The Lebanese military says two soldiers have been killed and two wounded as they investigated an Israeli drone crash in southern Lebanon. The army said the downed Israeli drone exploded on Thursday during an inspection at the crash site in the Naqoura area, not far from Lebanon's border with Israel. Lebanese President Joseph Aoun offered condolences to the soldiers who were killed and injured, stressing that the military "is paying, in blood, the price of preserving stability in the south" of the country. The deadly incident came as Israel has been carrying out near-daily attacks on Lebanon despite a ceasefire reached with Hezbollah in November. It also coincides with a United Nations Security Council vote to wind down a UN peacekeeping mission in southern Lebanon, which has for decades been tasked with maintaining a buffer between Hezbollah fighters and Israeli forces.

A senior United States envoy has praised the Lebanese government's response to a US proposal aimed at disarming Hezbollah amid Israel's continued military presence in the country. Thomas Barrack, an adviser to US President Donald Trump who serves as Washington's ambassador to Turkiye and special envoy for Syria, returned to Beirut on Monday after delivering the US proposal during a June 19 visit. The plan called for the Shia Lebanese group Hezbollah to fully disarm within four months in exchange for a halt to Israeli air strikes and the full withdrawal of Israel's military from the five positions it continues to occupy in southern Lebanon. "What the government gave us was something spectacular in a very short period of time," Barrack told reporters on Monday after meeting Lebanese President Joseph Aoun. "I'm unbelievably satisfied with the response." While Barrack confirmed that he had received a seven-page reply from the Lebanese side, he offered no details on its contents.

The vast collection of Holocaust survivor testimonies presents invaluable historical insights but poses challenges for manual analysis. This paper leverages advanced Natural Language Processing (NLP) techniques to explore the USC Shoah Foundation Holocaust testimony corpus. By treating testimonies as structured question-and-answer sections, we apply topic modeling to identify key themes. We experiment with BERTopic, which leverages recent advances in language modeling technology. We align testimony sections into fixed parts, revealing the evolution of topics across the corpus of testimonies. This highlights both a common narrative schema and divergences between subgroups based on age and gender. We introduce a novel method to identify testimonies within groups that exhibit atypical topic distributions resembling those of other groups. This study offers unique insights into the complex narratives of Holocaust survivors, demonstrating the power of NLP to illuminate historical discourse and identify potential deviations in survivor experiences.

Although the synthesis of programs encoding policies often carries the promise of interpretability, systematic evaluations were never performed to assess the interpretability of these policies, likely because of the complexity of such an evaluation. In this paper, we introduce a novel metric that uses large-language models (LLM) to assess the interpretability of programmatic policies. For our metric, an LLM is given both a program and a description of its associated programming language. The LLM then formulates a natural language explanation of the program. This explanation is subsequently fed into a second LLM, which tries to reconstruct the program from the natural-language explanation. Our metric then measures the behavioral similarity between the reconstructed program and the original. We validate our approach with synthesized and human-crafted programmatic policies for playing a real-time strategy game, comparing the interpretability scores of these programmatic policies to obfuscated versions of the same programs. Our LLM-based interpretability score consistently ranks less interpretable programs lower and more interpretable ones higher. These findings suggest that our metric could serve as a reliable and inexpensive tool for evaluating the interpretability of programmatic policies.

Vasyl Bilous's last name means "white mustache." His actual mustache is dark brown with a hint of gray. He's worn one since high school. In a picture that he took on the first day of Russia's full-scale invasion of Ukraine, Vasyl has a chevron mustache, a neat barbershop cut--close on the sides, paintbrush-thick on top. At the time, he was an assistant professor of forensics at the National Law University, in Kharkiv, and a lawyer in private practice.

With the amazing performance of AlphaGo beating the best Go player in the world, Lee Sedol (and Lee also striking back), Google DeepMind's next game to tackle has been the talk of the town. This doesn't surprise me at all, as StarCraft is the most strategically deep competitive video game in the world. It is really the natural next step after Chess and Go. While Chess, and especially Go, are known as games with near infinite possibilities on the ways that they can play out, StarCraft should be even harder to create an AI for. With three distinct races and countless professionally played maps, it already seems extremely tough.

Real-time Strategy (RTS) games are complex domains which are a significant challenge to both human and artificial intelligence (AI). For that reason, and although many AI approaches have been proposed for the RTS game AI problem, the AI of all commercial RTS games is scripted and offers a very static behavior subject to exploits. In this paper, we will focus on a case-based reasoning (CBR) approach to this problem, and concentrate on the process of case-acquisition. Specifically, we will describe 7 different techniques to automatically acquire plans by observing human demonstrations and compare their performance when using them in the Darmok 2 system in the context of an RTS game.