Israel has killed two people, including a State Security officer, in separate attacks in Lebanon as it continues its assaults on the country since the ceasefire with Hezbollah came into effect last week. For its part, the Lebanese group said on Monday that it carried out a "preliminary defensive response" to the "repeated violations" of the ceasefire by attacking an Israeli military base in the hills of Kfar Chouba, a disputed area that Lebanon claims as its own. Hezbollah said Israeli breaches of the truce that went into effect on Wednesday include deadly air raids across Lebanon, shooting at civilians in the south, and flying drones and jets in Lebanese airspace, including over the capital, Beirut. The group said it launched its "warning" attack because "appeals by the relevant authorities to stop these violations did not succeed". The renewed violence highlights the fragility of the ceasefire, which ended a devastating war that killed nearly 4,000 people in Lebanon and saw Hezbollah fire rockets daily at Israel.

In the field of remote sensing, the challenge of comparing images captured by disparate sensors is a common obstacle. This requires image translation -- converting imagery from one sensor domain to another while preserving the original content. Denoising Diffusion Implicit Models (DDIM) are potential state-of-the-art solutions for such domain translation due to their proven superiority in multiple image-to-image translation tasks in classic computer vision. However, these models struggle with large-scale multi-patch imagery, often focusing solely on small patches and resulting in inconsistencies across the full image. To overcome these limitations, we propose a novel method that leverages DDIM for effective optical image translation over large areas. Our approach is tailored to super-resolve large-scale low spatial resolution images into high-resolution equivalents from disparate optical sensors, ensuring uniformity across hundreds of patches. Extensive experiments with a dataset of paired Sentinel-II and Planet Dove images show that our approach provides precise domain adaptation and artifact reduction. Our technique preserves the image content while also improving radiometric (color) accuracy and feature representations. The outcome is a high-resolution large-scale image with consistent patches, vital for applications such as heterogeneous change detection (HCD). We present a unique training and testing algorithm rooted in DDIMs, a thorough image quality assessment, and a comparative study against the standard classifier-free guided DDIM framework and five other leading methods. The efficacy of our approach is further demonstrated by substantial enhancements in HCD tasks performed in the urban settings of Beirut, Lebanon, and Austin, USA.

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.

Most seismic risk assessment methods focus on estimating the damages to the built environment and the consequent socioeconomic losses without fully taking into account the social aspect of risk. Yet, human behaviour is a key element in predicting the human impact of an earthquake, therefore, it is important to include it in quantitative risk assessment studies. In this study, an interdisciplinary approach simulating pedestrians' evacuation during earthquakes at the city scale is developed using an agent-based model. The model integrates the seismic hazard, the physical vulnerability as well as individuals' behaviours and mobility. The simulator is applied to the case of Beirut, Lebanon. Lebanon is at the heart of the Levant fault system that has generated several Mw>7 earthquakes, the latest being in 1759. It is one of the countries with the highest seismic risk in the Mediterranean region. This is due to the high seismic vulnerability of the buildings due to the absence of mandatory seismic regulation until 2012, the high level of urbanization, and the lack of adequate spatial planning and risk prevention policies. Beirut as the main residential, economic and institutional hub of Lebanon is densely populated. To accommodate the growing need for urban development, constructions have almost taken over all of the green areas of the city; squares and gardens are disappearing to give place to skyscrapers. However, open spaces are safe places to shelter, away from debris, and therefore play an essential role in earthquake evacuation. Despite the massive urbanization, there are a few open spaces but locked gates and other types of anthropogenic barriers often limit their access. To simulate this complex context, pedestrians' evacuation simulations are run in a highly realistic spatial environment implemented in GAMA [1]. Previous data concerning soil and buildings in Beirut [2, 3] are complemented by new geographic data extracted from high-resolution Pleiades satellite images. The seismic loading is defined as a peak ground acceleration of 0.3g, as stated in Lebanese seismic regulations. Building damages are estimated using an artificial neural network trained to predict the mean damage [4] based on the seismic loading as well as the soil and building vibrational properties [5]. Moreover, the quantity and the footprint of the generated debris around each building are also estimated and included in the model. We simulate how topography, buildings, debris, and access to open spaces, affect individuals' mobility. Two city configurations are implemented: 1. Open spaces are accessible without any barriers; 2. Access to some open spaces is blocked. The first simulation results show that while 52% of the population is able to arrive to an open space within 5 minutes after an earthquake, this number is reduced to 39% when one of the open spaces is locked. These results show that the presence of accessible open spaces in a city and their proximity to the residential buildings is a crucial factor for ensuring people's safety when an earthquake occurs.

The talk that followed, entitled How to Land a Remote AI Job and presented by Mr. Marc Banoub, Founder and CEO of LyRise, addressed the previously raised concerns regarding the skill gap between graduates and industry. The beginning of the discussion revealed statistical data on different aspects of working from home, such as its impact on employee productivity and long-term connections. It also highlighted the growing demand for AI-related careers in such an environment. Mr. Banoub then proceeded to engage attendees with thought-provoking questions that aim to help them self-diagnose where they are in their career, where they want to get and how they can get there. His advice covered subjects like internships, mentoring and personal projects that showcase added value.

Some of the worst sectarian clashes since Lebanon's 15-year civil war (1975-1990) broke out in Beirut this week between supporters of Hezbollah and Amal, both Shiite political parties, and Christian, far-right Lebanese Forces. Shiite protesters were rallying against the state probe into the Beirut port blast, which occurred last year. They say authorities were singling out Shiite politicians for questioning and blame. In this video, watch Ian Bremmer's conversation with Lebanese journalist and author Kim Ghattas on GZW talking about the future of Lebanese politics and sectarianism in the county after the after the blast. It was originally published on August 19, 2020.

Another August, means another Beirut AI Bootcamp, and more exciting gatherings! Despite all challenges, and all circumstances, here we are again, celebrating the end of a three week successful Bootcamp with more than 80 participants from all over Lebanon! This August we hosted our Beirut AI Bootcamp but with some changes! We had the chance to meet more than 80 motivated and passionate participants who came up with brilliant ideas in their final projects. Many came from different backgrounds but with a similar goal: becoming AI heroes!

Originally, Maysam Moussalem dreamed of being an architect. "When I was 10, I looked up to see the Art Nouveau dome over the Galeries Lafayette in Paris, and I knew I wanted to make things like that," she says. "Growing up between Austin, Paris, Beirut and Istanbul just fed my love of architecture." But she found herself often talking to her father, a computer science (CS) professor, about what she wanted in a career. "I always loved art and science and I wanted to explore the intersections between fields. CS felt broader to me, and so I ended up there."

Russian mercenary groups have enabled renegade military commander Khalifa Hafter, who is based in eastern Libya, to blockade the country's oil exports, starving the country of much-needed money. Moscow's backing of Haftar, a former CIA asset, has increased tensions with the United States. Russian private military contractors are active in 16 African nations. How is the country paying for its overseas wars? Also on Counting the Cost: Currency crisis, debt default, hyperinflation and poverty - Lebanon was in economic and political paralysis long before the devastating explosion in Beirut.

We will cover various fundamental topics and areas in AI including: Deep Learning, Computer Vision, Natural Language Processing, Time Series, and more. Every day covers a different AI topic starting with theory and building on the theory with interactive coding projects. You will leave every day of the bootcamp with a new AI project in a unique area. At the end of the bootcamp, you will put all that you have learned into a final project of your choice and present the outcome of your work to a panel of judges on the last day! You can check the schedule on the website for more details on the covered topics.