertaining to this journal, assessment of the cause-effect relations between dynamic events makes a natural objective for the corresponding researches.

Fox News senior strategic analyst Gen. Jack Keane joins'Fox Report' to discuss strikes in Yemen, Iraq and Syria by the U.S. and U.K forces. Two U.S. officials have confirmed to Fox News that an Air Force MQ-9 Reaper drone crashed near Yemen after Houthi rebels claimed to have shot down an American aircraft. "We can confirm that a U.S. Air Force MQ-9 crashed off the coast of Hodeidah, Yemen, and are investigating the cause," a U.S. official told Fox News. The officials stressed that it was unclear if the Houthis were involved. If they are, it would be the second time since November 2023 that the Iranian-backed militant group has taken out a Reaper drone, which has a wingspan of 66 feet and costs about 32 million.

Virtual sensing techniques allow for inferring signals at new unmonitored locations by exploiting spatio-temporal measurements coming from physical sensors at different locations. However, as the sensor coverage becomes sparse due to costs or other constraints, physical proximity cannot be used to support interpolation. In this paper, we overcome this challenge by leveraging dependencies between the target variable and a set of correlated variables (covariates) that can frequently be associated with each location of interest. From this viewpoint, covariates provide partial observability, and the problem consists of inferring values for unobserved channels by exploiting observations at other locations to learn how such variables can correlate. We introduce a novel graph-based methodology to exploit such relationships and design a graph deep learning architecture, named GgNet, implementing the framework. The proposed approach relies on propagating information over a nested graph structure that is used to learn dependencies between variables as well as locations. GgNet is extensively evaluated under different virtual sensing scenarios, demonstrating higher reconstruction accuracy compared to the state-of-the-art.

While random forests are commonly used for regression problems, existing methods often lack adaptability in complex situations or lose optimality under simple, smooth scenarios. In this study, we introduce the adaptive split balancing forest (ASBF), capable of learning tree representations from data while simultaneously achieving minimax optimality under the Lipschitz class. To exploit higher-order smoothness levels, we further propose a localized version that attains the minimax rate under the H\"older class $\mathcal{H}^{q,\beta}$ for any $q\in\mathbb{N}$ and $\beta\in(0,1]$. Rather than relying on the widely-used random feature selection, we consider a balanced modification to existing approaches. Our results indicate that an over-reliance on auxiliary randomness may compromise the approximation power of tree models, leading to suboptimal results. Conversely, a less random, more balanced approach demonstrates optimality. Additionally, we establish uniform upper bounds and explore the application of random forests in average treatment effect estimation problems. Through simulation studies and real-data applications, we demonstrate the superior empirical performance of the proposed methods over existing random forests.

Tool learning is widely acknowledged as a foundational approach or deploying large language models (LLMs) in real-world scenarios. While current research primarily emphasizes leveraging tools to augment LLMs, it frequently neglects emerging safety considerations tied to their application. To fill this gap, we present $ToolSword$, a comprehensive framework dedicated to meticulously investigating safety issues linked to LLMs in tool learning. Specifically, ToolSword delineates six safety scenarios for LLMs in tool learning, encompassing $malicious$ $queries$ and $jailbreak$ $attacks$ in the input stage, $noisy$ $misdirection$ and $risky$ $cues$ in the execution stage, and $harmful$ $feedback$ and $error$ $conflicts$ in the output stage. Experiments conducted on 11 open-source and closed-source LLMs reveal enduring safety challenges in tool learning, such as handling harmful queries, employing risky tools, and delivering detrimental feedback, which even GPT-4 is susceptible to. Moreover, we conduct further studies with the aim of fostering research on tool learning safety. The data is released in https://github.com/Junjie-Ye/ToolSword.

Chain-of-Thought (CoT) prompting has marked a significant advancement in enhancing the reasoning capabilities of large language models (LLMs). Previous studies have developed various extensions of CoT, which focus primarily on enhancing end-task performance. In addition, there has been research on assessing the quality of reasoning chains in CoT. This raises an intriguing question: Is it possible to predict the accuracy of LLM outputs by scrutinizing the reasoning chains they generate? To answer this research question, we introduce a benchmark, R2PE, designed specifically to explore the relationship between reasoning chains and performance in various reasoning tasks spanning five different domains. This benchmark aims to measure the falsehood of the final output of LLMs based on the reasoning steps. To make full use of information in multiple reasoning chains, we propose the process discernibility score (PDS) framework that beats the answer-checking baseline by a large margin. Concretely, this resulted in an average of 5.1% increase in the F1 score across all 45 subsets within R2PE. We further demonstrate our PDS's efficacy in advancing open-domain QA accuracy. Data and code are available at https://github.com/XinXU-USTC/R2PE.