Rear Admiral Daniel Hagari of the Israeli Defense Forces updates the public Saturday, April 13 on Iranian UAVs being launched toward the Jewish state. Iran launched dozens of unmanned aerial vehicles (UAV), also known as drones, at Israel on Saturday evening local time after a week of threatening retaliation for an attack on a consulate in Damascus. "Iran has begun an airborne attack against Israel," White House National Security Council spokesperson Adrienne Watson said in a statement Saturday. "President Biden is being regularly updated on the situation by his national security team and will meet with them this afternoon at the White House." Iran's state-run news agency Mehr News reported that the Islamic Revolutionary Guard Corps (IRGC) had announced the start of the "anti-Zionist operation," which would hit targets in the Palestinian territories, with further details of the operation announced soon.

Rep. Carlos Gimenez, R-Fla., tells'Fox News Live' that China, Russia, North Korea and Iran want to establish a'new world order.' Lawmakers reacted after Iran launched drones from its own territory toward Israel late Saturday, calling for the White House to "stand firm" and "stop coddling Iran." Speaker Mike Johnson pledged America's "full resolve" to stand with Israel. "As Israel faces this vicious attack from Iran, America must show our full resolve to stand with our critical ally," Johnson said in a statement. "The world must be assured: Israel is not alone."

The White House vowed Saturday that the United States' support for Israel's security is "ironclad," pledging to stand with the Jewish state and "support their defense" after Iran launched an aerial drone attack towards the country Saturday afternoon. Iran launched drones from its own territory toward Israel late Saturday, days after its Supreme Leader warned it would hit back in response to an airstrike on the Iranian consulate in Syria that left several generals dead. "Iran has begun an airborne attack against Israel," White House National Security Council spokesperson Adrienne Watson said in a statement Saturday. "President Biden is being regularly updated on the situation by his national security team and will meet with them this afternoon at the White House." The White House said the president's team "is in constant communication with Israeli officials as well as other partners and allies."

We show that chimpanzee behaviour understanding from camera traps can be enhanced by providing visual architectures with access to an embedding of text descriptions that detail species behaviours. In particular, we present a vision-language model which employs multi-modal decoding of visual features extracted directly from camera trap videos to process query tokens representing behaviours and output class predictions. Query tokens are initialised using a standardised ethogram of chimpanzee behaviour, rather than using random or name-based initialisations. In addition, the effect of initialising query tokens using a masked language model fine-tuned on a text corpus of known behavioural patterns is explored. We evaluate our system on the PanAf500 and PanAf20K datasets and demonstrate the performance benefits of our multi-modal decoding approach and query initialisation strategy on multi-class and multi-label recognition tasks, respectively. Results and ablations corroborate performance improvements. We achieve state-of-the-art performance over vision and vision-language models in top-1 accuracy (+6.34%) on PanAf500 and overall (+1.1%) and tail-class (+2.26%) mean average precision on PanAf20K. We share complete source code and network weights for full reproducibility of results and easy utilisation.

Timely identification is essential for the efficient handling of mental health illnesses such as depression. However, the current research fails to adequately address the prediction of mental health conditions from social media data in low-resource African languages like Swahili. This study introduces two distinct approaches utilising model-agnostic meta-learning and leveraging large language models (LLMs) to address this gap. Experiments are conducted on three datasets translated to low-resource language and applied to four mental health tasks, which include stress, depression, depression severity and suicidal ideation prediction. we first apply a meta-learning model with self-supervision, which results in improved model initialisation for rapid adaptation and cross-lingual transfer. The results show that our meta-trained model performs significantly better than standard fine-tuning methods, outperforming the baseline fine-tuning in macro F1 score with 18\% and 0.8\% over XLM-R and mBERT. In parallel, we use LLMs' in-context learning capabilities to assess their performance accuracy across the Swahili mental health prediction tasks by analysing different cross-lingual prompting approaches. Our analysis showed that Swahili prompts performed better than cross-lingual prompts but less than English prompts. Our findings show that in-context learning can be achieved through cross-lingual transfer through carefully crafted prompt templates with examples and instructions.

Despite the superb performance in many tasks, large language models (LLMs) bear the risk of generating hallucination or even wrong answers when confronted with tasks that demand the accuracy of knowledge. The issue becomes even more noticeable when addressing logic queries that require multiple logic reasoning steps. On the other hand, knowledge graph (KG) based question answering methods are capable of accurately identifying the correct answers with the help of knowledge graph, yet its accuracy could quickly deteriorate when the knowledge graph itself is sparse and incomplete. It remains a critical challenge on how to integrate knowledge graph reasoning with LLMs in a mutually beneficial way so as to mitigate both the hallucination problem of LLMs as well as the incompleteness issue of knowledge graphs. In this paper, we propose 'Logic-Query-of-Thoughts' (LGOT) which is the first of its kind to combine LLMs with knowledge graph based logic query reasoning. LGOT seamlessly combines knowledge graph reasoning and LLMs, effectively breaking down complex logic queries into easy to answer subquestions. Through the utilization of both knowledge graph reasoning and LLMs, it successfully derives answers for each subquestion. By aggregating these results and selecting the highest quality candidate answers for each step, LGOT achieves accurate results to complex questions. Our experimental findings demonstrate substantial performance enhancements, with up to 20% improvement over ChatGPT.

We present labeled morphological segmentation, an alternative view of morphological processing that unifies several tasks. From an annotation standpoint, we additionally introduce a new hierarchy of morphotactic tagsets. Finally, we develop \modelname, a discriminative morphological segmentation system that, contrary to previous work, explicitly models morphotactics. We show that \textsc{chipmunk} yields improved performance on three tasks for all six languages: (i) morphological segmentation, (ii) stemming and (iii) morphological tag classification. On morphological segmentation, our method shows absolute improvements of 2--6 points $F_1$ over the baseline.

Recently, Bonaventure Dossou learned of an alarming tendency in a popular AI model. The program described Fon--a language spoken by Dossou's mother and millions of others in Benin and neighboring countries--as "a fictional language." This result, which I replicated, is not unusual. Dossou is accustomed to the feeling that his culture is unseen by technology that so easily serves other people. He grew up with no Wikipedia pages in Fon, and no translation programs to help him communicate with his mother in French, in which he is more fluent.

South Africa's Aerobotics is utilizing artificial intelligence (AI) in helping fruit and nut farmers in over 18 countries. JOHANNESBURG - South Africa's Aerobotics is utilizing artificial intelligence (AI) in helping fruit and nut farmers improve crop yields. Although the Cape Town-based company only started nine years ago, it is already operating in 18 countries, with the U.S. being their largest market, followed by South Africa, Australia, Spain and Portugal. Its customers produce tens of millions of tons of fresh produce every year. California is now ground zero for Aerobotics – where the company has the biggest concentration of customers.

Blocking events are an important cause of extreme weather, especially long-lasting blocking events that trap weather systems in place. The duration of blocking events is, however, underestimated in climate models. Explainable Artificial Intelligence are a class of data analysis methods that can help identify physical causes of prolonged blocking events and diagnose model deficiencies. We demonstrate this approach on an idealized quasigeostrophic model developed by Marshall and Molteni (1993). We train a convolutional neural network (CNN), and subsequently, build a sparse predictive model for the persistence of Atlantic blocking, conditioned on an initial high-pressure anomaly. Shapley Additive ExPlanation (SHAP) analysis reveals that high-pressure anomalies in the American Southeast and North Atlantic, separated by a trough over Atlantic Canada, contribute significantly to prediction of sustained blocking events in the Atlantic region. This agrees with previous work that identified precursors in the same regions via wave train analysis. When we apply the same CNN to blockings in the ERA5 atmospheric reanalysis, there is insufficient data to accurately predict persistent blocks. We partially overcome this limitation by pre-training the CNN on the plentiful data of the Marshall-Molteni model, and then using Transfer Learning to achieve better predictions than direct training. SHAP analysis before and after transfer learning allows a comparison between the predictive features in the reanalysis and the quasigeostrophic model, quantifying dynamical biases in the idealized model. This work demonstrates the potential for machine learning methods to extract meaningful precursors of extreme weather events and achieve better prediction using limited observational data.