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.

Diffusion-based video editing have reached impressive quality and can transform either the global style, local structure, and attributes of given video inputs, following textual edit prompts. However, such solutions typically incur heavy memory and computational costs to generate temporally-coherent frames, either in the form of diffusion inversion and/or cross-frame attention. In this paper, we conduct an analysis of such inefficiencies, and suggest simple yet effective modifications that allow significant speed-ups whilst maintaining quality. Moreover, we introduce Object-Centric Diffusion, coined as OCD, to further reduce latency by allocating computations more towards foreground edited regions that are arguably more important for perceptual quality. We achieve this by two novel proposals: i) Object-Centric Sampling, decoupling the diffusion steps spent on salient regions or background, allocating most of the model capacity to the former, and ii) Object-Centric 3D Token Merging, which reduces cost of cross-frame attention by fusing redundant tokens in unimportant background regions. Both techniques are readily applicable to a given video editing model \textit{without} retraining, and can drastically reduce its memory and computational cost. We evaluate our proposals on inversion-based and control-signal-based editing pipelines, and show a latency reduction up to 10x for a comparable synthesis quality.

Heritage Foundation researcher Emma Waters joins'Fox & Friends Weekend' to discuss a recent report that a global birth decline is good for the planet. More than 60 heads of state and government and hundreds of business leaders are coming to Switzerland to discuss the biggest global challenges during the World Economic Forum's annual gathering next week, ranging from Israeli President Isaac Herzog to Ukrainian President Volodymyr Zelenskyy. The likes of U.S. Secretary of State Antony Blinken, Chinese Premier Li Qiang, EU Commission President Ursula von der Leyen, French President Emmanuel Macron, U.N. Secretary-General Antonio Guterres and many others will descend on the Alpine ski resort town of Davos on Jan. 15-19, organizers said Tuesday. Attendees have their work cut out for them with two major wars -- the Israel-Hamas conflict and Russia's invasion of Ukraine -- plus problems like climate change, major disruptions to trade in the Red Sea, a weak global economy and misinformation powered by rapidly advancing artificial intelligence in a major election year. Trust has eroded on peace and security, with global cooperation down since 2016 and plummeting since 2020, forum President Borge Brende said at a briefing.

The Israeli army and Hezbollah, based in Lebanon, have again traded fire across the border. The Iran-backed Hezbollah on Tuesday launched a drone attack on an Israeli command base. Israel retaliated with air strikes, while it is also reported to have killed three Hezbollah members in a targeted strike. The rise in attacks across the Israel-Lebanon border is stoking fear that the war in Gaza threatens to spark a regional conflagration. Hezbollah said that it had targeted the "enemy's northern command centre in the city of Safed with several drones" in retaliation for the killing of Hezbollah field commander Wissam al-Tawil in Lebanon on Monday, as well as an attack on Hamas's deputy leader Saleh al-Arouri in Beirut last week.

The increasing use of transformer-based large language models brings forward the challenge of processing long sequences. In document visual question answering (DocVQA), leading methods focus on the single-page setting, while documents can span hundreds of pages. We present GRAM, a method that seamlessly extends pre-trained single-page models to the multi-page setting, without requiring computationally-heavy pretraining. To do so, we leverage a single-page encoder for local page-level understanding, and enhance it with document-level designated layers and learnable tokens, facilitating the flow of information across pages for global reasoning. To enforce our model to utilize the newly introduced document-level tokens, we propose a tailored bias adaptation method. For additional computational savings during decoding, we introduce an optional compression stage using our C-Former model, which reduces the encoded sequence length, thereby allowing a tradeoff between quality and latency. Extensive experiments showcase GRAM's state-of-the-art performance on the benchmarks for multi-page DocVQA, demonstrating the effectiveness of our approach.

Despite a sea of interpretability methods that can produce plausible explanations, the field has also empirically seen many failure cases of such methods. In light of these results, it remains unclear for practitioners how to use these methods and choose between them in a principled way. In this paper, we show that for moderately rich model classes (easily satisfied by neural networks), any feature attribution method that is complete and linear -- for example, Integrated Gradients and SHAP -- can provably fail to improve on random guessing for inferring model behaviour. Our results apply to common end-tasks such as characterizing local model behaviour, identifying spurious features, and algorithmic recourse. One takeaway from our work is the importance of concretely defining end-tasks: once such an end-task is defined, a simple and direct approach of repeated model evaluations can outperform many other complex feature attribution methods.

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 Iraq has condemned the United States after U.S. forces carried out a drone strike in central Baghdad on Thursday that killed a high-ranking militia commander. Iraqi Prime Minister Mohammed Shia al-Sudani said Friday that the U.S. targeting and killing Mushtaq Taleb al-Saidi -- or "Abu Taqwa," the leader of the Harakat Hezbollah al-Nujaba, an Iraqi Shi'ite militant group -- was a violation of Iraqi sovereignty. A U.S. defense official confirmed that Thursday's drone strike on a vehicle containing the militia leader and three other militia members was authorized as he was responsible for recent attacks on U.S. personnel. Sudani said Friday that the U.S. had bypassed the Iraqi government, which is "the body authorized to impose the law." In his statement, he also reiterated calls for U.S. troops to withdraw from the country.

Fox News Flash top headlines are here. Check out what's clicking on Foxnews.com. The Islamic State of Iraq and Syria (ISIS) is claiming responsibility for the suicide bomb attacks in Iran this week, Fox News Digital has learned. ISIS claims to have orchestrated the double suicide bomber attack at the memorial to deceased Iranian military official Qassem Soleimani. A statement from ISIS published to Telegram named terrorist operatives Omar al-Mowahid and Sayefulla al-Mujahid as the suicidal attackers behind the "dual martyrdom operation."