A mysterious sound emitted from the deepest part of the ocean has finally been identified as a Bryde's whale. Now, artificial intelligence is helping researchers track the elusive whale species responsible for the call. The puzzle began in 2014 when researchers recorded a sound resembling a moan followed by metallic sweeping pings over the Pacific Ocean's Mariana Trench. "Your average person would not think that it was made by an animal – they would think it was some ship or the Navy," says Ann Allen at the National Oceanic and Atmospheric Administration (NOAA). Years later, additional recordings of the sound, which researchers call a biotwang, were eventually linked to sightings of Bryde's whales (Balaenoptera brydei) near the Mariana Islands.

More than 120 bills related to regulating artificial intelligence are currently floating around the US Congress. This flood of bills is indicative of the desperation Congress feels to keep up with the rapid pace of technological improvements. Because of the way Congress works, the majority of these bills will never make it into law. But simply taking a look at them all can give us insight into policymakers' current preoccupations: where they think the dangers are, what each party is focusing on, and more broadly, what vision the US is pursuing when it comes to AI and how it should be regulated. That's why, with help from the Brennan Center for Justice, we've created a tracker with all the AI bills circulating in various committees in Congress right now, to see if there's anything we can learn from this legislative smorgasbord. Here's what I made of Snap's new augmented-reality Spectacles Snap has announced a new version of its Spectacles: AR glasses that could finally deliver on the promises that devices like Magic Leap, or HoloLens, or even Google Glass, made many years ago.

The China fight is on. Last week, the House of Representatives stepped up to the Herculean task of passing 25 bills targeting Chinese intrusions into America's economy and technology. This first-ever "China Week" took aim at drones, bad Chinese network routers, batteries and federal biotech contracts with Chinese firms. "House indulges in Mad Hatter's Tea Party," screamed state-run China Daily on Thursday, lamenting "40 years of mutually beneficial relationships. President, Xi Jinping waves as he leaves after speaking at a press event on Oct. 23, 2022, in Beijing, China. Rep. John Moolenaar, R-Mich., chairman of the House Select Committee on China, put it clearly. "This week, we will draw a line in the sand.

A Ukrainian drone attack targeting an armoury has caused a giant fireball, leading to a partial evacuation in western Russia. The attack, reported early on Wednesday, targeted a large arsenal close to the town of Toropets, some 400km (250 miles) northwest of Moscow in the Tver region. It illustrates Ukraine's continued effort to show it can strike at targets deep inside Russia. The drone attack caused an "extremely powerful detonation" and destroyed a large warehouse of the Main Missile and Artillery Directorate of the Russian Ministry of Defence and sparked a fire 6km (3.7 miles) wide, an unnamed source from the Ukrainian security services said. "The warehouse contained missiles intended for Iskander tactical missile systems, Tochka-U tactical missile systems, guided aerial bombs and artillery ammunition," the source told news wires.

Elon Musk's brain-chip startup Neuralink said on Tuesday its experimental implant aimed at restoring vision received the U.S. Food and Drug Administration's "breakthrough device" designation. The FDA's breakthrough tag is given to certain medical devices that provide treatment or diagnosis of life-threatening conditions. It is aimed at speeding up the development and review of devices currently under development. The experimental device, known as Blindsight, "will enable even those who have lost both eyes and their optic nerve to see," Musk said in a post on X. Neuralink did not immediately respond to a request seeking details about when it expects the Blindsight device to move into human trials. The FDA also did not immediately respond to a request for comment.

Abstract: In the field of global energy and environment, crude oil is an important strategic resource, and its price fluctuation has a far-reaching impact on the global economy, financial market and the process of low-carbon development. In recent years, with the gradual promotion of green energy transformation and low-carbon development in various countries, the dynamics of crude oil market have become more complicated and changeable. The price of crude oil is not only influenced by traditional factors such as supply and demand, geopolitical conflict and production technology, but also faces the challenges of energy policy transformation, carbon emission control and new energy technology development. This diversified driving factor makes the prediction of crude oil price not only very important in economic decision-making and energy planning, but also a key issue in financial markets.In this paper, the spot price data of European Brent crude oil provided by us energy information administration are selected, and a deep learning model with three layers of LSTM units is constructed to predict the crude oil price in the next few days. The results show that the LSTM model performs well in capturing the overall price trend, although there is some deviation during the period of sharp price fluctuation. The research in this paper not only verifies the applicability of LSTM model in energy market forecasting, but also provides data support for policy makers and investors when facing the uncertainty of crude oil price.

Kidney exchange programs have significantly increased transplantation rates but raise pressing questions about fairness in organ allocation. We present a novel framework leveraging Data Envelopment Analysis (DEA) to evaluate multiple fairness criteria--Priority, Access, and Outcome--within a single model, capturing complexities that may be overlooked in single-metric analyses. Using data from the United Network for Organ Sharing, we analyze these criteria individually, measuring Priority fairness through waitlist durations, Access fairness through Kidney Donor Profile Index scores, and Outcome fairness through graft lifespan. We then apply our DEA model to demonstrate significant disparities in kidney allocation efficiency across ethnic groups. To quantify uncertainty, we employ conformal prediction within the DEA framework, yielding group-conditional prediction intervals with finite sample coverage guarantees. Our findings show notable differences in efficiency distributions between ethnic groups. Our study provides a rigorous framework for evaluating fairness in complex resource allocation systems, where resource scarcity and mutual compatibility constraints exist. All code for using the proposed method and reproducing results is available on GitHub.

Bushfire is one of the major natural disasters that cause huge losses to livelihoods and the environment. Understanding and analyzing the severity of bushfires is crucial for effective management and mitigation strategies, helping to prevent the extensive damage and loss caused by these natural disasters. This study presents an in-depth analysis of bushfire severity in Australia over the last twelve years, combining remote sensing data and machine learning techniques to predict future fire trends. By utilizing Landsat imagery and integrating spectral indices like NDVI, NBR, and Burn Index, along with topographical and climatic factors, we developed a robust predictive model using XGBoost. The model achieved high accuracy, 86.13%, demonstrating its effectiveness in predicting fire severity across diverse Australian ecosystems. By analyzing historical trends and integrating factors such as population density and vegetation cover, we identify areas at high risk of future severe bushfires. Additionally, this research identifies key regions at risk, providing data-driven recommendations for targeted firefighting efforts. The findings contribute valuable insights into fire management strategies, enhancing resilience to future fire events in Australia. Also, we propose future work on developing a UAV-based swarm coordination model to enhance fire prediction in real-time and firefighting capabilities in the most vulnerable regions.

The United Nations 2030 Agenda for Sustainable Development outlines 17 goals to address global challenges. However, progress has been slower than expected and, consequently, there is a need to investigate the reasons behind this fact. In this study, we used a novel data-driven methodology to analyze data from 107 countries (2000$-$2022) using unsupervised machine learning techniques. Our analysis reveals strong positive and negative correlations between certain SDGs. The findings show that progress toward the SDGs is heavily influenced by geographical, cultural and socioeconomic factors, with no country on track to achieve all goals by 2030. This highlights the need for a region specific, systemic approach to sustainable development that acknowledges the complex interdependencies of the goals and the diverse capacities of nations. Our approach provides a robust framework for developing efficient and data-informed strategies, to promote cooperative and targeted initiatives for sustainable progress.

Dynamic topic modeling is useful at discovering the development and change in latent topics over time. However, present methodology relies on algorithms that separate document and word representations. This prevents the creation of a meaningful embedding space where changes in word usage and documents can be directly analyzed in a temporal context. This paper proposes an expansion of the compass-aligned temporal Word2Vec methodology into dynamic topic modeling. Such a method allows for the direct comparison of word and document embeddings across time in dynamic topics. This enables the creation of visualizations that incorporate temporal word embeddings within the context of documents into topic visualizations. In experiments against the current state-of-the-art, our proposed method demonstrates overall competitive performance in topic relevancy and diversity across temporal datasets of varying size. Simultaneously, it provides insightful visualizations focused on temporal word embeddings while maintaining the insights provided by global topic evolution, advancing our understanding of how topics evolve over time.