Foundation models have revolutionized artificial intelligence (AI), offering remarkable capabilities across multi-modal domains. Their ability to precisely locate objects in complex aerial and satellite images, using rich contextual information and detailed object descriptions, is essential for remote sensing (RS). These models can associate textual descriptions with object positions through the Visual Grounding (VG) task, but due to domain-specific challenges, their direct application to RS produces sub-optimal results. To address this, we applied Parameter Efficient Fine Tuning (PEFT) techniques to adapt these models for RS-specific VG tasks. Specifically, we evaluated LoRA placement across different modules in Grounding DINO and used BitFit and adapters to fine-tune the OFA foundation model pre-trained on general-purpose VG datasets. This approach achieved performance comparable to or surpassing current State Of The Art (SOTA) models while significantly reducing computational costs. This study highlights the potential of PEFT techniques to advance efficient and precise multi-modal analysis in RS, offering a practical and cost-effective alternative to full model training.

Lebanon's Ministry of Public Health has said that at least four people have been killed in two separate Israeli strikes in south Lebanon, as Israel claimed it struck Hezbollah operatives. Thursday's strikes were the latest in a series of deadly attacks in south Lebanon, despite a November ceasefire between Israel and Hezbollah after more than a year of hostilities, including two months of open war. An "Israeli enemy strike on a car in Yohmor al-Shaqeef led to the death of three people", said a Health Ministry statement reported by the National News Agency (NNA) on Thursday. The NNA said an "enemy drone" targeted a vehicle near the town, in a strike that came at the same time as artillery shelling. Elsewhere, the Israeli military said in a statement that "several Hezbollah terrorists were identified transferring weapons in the area of Yohmor in southern Lebanon", adding that the army "struck the terrorists".

Large Language Models (LLMs) are widely used for knowledge-seeking purposes yet suffer from hallucinations. The knowledge boundary of an LLM limits its factual understanding, beyond which it may begin to hallucinate. Investigating the perception of LLMs' knowledge boundary is crucial for detecting hallucinations and LLMs' reliable generation. Current studies perceive LLMs' knowledge boundary on questions with concrete answers (close-ended questions) while paying limited attention to semi-open-ended questions that correspond to many potential answers. Some researchers achieve it by judging whether the question is answerable or not. However, this paradigm is not so suitable for semi-open-ended questions, which are usually "partially answerable questions" containing both answerable answers and ambiguous (unanswerable) answers.

Cardiac ultrasound (US) scanning is a commonly used techniques in cardiology to diagnose the health of the heart and its proper functioning. Therefore, it is necessary to consider ways to automate these tasks and assist medical professionals in classifying and assessing cardiac US images. Machine learning (ML) techniques are regarded as a prominent solution due to their success in numerous applications aimed at enhancing the medical field, including addressing the shortage of echography technicians. However, the limited availability of medical data presents a significant barrier to applying ML in cardiology, particularly regarding US images of the heart. This paper addresses this challenge by introducing the first open graded dataset for Cardiac Assessment and ClassificaTion of UltraSound (CACTUS), which is available online. This dataset contains images obtained from scanning a CAE Blue Phantom and representing various heart views and different quality levels, exceeding the conventional cardiac views typically found in the literature. Additionally, the paper introduces a Deep Learning (DL) framework consisting of two main components. The first component classifies cardiac US images based on the heart view using a Convolutional Neural Network (CNN). The second component uses Transfer Learning (TL) to fine-tune the knowledge from the first component and create a model for grading and assessing cardiac images. The framework demonstrates high performance in both classification and grading, achieving up to 99.43% accuracy and as low as 0.3067 error, respectively. To showcase its robustness, the framework is further fine-tuned using new images representing additional cardiac views and compared to several other state-of-the-art architectures. The framework's outcomes and performance in handling real-time scans were also assessed using a questionnaire answered by cardiac experts.

A security camera in eastern Lebanon captured the moments a car was hit in a suspected Israeli drone attack. Lebanon's health ministry said one person was killed and another injured.

At least one person has been killed in an Israeli drone attack on a vehicle in southern Lebanon, just one day after Israeli troops withdrew from most of the border area. Lebanon's state-run National News Agency (NNA) reported that "an enemy drone struck a vehicle… in the town of Aita al-Shaab" on Wednesday. Sanad, Al Jazeera's fact-checking agency, identified the victim as Yusuf Mohammed Sorour, the son of the mayor of the town, targeted while he was sitting in his car in front of his home. NNA had earlier reported that in a separate attack, one person was wounded in the Wazzani region after Israeli forces opened fire "while residents were inspecting" restaurants and cafes in the area. The NNA also reported Israeli forces shot towards homes near the town of Shebaa.

This study uses the Palestinian Oral History Archive (POHA) to investigate how Palestinian refugee groups in Lebanon sustain a cohesive collective memory of the Nakba through shared narratives. Grounded in Halbwachs' theory of group memory, we employ statistical analysis of pairwise similarity of narratives, focusing on the influence of shared gender and location. We use textual representation and semantic embeddings of narratives to represent the interviews themselves. Our analysis demonstrates that shared origin is a powerful determinant of narrative similarity across thematic keywords, landmarks, and significant figures, as well as in semantic embeddings of the narratives. Meanwhile, shared residence fosters cohesion, with its impact significantly amplified when paired with shared origin. Additionally, women's narratives exhibit heightened thematic cohesion, particularly in recounting experiences of the British occupation, underscoring the gendered dimensions of memory formation. This research deepens the understanding of collective memory in diasporic settings, emphasizing the critical role of oral histories in safeguarding Palestinian identity and resisting erasure.

Language Models (LMs) have been shown to exhibit a strong preference towards entities associated with Western culture when operating in non-Western languages. In this paper, we aim to uncover the origins of entity-related cultural biases in LMs by analyzing several contributing factors, including the representation of entities in pre-training data and the impact of variations in linguistic phenomena across languages. We introduce CAMeL-2, a parallel Arabic-English benchmark of 58,086 entities associated with Arab and Western cultures and 367 masked natural contexts for entities. Our evaluations using CAMeL-2 reveal reduced performance gaps between cultures by LMs when tested in English compared to Arabic. We find that LMs struggle in Arabic with entities that appear at high frequencies in pre-training, where entities can hold multiple word senses. This also extends to entities that exhibit high lexical overlap with languages that are not Arabic but use the Arabic script. Further, we show how frequency-based tokenization leads to this issue in LMs, which gets worse with larger Arabic vocabularies. We will make CAMeL-2 available at: https://github.com/tareknaous/camel2

At least five people have been killed in Israeli attacks on several towns in southern Lebanon, the country's Health Ministry has said, amid a fragile ceasefire between Israel and Hezbollah. "An Israeli enemy drone strike on the town of Ainata killed one person and wounded another," the ministry said. An "Israeli strike on the town of Bint Jbeil killed three people," while a third "on Beit Lif killed one person", it added. There was no immediate comment from the Israeli military on the attacks. Israel's army escalated its attacks on Lebanon in late September after more than 11 months of cross-border exchanges of fire with the Lebanese armed group Hezbollah, which began firing rockets towards Israel after the Palestinian group Hamas's attack on southern Israel on October 7, 2023.

An Israeli drone strike has killed one person in southern Lebanon, according to Lebanese health authorities, as Prime Minister Benjamin Netanyahu promised to enforce the ceasefire with Hezbollah "with an iron fist". Lebanon's Ministry of Public Health and state media said Israeli forces carried out several new drone and artillery strikes in southern Lebanon on Tuesday, putting further strain on a tenuous 6-day-old ceasefire with Hezbollah. "An Israeli enemy drone strike on the town of Shebaa killed one person," a Health Ministry statement said. The state-run National News Agency (NNA) described the man who was killed as a "shepherd". The new attacks come as Israeli officials threatened to expand attacks on Lebanon if the ceasefire with the Lebanese armed group Hezbollah collapses.