Large vision-language models have recently achieved remarkable progress, exhibiting great perception and reasoning abilities concerning visual information. However, how to effectively evaluate these large vision-language models remains a major obstacle, hindering future model development. Traditional benchmarks like VQAv2 or COCO Caption provide quantitative performance measurements but suffer from a lack of fine-grained ability assessment and non-robust evaluation metrics. Recent subjective benchmarks, such as OwlEval, offer comprehensive evaluations of a model's abilities by incorporating human labor, but they are not scalable and display significant bias. In response to these challenges, we propose MMBench, a novel multi-modality benchmark. MMBench methodically develops a comprehensive evaluation pipeline, primarily comprised of two elements. The first element is a meticulously curated dataset that surpasses existing similar benchmarks in terms of the number and variety of evaluation questions and abilities. The second element introduces a novel CircularEval strategy and incorporates the use of ChatGPT. This implementation is designed to convert free-form predictions into pre-defined choices, thereby facilitating a more robust evaluation of the model's predictions. MMBench is a systematically-designed objective benchmark for robustly evaluating the various abilities of vision-language models. We hope MMBench will assist the research community in better evaluating their models and encourage future advancements in this domain. Project page: https://opencompass.org.cn/mmbench.

Bogged down in small-scale infantry attacks and incremental advances, Ukraine sought to gain an advantage in the 76th week of the war by attacking Russian shipping at range and was accused of drone strikes targeting Moscow. Drone footage Ukraine released on August 4 showed the prow of a surface drone approaching the Olenegorsky Gornyak, a Ropucha-class Russian landing ship, before going blank at contact range. The attack happened just outside Novorossiysk harbour, supposedly a safe port on the eastern edge of the Black Sea, to which Russia had relocated much of its navy fleet based in Sevastopol after Ukraine sank its Black Sea flagship in May. Daylight footage showed the Olenegorsky Gornyak listing severely to port as it was towed to Novorossiysk harbour. "This poses a great dilemma for the Russians," wrote Phillips O'Brien, professor of strategy at St Andrews University.

The accurate assessment of fish stocks is crucial for sustainable fisheries management. However, existing statistical stock assessment models can have low forecast performance of relevant stock parameters like recruitment or spawning stock biomass, especially in ecosystems that are changing due to global warming and other anthropogenic stressors. In this paper, we investigate the use of machine learning models to improve the estimation and forecast of such stock parameters. We propose a hybrid model that combines classical statistical stock assessment models with supervised ML, specifically gradient boosted trees. Our hybrid model leverages the initial estimate provided by the classical model and uses the ML model to make a post-hoc correction to improve accuracy. We experiment with five different stocks and find that the forecast accuracy of recruitment and spawning stock biomass improves considerably in most cases.

The footprint of Russian President Vladimir Putin's war on Ukraine is growing fast after a weekend in which sea drones crippled a Russian naval vessel and oil tanker. For the first time, the attacks put at risk Russia's commodity exports via the Black Sea, a route that accounts for most of the grain and 15% to 20% of the oil Russia sells daily on global markets. Significantly higher insurance and shipping costs are likely to follow for Moscow, but there are risks to European and global markets, too. The expansion comes as Ukraine's counteroffensive advances more slowly than Kyiv officials planned, and as Saudi Arabia's attempt to catalyze peace talks by hosting a multinational conference showed just how hard it is likely to be to end the bloodshed on terms both sides can accept.

A Russian tanker was damaged in a Ukrainian drone attack in the Kerch Strait, briefly halting traffic on the strategic bridge linking Crimea to Russia on Saturday, a day after one of Moscow's warships was hit in the Black Sea. The number of attacks in the Black Sea has increased from both sides since Moscow exited a deal last month that had allowed Ukrainian grain exports via the shipping hub during the conflict between the two countries. The Russian tanker SIG was hit around 11:20 p.m. Friday south of the Kerch Strait, Russia's Federal Agency for Sea and Inland Water Transport said.

With the rapid amassing of spatial-temporal (ST) ocean data, many spatial-temporal data mining (STDM) studies have been conducted to address various oceanic issues, including climate forecasting and disaster warning. Compared with typical ST data (e.g., traffic data), ST ocean data is more complicated but with unique characteristics, e.g., diverse regionality and high sparsity. These characteristics make it difficult to design and train STDM models on ST ocean data. To the best of our knowledge, a comprehensive survey of existing studies remains missing in the literature, which hinders not only computer scientists from identifying the research issues in ocean data mining but also ocean scientists to apply advanced STDM techniques. In this paper, we provide a comprehensive survey of existing STDM studies for ocean science. Concretely, we first review the widely-used ST ocean datasets and highlight their unique characteristics. Then, typical ST ocean data quality enhancement techniques are explored. Next, we classify existing STDM studies in ocean science into four types of tasks, i.e., prediction, event detection, pattern mining, and anomaly detection, and elaborate on the techniques for these tasks. Finally, promising research opportunities are discussed. This survey can help scientists from both computer science and ocean science better understand the fundamental concepts, key techniques, and open challenges of STDM for ocean science.

The Russian military said anti-aircraft units thwarted a Ukrainian "terrorist attack" and downed drones targeting Moscow, but that one drone struck a high-rise tower that was hit earlier in the week. Russia said it had repelled an overnight Ukrainian drone attack aimed at its patrol boats in the Black Sea. Mykhailo Podolyak, an adviser to Ukrainian President Volodymyr Zelenskyy, told the Reuters news agency that Kyiv did not attack and would not attack civilian vessels in the Black Sea, calling Russian claims "fictitious". Drones struck populated areas in the Ukrainian city of Kharkiv, destroying two floors of a college dormitory, according to local officials. Kharkiv Mayor Ihor Terekhov said there had been three separate attacks on Ukraine's second-largest city.

Spatial processes observed in various fields, such as climate and environmental science, often occur on a large scale and demonstrate spatial nonstationarity. Fitting a Gaussian process with a nonstationary Mat\'ern covariance is challenging. Previous studies in the literature have tackled this challenge by employing spatial partitioning techniques to estimate the parameters that vary spatially in the covariance function. The selection of partitions is an important consideration, but it is often subjective and lacks a data-driven approach. To address this issue, in this study, we utilize the power of Convolutional Neural Networks (ConvNets) to derive subregions from the nonstationary data. We employ a selection mechanism to identify subregions that exhibit similar behavior to stationary fields. In order to distinguish between stationary and nonstationary random fields, we conducted training on ConvNet using various simulated data. These simulations are generated from Gaussian processes with Mat\'ern covariance models under a wide range of parameter settings, ensuring adequate representation of both stationary and nonstationary spatial data. We assess the performance of the proposed method with synthetic and real datasets at a large scale. The results revealed enhanced accuracy in parameter estimations when relying on ConvNet-based partition compared to traditional user-defined approaches.

Machine learning (ML) refers to computer algorithms that predict a meaningful output or categorize complex systems based on a large amount of data. ML is applied in various areas including natural science, engineering, space exploration, and even gaming development. This review focuses on the use of machine learning in the field of chemical and biological oceanography. In the prediction of global fixed nitrogen levels, partial carbon dioxide pressure, and other chemical properties, the application of ML is a promising tool. Machine learning is also utilized in the field of biological oceanography to detect planktonic forms from various images (i.e., microscopy, FlowCAM, and video recorders), spectrometers, and other signal processing techniques. Moreover, ML successfully classified the mammals using their acoustics, detecting endangered mammalian and fish species in a specific environment. Most importantly, using environmental data, the ML proved to be an effective method for predicting hypoxic conditions and harmful algal bloom events, an essential measurement in terms of environmental monitoring. Furthermore, machine learning was used to construct a number of databases for various species that will be useful to other researchers, and the creation of new algorithms will help the marine research community better comprehend the chemistry and biology of the ocean.

Drones, missiles and cross-border artillery took centre stage during the 62nd week of Russia's war in Ukraine, as the 63rd began with a dramatic allegation from Russia – that Ukraine made an attempt on President Vladimir Putin's life. Ukraine may have targeted Russian fuel depots – a possible preamble to its expected counteroffensive. Russia, meanwhile, sharply intensified strikes against Ukrainian civilians, claiming dozens of lives. Ukraine was likely responsible for explosions in Kozacha Bay, near Sevastopol on the Crimean Peninsula, where the Russian Black Sea Fleet has a base, on April 29. Footage showed a massive black mushroom cloud rising from a fuel tank park.