While data-driven approaches demonstrate great potential in atmospheric modeling and weather forecasting, ocean modeling poses distinct challenges due to complex bathymetry, land, vertical structure, and flow non-linearity. This study introduces OceanNet, a principled neural operator-based digital twin for ocean circulation. OceanNet uses a Fourier neural operator and predictor-evaluate-corrector integration scheme to mitigate autoregressive error growth and enhance stability over extended time scales. A spectral regularizer counteracts spectral bias at smaller scales. OceanNet is applied to the northwest Atlantic Ocean western boundary current (the Gulf Stream), focusing on the task of seasonal prediction for Loop Current eddies and the Gulf Stream meander. Trained using historical sea surface height (SSH) data, OceanNet demonstrates competitive forecast skill by outperforming SSH predictions by an uncoupled, state-of-the-art dynamical ocean model forecast, reducing computation by 500,000 times. These accomplishments demonstrate the potential of physics-inspired deep neural operators as cost-effective alternatives to high-resolution numerical ocean models.

Studying how people interact with large language models (LLMs) in real-world scenarios is increasingly important due to their widespread use in various applications. In this paper, we introduce LMSYS-Chat-1M, a large-scale dataset containing one million real-world conversations with 25 state-of-the-art LLMs. This dataset is collected from 210K unique IP addresses in the wild on our Vicuna demo and Chatbot Arena website. We offer an overview of the dataset's content, including its curation process, basic statistics, and topic distribution, highlighting its diversity, originality, and scale. We demonstrate its versatility through four use cases: developing content moderation models that perform similarly to GPT-4, building a safety benchmark, training instruction-following models that perform similarly to Vicuna, and creating challenging benchmark questions. We believe that this dataset will serve as a valuable resource for understanding and advancing LLM capabilities. The dataset is publicly available at https://huggingface.co/datasets/lmsys/lmsys-chat-1m.

Deep neural networks often exploit non-predictive features that are spuriously correlated with class labels, leading to poor performance on groups of examples without such features. Despite the growing body of recent works on remedying spurious correlations, the lack of a standardized benchmark hinders reproducible evaluation and comparison of the proposed solutions. To address this, we present SpuCo, a python package with modular implementations of state-of-the-art solutions enabling easy and reproducible evaluation of current methods. Using SpuCo, we demonstrate the limitations of existing datasets and evaluation schemes in validating the learning of predictive features over spurious ones. To overcome these limitations, we propose two new vision datasets: (1) SpuCoMNIST, a synthetic dataset that enables simulating the effect of real world data properties e.g. difficulty of learning spurious feature, as well as noise in the labels and features; (2) SpuCoAnimals, a large-scale dataset curated from ImageNet that captures spurious correlations in the wild much more closely than existing datasets. These contributions highlight the shortcomings of current methods and provide a direction for future research in tackling spurious correlations. SpuCo, containing the benchmark and datasets, can be found at https://github.com/BigML-CS-UCLA/SpuCo, with detailed documentation available at https://spuco.readthedocs.io/en/latest/.

Former U.S. Defense intel officer Rebekah Koffler discusses additional aid pledged to Ukraine and the U.S.'s decision to launch an unarmed ICBM in California. Last Friday, responding to questions about recent strikes on Crimea, Vice Prime Minister and Minister of Digital Transformation of Ukraine Mykhailo Fedorov, acknowledged, albeit indirectly, that Ukraine was behind them. He also warned that there will be more drone attacks on Russian warships. Drone warfare is a critical component to Ukrainian President Volodymyr Zelenskyy's new asymmetric strategy, likely intended to ensure that Ukrainian armed forces are able to stay in the fight, over the long run, even if they are unable to secure a clear military victory over their highly entrenched opponent. Zelenskyy probably calculates that by systematically employing small scale drone attacks, Ukraine may be able to frustrate, demoralize and exhaust the Russian forces and psychologically dislodge Russian civilians.

Russia released a video reportedly showing Viktor Sokolov, commander of Russia's Black Sea Fleet in Crimea, at a meeting with Defence Minister Sergei Shoigu and other military top brass a day after Ukrainian special forces claimed he was among dozens of officers killed in an attack on the fleet's Sevastopol naval base. Ukraine said it was clarifying information regarding Sokolov. The United Kingdom's defence ministry said "a dynamic, deep strike battle" was under way in the Black Sea after the Russian Black Sea Fleet suffered a series of major attacks. Kyiv said its air defences destroyed 26 of 38 Russian drones fired overnight but that some of the drones hit the Danube River port of Izmail, damaging more than 30 vehicles and injuring two drivers during a two-hour attack. The drone barrage also prompted the temporary suspension of ferry services to Romania.

In this study, we investigated the relationship between sailboat technical specifications and their prices, as well as regional pricing influences. Utilizing a dataset encompassing characteristics like length, beam, draft, displacement, sail area, and waterline, we applied multiple machine learning models to predict sailboat prices. The gradient descent model demonstrated superior performance, producing the lowest MSE and MAE. Our analysis revealed that monohulled boats are generally more affordable than catamarans, and that certain specifications such as length, beam, displacement, and sail area directly correlate with higher prices. Interestingly, lower draft was associated with higher listing prices. We also explored regional price determinants and found that the United States tops the list in average sailboat prices, followed by Europe, Hong Kong, and the Caribbean. Contrary to our initial hypothesis, a country's GDP showed no direct correlation with sailboat prices. Utilizing a 50% cross-validation method, our models yielded consistent results across test groups. Our research offers a machine learning-enhanced perspective on sailboat pricing, aiding prospective buyers in making informed decisions.

Air transport poses significant environmental challenges, particularly regarding the role of flight contrails in climate change due to their potential global warming impact. Traditional computer vision techniques struggle under varying remote sensing image conditions, and conventional machine learning approaches using convolutional neural networks are limited by the scarcity of hand-labeled contrail datasets. To address these issues, we employ few-shot transfer learning to introduce an innovative approach for accurate contrail segmentation with minimal labeled data. Our methodology leverages backbone segmentation models pre-trained on extensive image datasets and fine-tuned using an augmented contrail-specific dataset. We also introduce a novel loss function, termed SR Loss, which enhances contrail line detection by transforming the image space into Hough space. This transformation results in a significant performance improvement over generic image segmentation loss functions. Our approach offers a robust solution to the challenges posed by limited labeled data and significantly advances the state of contrail detection models.

Anthropogenic pollution of hydrological systems affects diverse communities and ecosystems around the world. Data analytics and modeling tools play a key role in fighting this challenge, as they can help identify key sources as well as trace transport and quantify impact within complex hydrological systems. Several tools exist for simulating and tracing pollutant transport throughout surface waters using detailed physical models; these tools are powerful, but can be computationally intensive, require significant amounts of data to be developed, and require expert knowledge for their use (ultimately limiting application scope). In this work, we present a graph modeling framework -- which we call ${\tt HydroGraphs}$ -- for understanding pollutant transport and fate across waterbodies, rivers, and watersheds. This framework uses a simplified representation of hydrological systems that can be constructed based purely on open-source data (National Hydrography Dataset and Watershed Boundary Dataset). The graph representation provides an flexible intuitive approach for capturing connectivity and for identifying upstream pollutant sources and for tracing downstream impacts within small and large hydrological systems. Moreover, the graph representation can facilitate the use of advanced algorithms and tools of graph theory, topology, optimization, and machine learning to aid data analytics and decision-making. We demonstrate the capabilities of our framework by using case studies in the State of Wisconsin; here, we aim to identify upstream nutrient pollutant sources that arise from agricultural practices and trace downstream impacts to waterbodies, rivers, and streams. Our tool ultimately seeks to help stakeholders design effective pollution prevention/mitigation practices and evaluate how surface waters respond to such practices.

Russian aerial defence systems destroyed a wave of 19 Ukrainian drones that were launched overnight in attacks against targets in the Russia-annexed Crimean peninsula, the surrounding Black Sea and other regions of Russia. The Russian defence ministry said early on Thursday that it had "thwarted" the attacks by Ukraine's aircraft-type unmanned aerial vehicles (UAVs). "In the night from 20th to 21st September, an attempt by the Kyiv regime to commit a terrorist attack with lethal drones on sites in the Russian Federation was intercepted," the defence ministry said on the Telegram messaging app. "Air defence systems destroyed 19 Ukrainian UAVs over the Black Sea and the territory of the Republic of Crimea, and one each over the territories of Kursk, Belgorod and Oryol regions," the ministry said. The Belgorod and Kursk regions of Russia border eastern Ukraine, while Oryol is closer to the capital, Moscow.

Ukrainian President Volodymyr Zelenskyy travelled to New York to address the United Nations General Assembly in person for the first time since Moscow began its full-scale invasion of his country in February 2022. Dressed in his trademark khaki green shirt, he urged member states to come together to oppose Russian aggression and stressed the need for a peace recognising Ukraine's territorial integrity. Here is the full text of Zelenskyy's speech from September 19. I welcome all who stand for common efforts! And I promise – being really united we can guarantee fair peace for all nations.