Phytoplankton parasites are largely understudied microbial components with a potentially significant ecological impact on phytoplankton bloom dynamics. To better understand their impact, we need improved detection methods to integrate phytoplankton parasite interactions in monitoring aquatic ecosystems. Automated imaging devices usually produce high amount of phytoplankton image data, while the occurrence of anomalous phytoplankton data is rare. Thus, we propose an unsupervised anomaly detection system based on the similarity of the original and autoencoder-reconstructed samples. With this approach, we were able to reach an overall F1 score of 0.75 in nine phytoplankton species, which could be further improved by species-specific fine-tuning. The proposed unsupervised approach was further compared with the supervised Faster R-CNN based object detector. With this supervised approach and the model trained on plankton species and anomalies, we were able to reach the highest F1 score of 0.86. However, the unsupervised approach is expected to be more universal as it can detect also unknown anomalies and it does not require any annotated anomalous data that may not be always available in sufficient quantities. Although other studies have dealt with plankton anomaly detection in terms of non-plankton particles, or air bubble detection, our paper is according to our best knowledge the first one which focuses on automated anomaly detection considering putative phytoplankton parasites or infections.

Fox News Greg Palkot reports from Kyiv on another deadly Russian missile strike and Moscows efforts to block Ukraine food exports. Russia continued to target Ukrainian grain infrastructure in attacks overnight Wednesday, a sign the country could be struggling to achieve a victory in its full-scale invasion of Ukraine. "By targeting Ukraine's grain depots, Putin seeks to starve Ukrainians and create a food crisis, in order to compel [Ukrainian President Volodymyr] Zelenskyy to capitulate and Western nations to withdraw support from Ukraine," Rebekah Koffler, a strategic military intelligence analyst, former senior official at the Defense Intelligence Agency, and author of "Putin's Playbook," told Fox News Digital. "Putin's goal at this stage is to turn Ukraine into a dysfunctional state, that is unable to govern itself and feed its people, thus raising the cost of rebuilding it for the U.S. and European countries." Koffler's comments come after another round of attacks against Ukraine's southern Odesa region, where overnight Russian drones hit storage facilities and ports that Ukraine has been using for grain transport, according to a report from The Associated Press.

Ukraine's air force said a wave of Russian military drones had entered the mouth of the Danube River and were headed towards the country's Izmail river port near the border with Romania. Social media groups monitoring the war reported hearing air defence systems firing in the area near Ukraine's Danube ports of Izmail and Reni early on Wednesday morning. The governor of southern Odesa region, Oleh Kiper, asked residents of Izmail district to take shelter at around 1:30 a.m. Ukraine's Danube River ports accounted for around a quarter of all grain exports from Ukraine before Russia recently pulled out of a deal allowing safe passage for the export of Ukrainian grain via the country's Black Sea ports. Danube River ports have now become the main export route, with grain shipments sent on barges from Ukraine across the Danube to Romania and its Black Sea port of Constanta for onward shipment.

In social science, formal and quantitative models, such as ones describing economic growth and collective action, are used to formulate mechanistic explanations, provide predictions, and uncover questions about observed phenomena. Here, we demonstrate the use of a machine learning system to aid the discovery of symbolic models that capture nonlinear and dynamical relationships in social science datasets. By extending neuro-symbolic methods to find compact functions and differential equations in noisy and longitudinal data, we show that our system can be used to discover interpretable models from real-world data in economics and sociology. Augmenting existing workflows with symbolic regression can help uncover novel relationships and explore counterfactual models during the scientific process. We propose that this AI-assisted framework can bridge parametric and non-parametric models commonly employed in social science research by systematically exploring the space of nonlinear models and enabling fine-grained control over expressivity and interpretability.

Our test sets were too small and too haphazard to support statistically valid conclusions, but they were suggestive of a number of conclusions. We summarize these here, and discuss them at greater length in section 7. Over the kinds of problems tested, GPT-4 with either plug-in is significantly stronger than GPT-4 by itself, or, almost certainly, than any AI that existed a year ago. However it is still far from reliable; it often outputs a wrong answer or fails to output any answer. In terms of overall score, we would judge that these systems performs on the level of a middling undergraduate student. However, their capacities and weaknesses do not align with a human student; the systems solve some problems that even capable students would find challenging, whereas they fail on some problems that even middling high school students would find easy.

Russian shelling has killed seven people, including a 23-day-old infant, and wounded 20 others in Ukraine's southern region of Kherson, prompting local officials to declare a day of mourning. Kyiv reclaimed part of Kherson from Russian occupation last November, but Kremlin troops have continued shelling the regional capital and areas around it from across the Dnipro River. A couple, their 23-day-old child and another man were killed in the village of Shyroka Balka, Interior Minister Ihor Klymenko said on Sunday. The couple's 12-year-old son was critically wounded and died in hospital. "The terrorists will never willingly stop killing civilians," Klymenko wrote in a Telegram post.

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.

Russia's defence ministry said its forces destroyed a wave of 20 Ukrainian drones over the Russian-annexed Crimean Peninsula. There were no casualties and no damage as a result of the attempted attack early on Saturday morning, the defence ministry said on the Telegram messaging app. Fourteen drones were destroyed by air defence systems and six were suppressed by electronic warfare, the ministry said. It was not immediately clear what was the target of the reported attacks on the peninsula. Sergei Kryuchkov, an adviser to the Russia-installed governor of Crimea, said earlier that air defence systems were engaged in repelling air attacks in different parts of the peninsula.

A millennium of endeavors to fully recognize and foresee the evolution of dynamic environments has produced many mathematical models for forecasting, and information-gathering techniques, but also exceptionally complicated computational systems. Predefined complicated realities called hyperchaotic frameworks [1] demonstrate unpredictable sequences of behavior over time and sometimes defy standards. These events' temporal and spatial relationships can be compared to physiological kinetics [2]. Several complicated frameworks are currently developed to comprehend spontaneous incidents, their erratic conduct, and how changing the circumstances of actual events may result in an unanticipated shift in the result. Over the duration of the past couple of eons, the objective of being able to understand and anticipate unpredictable actions has been accomplished with the aid of innovations in technology [3] and fundamental principles [4].

Bucharest, Romania – Last Wednesday, a Russian drone attack on Ukraine's grain port infrastructure shook Romania, a NATO member. The force of the attack on the Izmail port, across the Danube River from the Eastern European nation, was so intense that the windows of some village homes in southeastern Romania shattered. Even though she lives far from the county of Tulcea, where the impact was felt, 28-year-old Alexandra, a paralegal from the capital Bucharest, is concerned. "We share a border with Ukraine and the conflict could expand at any moment," she told Al Jazeera. Russia has launched several attacks on Danube ports since pulling out of the wartime Black Sea grain deal.