On May 4, Sudan's paramilitary Rapid Support Forces (RSF) launched a barrage of suicide drones at Port Sudan, the army's de facto wartime capital on the Red Sea. The Sudanese Armed Forces (SAF) accused foreign actors of supporting the RSF's attacks and even threatened to sever ties with one of its biggest trading partners. The RSF surprised many with the strikes. It had used drones before, but never hit targets as far away as Port Sudan, which used to be a haven, until last week. "The strikes … led to a huge displacement from the city. Many people left Port Sudan," Aza Aera, a local relief worker, told Al Jazeera.

Former President Donald Trump is embarking this week on a high-stakes tour of the Persian Gulf region, targeting business deals and strategic partnerships with three oil-rich nations: Saudi Arabia, the United Arab Emirates and Qatar. The trip marks Trump's first major foreign visit of his new term and comes as nuclear negotiations with Iran drag on and as war continues between Israel and the Palestinian terror organization, Hamas, in the Gaza Strip. While business is the official focus, the backdrop is anything but calm. White House press secretary Karoline Leavitt described the mission as part of Trump's broader vision that "extremism is defeated [through] commerce and cultural exchanges." Under President Joe Biden, U.S. relations with Gulf states cooled, particularly after Biden vowed to make Saudi Crown Prince Mohammed bin Salman a "pariah" over the 2018 killing of journalist Jamal Khashoggi.

Large language models (LLMs) have been widely used for relevance assessment in information retrieval. However, our study demonstrates that combining two distinct small language models (SLMs) with different architectures can outperform LLMs in this task. Our approach -- QUPID -- integrates a generative SLM with an embedding-based SLM, achieving higher relevance judgment accuracy while reducing computational costs compared to state-of-the-art LLM solutions. This computational efficiency makes QUPID highly scalable for real-world search systems processing millions of queries daily. In experiments across diverse document types, our method demonstrated consistent performance improvements (Cohen's Kappa of 0.646 versus 0.387 for leading LLMs) while offering 60x faster inference times. Furthermore, when integrated into production search pipelines, QUPID improved nDCG@5 scores by 1.9%. These findings underscore how architectural diversity in model combinations can significantly enhance both search relevance and operational efficiency in information retrieval systems.

Sentiment classification, a complex task in natural language processing, becomes even more challenging when analyzing passages with multiple conflicting tones. Typically, longer passages exacerbate this issue, leading to decreased model performance. The aim of this paper is to introduce novel methodologies for isolating conflicting sentiments and aggregating them to effectively predict the overall sentiment of such passages. One of the aggregation strategies involves a Multi-Layer Perceptron (MLP) model which outperforms baseline models across various datasets, including Amazon, Twitter, and SST while costing $\sim$1/100 of what fine-tuning the baseline would take.

Wild salmon are essential to the ecological, economic, and cultural sustainability of the North Pacific Rim. Y et climate variability, habitat loss, and data limitations in remote ecosystems that lack basic infrastructure support pose significant challenges to effective fisheries management. This project explores the integration of multimodal foundation AI and expert-in-the-loop frameworks to enhance wild salmon monitoring and sustainable fisheries management in Indigenous rivers across Pacific Northwest. By leveraging video and sonar-based monitoring, we develop AI-powered tools for automated species identification, counting, and length measurement, reducing manual effort, expediting delivery of results, and improving decision-making accuracy. Expert validation and active learning frameworks ensure ecological relevance while reducing annotation burdens. To address unique technical and societal challenges, we bring together a cross-domain, interdisciplinary team of university researchers, fisheries biologists, Indigenous stewardship practitioners, government agencies, and conservation organizations. Through these collaborations, our research fosters ethical AI co-development, open data sharing, and culturally informed fisheries management.

Robustness to label noise within data is a significant challenge in federated learning (FL). From the data-centric perspective, the data quality of distributed datasets can not be guaranteed since annotations of different clients contain complicated label noise of varying degrees, which causes the performance degradation. There have been some early attempts to tackle noisy labels in FL. However, there exists a lack of benchmark studies on comprehensively evaluating their practical performance under unified settings. To this end, we propose the first benchmark study FNBench to provide an experimental investigation which considers three diverse label noise patterns covering synthetic label noise, imperfect human-annotation errors and systematic errors. Our evaluation incorporates eighteen state-of-the-art methods over five image recognition datasets and one text classification dataset. Meanwhile, we provide observations to understand why noisy labels impair FL, and additionally exploit a representation-aware regularization method to enhance the robustness of existing methods against noisy labels based on our observations. Finally, we discuss the limitations of this work and propose three-fold future directions. To facilitate related communities, our source code is open-sourced at https://github.com/Sprinter1999/FNBench.

Road accidents significantly threaten public safety and require in-depth analysis for effective prevention and mitigation strategies. This paper focuses on predicting accidents through the examination of a comprehensive traffic dataset covering 49 states in the United States. The dataset integrates information from diverse sources, including transportation departments, law enforcement, and traffic sensors. This paper specifically emphasizes predicting the number of accidents, utilizing advanced machine learning models such as regression analysis and time series analysis. The inclusion of various factors, ranging from environmental conditions to human behavior and infrastructure, ensures a holistic understanding of the dynamics influencing road safety. Temporal and spatial analysis further allows for the identification of trends, seasonal variations, and high-risk areas. The implications of this research extend to proactive decision-making for policymakers and transportation authorities. By providing accurate predictions and quantifiable insights into expected accident rates under different conditions, the paper aims to empower authorities to allocate resources efficiently and implement targeted interventions. The goal is to contribute to the development of informed policies and interventions that enhance road safety, creating a safer environment for all road users. Keywords: Machine Learning, Random Forest, Accident Prediction, AutoML, LSTM.

The Trump administration is considering a deal that could send hundreds of thousands of U.S.-designed artificial intelligence chips to G42, an Emirati A.I. firm that the U.S. government has scrutinized in the past for its ties to China, three people familiar with the discussions said. The negotiations, which are ongoing, highlight a major shift in U.S. tech policy ahead of President Trump's visit to the Persian Gulf states this week. The talks have also created tension inside the Trump administration between tech- and business-minded leaders who want to close a deal before Mr. Trump's trip and national security officials who worry that the technology could be misused by the Emiratis. The Trump administration has embraced cutting direct deals for A.I. chips with officials from the Middle East, as it looks to strengthen U.S. ties in the region, said the people, who spoke on the condition of anonymity because the negotiations are ongoing. The approach marks a break from the Biden administration, which had rejected similar A.I. chip sales over fears that they could give autocratic governments with strong ties to China an edge over the United States in developing the most cutting-edge A.I. models in coming years.

United States President Donald Trump will undertake a three-day tour of the Gulf for his first state visit since retaking office in January. The trip begins in Saudi Arabia, followed by Qatar and the United Arab Emirates. It marks Trump's second foreign visit as president after he attended Pope Francis's funeral in Rome in April. Trump will fly out of the US on Monday and start his trip in the Saudi capital, Riyadh, on Tuesday. He is expected to attend a Gulf summit in the city on Wednesday, visit Qatar later that day and conclude his visit in the UAE on Thursday.

As President Trump tours the Middle East this week, governments that are flush with oil wealth will be focused on a different treasure, found in America's Silicon Valley. Artificial intelligence chips, which are made by U.S. companies like Nvidia and AMD, are highly coveted by governments across the Middle East. Leaders of Saudi Arabia, Qatar and the United Arab Emirates want to pour billions of dollars into the construction of data centers to put their countries at the forefront of a new technology heralded for its power to disrupt businesses and create trillions of dollars in economic value. The Gulf States have plenty of energy and cash to build data centers, which house the supercomputers that run A.I. systems. But they need U.S. government approval to buy the American-designed chips to power them.