Modeling is crucial to understanding the effect of greenhouse gases, warming, and ice sheet melting on the ocean. At the same time, ocean processes affect phenomena such as hurricanes and droughts. Parameters in the models that cannot be physically measured have a significant effect on the model output. For an idealized ocean model, we generated perturbed parameter ensemble data and trained surrogate neural network models. The neural surrogates accurately predicted the one-step forward dynamics, of which we then computed the parametric sensitivity.

Fox News Flash top entertainment and celebrity headlines are here. The vastness of the universe has always captivated the human imagination, and filmmakers have often looked to the stars for inspiration. Space-related movies have become a genre of their own, offering audiences an opportunity to explore the unknown, experience the thrill of interstellar travel and ponder the profound questions of our existence. These are some of the most iconic and thought-provoking space-theme films that have left a lasting impact on both the science fiction and Hollywood. 'GRAVITY' REVIEW: THERE HAS NEVER BEFORE BEEN MOVIE LIKE THIS From "2001: A Space Odyssey" to "Interstellar" and space survival tales like "Gravity" and "The Martian," Fox News Digital dives into the cinematic cosmos, celebrating their enduring impact on our love for science fiction.

The emergence of large language models (LLMs) has marked a significant breakthrough in natural language processing (NLP), leading to remarkable advancements in text understanding and generation. Nevertheless, alongside these strides, LLMs exhibit a critical tendency to produce hallucinations, resulting in content that is inconsistent with real-world facts or user inputs. This phenomenon poses substantial challenges to their practical deployment and raises concerns over the reliability of LLMs in real-world scenarios, which attracts increasing attention to detect and mitigate these hallucinations. In this survey, we aim to provide a thorough and in-depth overview of recent advances in the field of LLM hallucinations. We begin with an innovative taxonomy of LLM hallucinations, then delve into the factors contributing to hallucinations. Subsequently, we present a comprehensive overview of hallucination detection methods and benchmarks. Additionally, representative approaches designed to mitigate hallucinations are introduced accordingly. Finally, we analyze the challenges that highlight the current limitations and formulate open questions, aiming to delineate pathways for future research on hallucinations in LLMs.

Large Multimodal Models (LMMs) have demonstrated impressive performance across various vision and language tasks, yet their potential applications in recommendation tasks with visual assistance remain unexplored. To bridge this gap, we present a preliminary case study investigating the recommendation capabilities of GPT-4V(ison), a recently released LMM by OpenAI. We construct a series of qualitative test samples spanning multiple domains and employ these samples to assess the quality of GPT-4V's responses within recommendation scenarios. Evaluation results on these test samples prove that GPT-4V has remarkable zero-shot recommendation abilities across diverse domains, thanks to its robust visual-text comprehension capabilities and extensive general knowledge. However, we have also identified some limitations in using GPT-4V for recommendations, including a tendency to provide similar responses when given similar inputs. This report concludes with an in-depth discussion of the challenges and research opportunities associated with utilizing GPT-4V in recommendation scenarios. Our objective is to explore the potential of extending LMMs from vision and language tasks to recommendation tasks. We hope to inspire further research into next-generation multimodal generative recommendation models, which can enhance user experiences by offering greater diversity and interactivity.

Forecasts of regional electricity net-demand, consumption minus embedded generation, are an essential input for reliable and economic power system operation, and energy trading. While such forecasts are typically performed region by region, operations such as managing power flows require spatially coherent joint forecasts, which account for cross-regional dependencies. Here, we forecast the joint distribution of net-demand across the 14 regions constituting Great Britain's electricity network. Joint modelling is complicated by the fact that the net-demand variability within each region, and the dependencies between regions, vary with temporal, socio-economical and weather-related factors. We accommodate for these characteristics by proposing a multivariate Gaussian model based on a modified Cholesky parametrisation, which allows us to model each unconstrained parameter via an additive model. Given that the number of model parameters and covariates is large, we adopt a semi-automated approach to model selection, based on gradient boosting. In addition to comparing the forecasting performance of several versions of the proposed model with that of two non-Gaussian copula-based models, we visually explore the model output to interpret how the covariates affect net-demand variability and dependencies. The code for reproducing the results in this paper is available at https://doi.org/10.5281/zenodo.7315105, while methods for building and fitting multivariate Gaussian additive models are provided by the SCM R package, available at https://github.com/VinGioia90/SCM.

Russian forces have bombarded Ukraine's port city of Odesa with missiles and drones. Four missiles and 22 attack drones were launched from the occupied region of Crimea of Ukraine at the Black Sea port late on Sunday, Ukraine's air force reported on Monday. The attacks injured at least eight people, destroyed grain, and damaged the 124-year-old Odesa Fine Arts Museum. "Fifteen Shaheds and one Kh-59 air guided missile were shot down," the Ukrainian air force said, referring to the Iranian-designed kamikaze unmanned aerial vehicle. Ukrainian Presidential Chief of Staff Andriy Yermak posted images on social media of the aftermath of the strike, vowing retribution for the attack.

Two people were killed and the power supply was disrupted in Russian shelling of Ukraine's southern Kherson region. There were more than 40 hits in the village," regional governor Oleksandr Prokudin said on the Telegram messaging app. President Volodymyr Zelenskyy said Ukrainian forces repelled a new Russian assault near the town of Vuhledar between the eastern and southern front lines in eastern Donetsk. Zelenskyy said the Russians had suffered "heavy losses" with many soldiers killed and wounded. Oleksandr Shtupun, a spokesman for Ukraine's military command, said Russian forces were trying to regroup and recover their losses near the eastern city of Avdiivka before trying to press ahead with its attempt to encircle the ruined town. Russia accused Ukraine of risking nuclear disaster after it shot down nine Ukrainian drones near the Zaporizhzhia nuclear power station, which has been occupied by Russia since early March 2022. The drones were shot down near the Russian-held city of Enerhodar, where many of the plant's workers live. Russia and Ukraine have each accused the other of attacks near the plant. Russia said its air defences also brought down five Ukrainian drones over Crimea and one over the Black Sea. Russia jailed two more Ukrainian soldiers who fought in the city of Mariupol to lengthy prison sentences, as it continued to put dozens of prisoners of war on trial. Russia took thousands of Ukrainian soldiers captive after it seized Mariupol last May. Some were sent to Russia while others have been tried by Moscow-backed courts in occupied parts of eastern Ukraine. Under international law, soldiers cannot be prosecuted for having fought for their country. Two people were killed and the power supply was disrupted in Russian shelling of Ukraine's southern Kherson region. There were more than 40 hits in the village," regional governor Oleksandr Prokudin said on the Telegram messaging app.

State of the art controllers for back exoskeletons largely rely on body kinematics. This results in control strategies which cannot provide adaptive support under unknown external loads. We developed a neuromechanical model-based controller (NMBC) for a soft back exosuit, wherein assistive forces were proportional to the active component of lumbosacral joint moments, derived from real-time electromyography-driven models. The exosuit provided adaptive assistance forces with no a priori information on the external loading conditions. Across 10 participants, who stoop-lifted 5 and 15 kg boxes, our NMBC was compared to a non-adaptive virtual spring-based control(VSBC), in which exosuit forces were proportional to trunk inclination. Peak cable assistive forces were modulated across weight conditions for NMBC (5kg: 2.13 N/kg; 15kg: 2.82 N/kg) but not for VSBC (5kg: 1.92 N/kg; 15kg: 2.00 N/kg). The proposed NMBC strategy resulted in larger reduction of cumulative compression forces for 5 kg (NMBC: 18.2%; VSBC: 10.7%) and 15 kg conditions (NMBC: 21.3%; VSBC: 10.2%). Our proposed methodology may facilitate the adoption of non-hindering wearable robotics in real-life scenarios.

Ukraine's Interior Minister Ihor Klymenko said 118 settlements in 10 regions of Ukraine's east had come under Russian fire in the previous 24 hours, marking the heaviest day of Russian shelling this year. Ukraine said the Kremenchuk oil refinery in central Ukraine caught fire after a Russian drone attack that knocked out the power supply in three villages while falling debris from downed drones damaged railway power lines in a nearby region. Officials said the fire was quickly extinguished. Ukraine's air force said air defences shot down 18 of 20 Russian drones and a missile before they reached their targets. Writing in The Economist newspaper, Ukraine's commander-in-chief General Valery Zaluzhny said the army needed new military capabilities and technological innovation – and air power, in particular – to break out of the current attritional fighting along the front line.

Large multimodal models demonstrate remarkable generalist ability to perform diverse multimodal tasks in a zero-shot manner. Large-scale web-based image-text pairs contribute fundamentally to this success, but suffer from excessive noise. Recent studies use alternative captions synthesized by captioning models and have achieved notable benchmark performance. However, our experiments reveal significant Scalability Deficiency and World Knowledge Loss issues in models trained with synthetic captions, which have been largely obscured by their initial benchmark success. Upon closer examination, we identify the root cause as the overly-simplified language structure and lack of knowledge details in existing synthetic captions. To provide higher-quality and more scalable multimodal pretraining data, we propose CapsFusion, an advanced framework that leverages large language models to consolidate and refine information from both web-based image-text pairs and synthetic captions. Extensive experiments show that CapsFusion captions exhibit remarkable all-round superiority over existing captions in terms of model performance (e.g., 18.8 and 18.3 improvements in CIDEr score on COCO and NoCaps), sample efficiency (requiring 11-16 times less computation than baselines), world knowledge depth, and scalability. These effectiveness, efficiency and scalability advantages position CapsFusion as a promising candidate for future scaling of LMM training.