"No one wants to invest, but Egypt is too big to fail," Mr. Landis said, explaining that the United States and I.M.F. are unlikely to let the country default on its 165 billion of foreign loans given its strategic and political importance. The drop in shipping traffic crossing into the Red Sea from the Suez Canal is the latest blow. Between January and August, Egypt brought in an average of 862 million per month in revenue from the canal, which carries 11 percent of global maritime trade. James Swanston, an emerging-markets economist at Capital Economics, said that according to the head of the Suez Canal Authority, traffic is down 30 percent this month from December and revenues are 40 percent weaker compared to 2023 levels. "That's the biggest spillover effect," he said.

Ukrainian President Volodomyr Zelenskyy gives his outlook on the conflict and offers an update on his country's counter-offensive on'Special Report.' Ukrainian President Volodymyr Zelenskyy huddled with corporate executives and world leaders in a frenzied first full day of the World Economic Forum's annual meeting in the Swiss ski resort of Davos, where top officials from the United States, European Union, China, the Middle East and beyond spoke Tuesday about tackling conflict and embracing technology like artificial intelligence. Zelenskyy is endeavoring to keep his country's long and largely stalemated defense against Russia on the minds of political leaders, just as Israel's war with Hamas, which passed the 100-day mark this week, has siphoned off much of the world's attention and sparked concerns about a wider conflict in the Middle East. "It is important that you stand with us, I thank you for your support. It is very important to be here, to boost investment in Ukraine and support our economy," Zelenskyy said at an invitation-only "CEOs for Ukraine" session, according to his office.

The U.S. Navy on Tuesday announced what's considered the first seizure of Iranian weapons bound for Yemen since Houthi rebels began their campaign of attacks against international merchant shipping in the Red Sea two months ago – yet the two Navy SEALs lost at sea during the mission carried out last week still remain missing amid search and rescue efforts. On Jan. 11, 2024, while conducting a flag verification, U.S. CENTCOM Navy forces "conducted a night-time seizure of a dhow conducting illegal transport of advanced lethal aid from Iran to resupply Houthi forces in Yemen as part of the Houthis' ongoing campaign of attacks against international merchant shipping," U.S. Central Command said in a statement Tuesday. "U.S. Navy SEALs operating from USS Lewis B Puller (ESB 3), supported by helicopters and unmanned aerial vehicles (UAVs), executed a complex boarding of the dhow near the coast of Somalia in international waters of the Arabian Sea, seizing Iranian-made ballistic missile and cruise missiles components," the statement said. "Seized items include propulsion, guidance, and warheads for Houthi medium range ballistic missiles (MRBMs) and anti-ship cruise missiles (ASCMs), as well as air defense associated components." On Jan. 10, 2024, a dhow was identified, and an assessment was made that the dhow was in the process of smuggling.

Senior foreign affairs correspondent Greg Palkot provides details on the major strike on an Iraqi militia leader and the U.S.'s response to Houthi attacks in the Red Sea Three armed drones were shot down in Iraq on Tuesday, near where U.S. and other international forces are stationed, officials said. Iraqi Kurdistan's counter-terrorism service said its forces intercepted and shot down the drones over Erbil airport in northern Iraq at around 5:05 a.m. It did not say if there were any casualties or damage to infrastructure. There was no immediate claim of responsibility. Similar previous attacks have been claimed by a group called the Islamic Resistance in Iraq, an umbrella group of Iran-aligned Iraqi militias.

In recent years there has been significant progress in time series anomaly detection. However, after detecting an (perhaps tentative) anomaly, can we explain it? Such explanations would be useful to triage anomalies. For example, in an oil refinery, should we respond to an anomaly by dispatching a hydraulic engineer, or an intern to replace the battery on a sensor? There have been some parallel efforts to explain anomalies, however many proposed techniques produce explanations that are indirect, and often seem more complex than the anomaly they seek to explain. Our review of the literature/checklists/user-manuals used by frontline practitioners in various domains reveals an interesting near-universal commonality. Most practitioners discuss, explain and report anomalies in the following format: The anomaly would be like normal data A, if not for the corruption B. The reader will appreciate that is a type of counterfactual explanation. In this work we introduce a domain agnostic counterfactual explanation technique to produce explanations for time series anomalies. As we will show, our method can produce both visual and text-based explanations that are objectively correct, intuitive and in many circumstances, directly actionable.

Multimodal large language models (MLLMs) have shown remarkable capabilities across a broad range of tasks but their knowledge and abilities in the geographic and geospatial domains are yet to be explored, despite potential wide-ranging benefits to navigation, environmental research, urban development, and disaster response. We conduct a series of experiments exploring various vision capabilities of MLLMs within these domains, particularly focusing on the frontier model GPT-4V, and benchmark its performance against open-source counterparts. Our methodology involves challenging these models with a small-scale geographic benchmark consisting of a suite of visual tasks, testing their abilities across a spectrum of complexity. The analysis uncovers not only where such models excel, including instances where they outperform humans, but also where they falter, providing a balanced view of their capabilities in the geographic domain. To enable the comparison and evaluation of future models, our benchmark will be publicly released.

Despite the great success of large language models (LLMs) in various tasks, they suffer from generating hallucinations. We introduce Truth Forest, a method that enhances truthfulness in LLMs by uncovering hidden truth representations using multi-dimensional orthogonal probes. Specifically, it creates multiple orthogonal bases for modeling truth by incorporating orthogonal constraints into the probes. Moreover, we introduce Random Peek, a systematic technique considering an extended range of positions within the sequence, reducing the gap between discerning and generating truth features in LLMs. By employing this approach, we improved the truthfulness of Llama-2-7B from 40.8\% to 74.5\% on TruthfulQA. Likewise, significant improvements are observed in fine-tuned models. We conducted a thorough analysis of truth features using probes. Our visualization results show that orthogonal probes capture complementary truth-related features, forming well-defined clusters that reveal the inherent structure of the dataset.

Attacks on container ships in the Red Sea have forced hundreds of ships carrying billions of dollars' worth of cargo to avoid the region and the shortcut to the Mediterranean through the Suez Canal, resulting in global increases in inflation and carbon emissions – and much of this disruption comes down to mass-produced, explosive drones made for relatively low cost.

Iran announced Thursday that it has identified the alleged mastermind behind dual suicide bombing attacks that left nearly 100 people dead at a recent memorial for late Gen. Qassem Soleimani, who was killed years ago by a U.S. drone strike. The IRNA news agency carried a statement by the intelligence ministry saying the main suspect who planned the Jan. 3 attack in Kerman, a city southeast of the Iranian capital of Tehran, was a Tajik national known by his alias Abdollah Tajiki. Tajiki reportedly entered the country in mid-December by crossing Iran's southeast border, and left two days before the attack, after making the bombs. One bomber first detonated his explosives at the ceremony in Kerman, then another attacked 20 minutes later as emergency workers and other people tried to help the wounded from the first explosion, according to The Associated Press. The report identified one of the bombers by his family name of Bozrov, saying the man was 24 years old and had Tajik and Israeli nationality.

Synthetic Aperture Radar (SAR) imagery is the primary data type used for sea ice mapping due to its spatio-temporal coverage and the ability to detect sea ice independent of cloud and lighting conditions. Automatic sea ice detection using SAR imagery remains problematic due to the presence of ambiguous signal and noise within the image. Conversely, ice and water are easily distinguishable using multispectral imagery (MSI), but in the polar regions the ocean's surface is often occluded by cloud or the sun may not appear above the horizon for many months. To address some of these limitations, this paper proposes a new tool trained using concurrent multispectral Visible and SAR imagery for sea Ice Detection (ViSual\_IceD). ViSual\_IceD is a convolution neural network (CNN) that builds on the classic U-Net architecture by containing two parallel encoder stages, enabling the fusion and concatenation of MSI and SAR imagery containing different spatial resolutions. The performance of ViSual\_IceD is compared with U-Net models trained using concatenated MSI and SAR imagery as well as models trained exclusively on MSI or SAR imagery. ViSual\_IceD outperforms the other networks, with a F1 score 1.60\% points higher than the next best network, and results indicate that ViSual\_IceD is selective in the image type it uses during image segmentation. Outputs from ViSual\_IceD are compared to sea ice concentration products derived from the AMSR2 Passive Microwave (PMW) sensor. Results highlight how ViSual\_IceD is a useful tool to use in conjunction with PMW data, particularly in coastal regions. As the spatial-temporal coverage of MSI and SAR imagery continues to increase, ViSual\_IceD provides a new opportunity for robust, accurate sea ice coverage detection in polar regions.