We present a Bayesian nonparametric model that discovers implicit social structure from interaction time-series data. Social groups are often formed implicitly, through actions among members of groups. Yet many models of social networks use explicitly declared relationships to infer social structure. We consider a particular class of Hawkes processes, a doubly stochastic point process, that is able to model reciprocity between groups of individuals. We then extend the Infinite Relational Model by using these reciprocating Hawkes processes to parameterise its edges, making events associated with edges co-dependent through time. Our model outperforms general, unstructured Hawkes processes as well as structured Poisson process-based models at predicting verbal and email turn-taking, and military conflicts among nations.

This paper investigates the prediction of vessels' arrival time to the pilotage area using multi-data fusion and deep learning approaches. Firstly, the vessel arrival contour is extracted based on Multivariate Kernel Density Estimation (MKDE) and clustering. Secondly, multiple data sources, including Automatic Identification System (AIS), pilotage booking information, and meteorological data, are fused before latent feature extraction. Thirdly, a Temporal Convolutional Network (TCN) framework that incorporates a residual mechanism is constructed to learn the hidden arrival patterns of the vessels. Extensive tests on two real-world data sets from Singapore have been conducted and the following promising results have been obtained: 1) fusion of pilotage booking information and meteorological data improves the prediction accuracy, with pilotage booking information having a more significant impact; 2) using discrete embedding for the meteorological data performs better than using continuous embedding; 3) the TCN outperforms the state-of-the-art baseline methods in regression tasks, exhibiting Mean Absolute Error (MAE) ranging from 4.58 min to 4.86 min; and 4) approximately 89.41% to 90.61% of the absolute prediction residuals fall within a time frame of 10 min.

The Yemen-based branch of Al Qaeda said on Sunday that its leader, Khaled Batarfi, had died. Al Qaeda in the Arabian Peninsula, known as A.Q.A.P., released a video announcing Mr. Batarfi's death, showing images of him wrapped in a white funeral shroud overlaid with a black Al Qaeda flag. It did not explain how he had died. The United States government once considered Al Qaeda in the Arabian Peninsula to be one of the world's most dangerous terrorist organizations. The group tried and failed at least three times to blow up American airliners, and has been targeted by American drone strikes for two decades.

Dengue fever is one of the most deadly mosquito-born tropical infectious diseases. Detailed long range forecast model is vital in controlling the spread of disease and making mitigation efforts. In this study, we examine methods used to forecast dengue cases for long range predictions. The dataset consists of local climate/weather in addition to global climate indicators of Singapore from 2000 to 2019. We utilize newly developed deep neural networks to learn the intricate relationship between the features. The baseline models in this study are in the class of recent transformers for long sequence forecasting tasks. We found that a Fourier mixed window attention (FWin) based transformer performed the best in terms of both the mean square error and the maximum absolute error on the long range dengue forecast up to 60 weeks.

U.S. destroyer USS Carney shot down drones and a missile fired toward it in the Red Sea by Yemen's Houthi rebels, U.S. Central Command (CENTCOM) announced Wednesday. USS Carney, an Arleigh Burke-class destroyer that has been involved in the American campaign against the Iranian-backed rebels, shot down one anti-ship ballistic missile and three one-way attack unmanned aerial systems launched from Houthi-controlled areas of Yemen between 3 p.m. and 5 p.m. Sanaa time, CENTCOM said. Several hours later, CENTCOM forces destroyed three anti-ship missiles and three unmanned surface vessels (USV) in self-defense. The missiles and USVs were located in Houthi-controlled areas of Yemen. "CENTCOM forces identified the missiles, UAVs, and USVs and determined that they presented an imminent threat to merchant vessels and to the U.S. Navy ships in the region," CENTCOM said in a statement.

Existing approaches on zero-shot event detection usually train models on datasets annotated with known event types, and prompt them with unseen event definitions. These approaches yield sporadic successes, yet generally fall short of expectations. In this work, we aim to improve zero-shot event detection by training models to better follow event definitions. We hypothesize that a diverse set of event types and definitions are the key for models to learn to follow event definitions while existing event extraction datasets focus on annotating many high-quality examples for a few event types. To verify our hypothesis, we construct an automatically generated Diverse Event Definition (DivED) dataset and conduct comparative studies. Our experiments reveal that a large number of event types (200) and diverse event definitions can significantly boost event extraction performance; on the other hand, the performance does not scale with over ten examples per event type. Beyond scaling, we incorporate event ontology information and hard-negative samples during training, further boosting the performance. Based on these findings, we fine-tuned a LLaMA-2-7B model on our DivED dataset, yielding performance that surpasses SOTA large language models like GPT-3.5 across three open benchmarks on zero-shot event detection.

U.S. forces carried out more strikes against anti-ship cruise missiles and a drone in the Red Sea Thursday evening, Central Command said. CENTCOM forces conducted two self-defense strikes against six mobile anti-ship cruise missiles that were prepared to launch towards the Red Sea between 6 and 7:15 p.m. local time. Earlier in the evening, CENTCOM forces shot down a drone over the southern Red Sea in self-defense, CENTCOM said. "CENTCOM forces determined that the missiles and UAV presented an imminent threat to merchant vessels and to the U.S. Navy ships in the region," the command said. "These actions will protect freedom of navigation and make international waters safer and more secure for U.S. Navy and merchant vessels."

A series of airstrikes carried out by the United States and the United Kingdom on Saturday destroyed or damaged 17 of 18 Houthi targets in Yemen, Department of Defense (DoD) officials told Fox News on Tuesday. The targets included underground weapons storage facilities, missile storage facilities, one-way attack unmanned aerial systems, air defense systems, radars, and a helicopter, said DoD spokesperson U.S. Army Major Pete Nguyen. The coalition airstrikes targeted Yemen's Iran-backed Houthis, and came days after a British cargo ship was hit by a Houthi missile. "More broadly, since the first coalition strikes on Jan. 11, we assess that we've destroyed or degraded more than 150 missiles and launchers, including anti-ship land attack and surface-to-air missiles, plus numerous communication capabilities, unmanned aerial vehicles, unmanned surface vessels, coastal radars, air surveillance capabilities, rotary wing aircraft, underground facilities including weapon storage areas, and command and control buildings," Nguyen said. Gen. Pat Ryder said the strikes have degraded "a significant amount of capability" for the Houthis.

The US military has ramped up its use of artificial intelligence tools after the October 7 Hamas attacks on Israel, based on a new report by Bloomberg. Schuyler Moore, US Central Command's chief technology officer, told the news organization that machine learning algorithms helped the Pentagon identify targets for more than 85 air strikes in the Middle East this month. US bombers and fighter aircraft carried out those air strikes against seven facilities in Iraq and Syria on February 2, fully destroying or at least damaging rockets, missiles, drone storage facilities and militia operations centers. The Pentagon had also used AI systems to find rocket launchers in Yemen and surface combatants in the Red Sea, which it had then destroyed through multiple air strikes in the same month. The machine learning algorithms used to narrow down targets were developed under Project Maven, Google's now-defunct partnership the Pentagon.

U.S. Central Command said Sunday that Houthis launched an anti-ballistic missile toward a tanker ship that carries oil and chemicals in the Gulf of Aiden on Saturday, though it struck the water and did not cause damage to the ship or injuries to those on board. In a post on X, U.S. Central Command said the Iranian-backed Houthis were likely targeting the M/V Torm Thor, which is flagged and owned by a U.S. company. The ship was sailing in the Gulf of Aden at the time of the incident, which was reportedly at 11:45 p.m. local time. Central Command said a third UAV was also heading toward the area and crashed from what appeared to be an in-flight failure. A protestor holds a model of a Houthi missile during a protest held against the U.S.-led airstrikes and sanctions against the Houthi group in Sanaa, Yemen, Feb. 16, 2024.