Gen. Qassem Soleimani was killed in a U.S. drone strike in Iraq, the Tehran-backed Lebanese organization Hezbollah urgently met with Iraqi militia leaders, seeking to unite them in the face of a huge void left by their powerful mentor's death, two sources with knowledge of the meetings said. The meetings were meant to coordinate the political efforts of Iraq's often-fractious militias, which lost not only Soleimani but also Abu Mahdi al-Muhandis, a unifying Iraqi paramilitary commander, in the Jan. 3 attack at Baghdad airport, the sources said. While offering few details, two additional sources in a pro-Iran regional alliance confirmed that Hezbollah, which is sanctioned as a terrorist group by the United States, has stepped in to help fill the void left by Soleimani in guiding the militias. All sources in this article spoke on condition of anonymity to address sensitive political activities rarely addressed in public. Officials with the governments of Iraq and Iran did not respond to requests for comment, nor did a spokesperson for the militia groups.

They are trying to get a head start, and unlike most of the 11,000 athletes who will be in Tokyo for the games, and thousands more for the Paralympics, they will be able to speak Japanese. "Just the language itself, I love it," said Abraham Majok, a runner who arrived in Japan in November with three other South Sudanese athletes and a coach. "And it's nice and since we started learning it. But, you know, we are moving well with it and we just love it." They are training northwest of Tokyo in Maebashi, Gunma Prefecture, supported mainly by donations from the public.

In images taken from a satellite floating 400 kilometers above the Earth, Europe's humanitarian crisis shows up as white pixels against the blue-green vastness of the Mediterranean. Captured by the sensors in space, small overcrowded boats with migrants leaving Africa headed north look like tiny white comets bursting through the ocean, leaving a tail where they stir waves. "It's not that with every image I look at, I think about how someone could be dying right now," said Elisabeth Wittmann as she clicked through satellite footage on her laptop showing the coast west of the Libyan port of Sabratha. "That's also to protect myself," she added. The 26-year-old computer scientist from southern Germany is one of a dozen researchers who have teamed up with a new NGO called Space-Eye to develop artificial intelligence technology that allows computers to detect migrant boats in satellite images.

Mediation Perspectives is a periodic blog entry that's provided by the CSS' Mediation Support Team and occasional guest authors. How is artificial intelligence (AI) affecting conflict and its resolution? Peace practitioners and scholars cannot afford to disregard ongoing developments related to AI-based technologies – both from an ethical and a pragmatic perspective. In this blog, I explore AI as an evolving field of information management technologies that is changing both the nature of armed conflict and the way we can respond to it. AI encompasses the use of computer programmes to analyse big amounts of data (such as online communication and transactions) in order to learn from patterns and predict human behaviour on a massive scale.

Factorization machine (FM) is an effective model for feature-based recommendation which utilizes inner product to capture second-order feature interactions. However, one of the major drawbacks of FM is that it couldn't capture complex high-order interaction signals. A common solution is to change the interaction function, such as stacking deep neural networks on the top of FM. In this work, we propose an alternative approach to model high-order interaction signals in the embedding level, namely Generalized Embedding Machine (GEM). The embedding used in GEM encodes not only the information from the feature itself but also the information from other correlated features. Under such situation, the embedding becomes high-order. Then we can incorporate GEM with FM and even its advanced variants to perform feature interactions. More specifically, in this paper we utilize graph convolution networks (GCN) to generate high-order embeddings. We integrate GEM with several FM-based models and conduct extensive experiments on two real-world datasets. The results demonstrate significant improvement of GEM over corresponding baselines.

Higher-order tensors arise frequently in applications such as neuroimaging, recommendation system, social network analysis, and psychological studies. We consider the problem of low-rank tensor estimation from possibly incomplete, ordinal-valued observations. Two related problems are studied, one on tensor denoising and another on tensor completion. We propose a multi-linear cumulative link model, develop a rank-constrained M-estimator, and obtain theoretical accuracy guarantees. Our mean squared error bound enjoys a faster convergence rate than previous results, and we show that the proposed estimator is minimax optimal under the class of low-rank models. Furthermore, the procedure developed serves as an efficient completion method which guarantees consistent recovery of an order-$K$ $(d,\ldots,d)$-dimensional low-rank tensor using only $\tilde{\mathcal{O}}(Kd)$ noisy, quantized observations. We demonstrate the outperformance of our approach over previous methods on the tasks of clustering and collaborative filtering.

Together with the UN Refugee Agency (UNHCR) 34 collaborators built several AI and machine learning based solutions to predict forced displacement, violent conflicts, and climate change in Somalia. In addition, an exploratory data analysis resulted in powerful insights regarding conflict types, areas, and reasons. The findings will help UNHCR to execute necessary support mechanism for people at need in a faster and more effective way. Millions of people in Somalia are forced to leave their current area of residence or community due to resource shortage and natural disasters like droughts and floods as well as violent conflicts. Our challenge partner, UNHCR, provides assistance and protection for those who are forcibly displaced inside of Somalia.

In its two decades in business, Yandex has been called the Russian Google, Amazon, and Spotify, mostly due to the Moscow-based tech giant's expansive reach into every nook -- including online search, music streaming, email, maps and navigation, video, and more. In 2011, Yandex launched a mobile taxi-hailing service called Yandex.Taxi, leading to the inevitable "Uber of Russia" proclamations. Then in 2017, Yandex.Taxi and Uber merged their operations in the region to launch a new joint venture targeting Eastern Europe. Yandex.Taxi now operates across the Commonwealth of Independent States (CIS), in addition to a handful of markets elsewhere in Europe, the Middle East, and Africa. The company has followed a trajectory similar to Uber's, insofar as it now also offers food delivery, and in 2018 it launched one of Europe's first public self-driving taxi services as part of a limited pilot.

Efficient and interpretable spatial analysis is crucial in many fields such as geology, sports, and climate science. Large-scale spatial data often contains complex higher-order correlations across features and locations. While tensor latent factor models can describe higher-order correlations, they are inherently computationally expensive to train. Furthermore, for spatial analysis, these models should not only be predictive but also be spatially coherent. However, latent factor models are sensitive to initialization and can yield inexplicable results. We develop a novel Multi-resolution Tensor Learning (MRTL) algorithm for efficiently learning interpretable spatial patterns. MRTL initializes the latent factors from an approximate full-rank tensor model for improved interpretability and progressively learns from a coarse resolution to the fine resolution for an enormous computation speedup. We also prove the theoretical convergence and computational complexity of MRTL. When applied to two real-world datasets, MRTL demonstrates 4 ~ 5 times speedup compared to a fixed resolution while yielding accurate and interpretable models.

Last week we took a look at computer vision; what it is, how it works, and some of the applications for computer vision in agtech. In case you missed last week's article, computer vision or machine vision typically refers to the use of machine learning or deep learning algorithms in image processing to allow a machine to "see" and identify objects around it. Different computer vision technologies may use a variety of camera types to act as the machine's "eyes" depending on the imaging requirements. In the case of fully autonomous vehicles, an accurate computer vision system is essential. In typical vehicles, hazard detection, navigation, and object avoidance all depend on a human operator.