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.

In this paper, we propose an autonomous information seeking visual question answering framework, AVIS. Our method leverages a Large Language Model (LLM) to dynamically strategize the utilization of external tools and to investigate their outputs, thereby acquiring the indispensable knowledge needed to provide answers to the posed questions. Responding to visual questions that necessitate external knowledge, such as "What event is commemorated by the building depicted in this image?", is a complex task. This task presents a combinatorial search space that demands a sequence of actions, including invoking APIs, analyzing their responses, and making informed decisions. We conduct a user study to collect a variety of instances of human decision-making when faced with this task. This data is then used to design a system comprised of three components: an LLM-powered planner that dynamically determines which tool to use next, an LLM-powered reasoner that analyzes and extracts key information from the tool outputs, and a working memory component that retains the acquired information throughout the process. The collected user behavior serves as a guide for our system in two key ways. First, we create a transition graph by analyzing the sequence of decisions made by users. This graph delineates distinct states and confines the set of actions available at each state. Second, we use examples of user decision-making to provide our LLM-powered planner and reasoner with relevant contextual instances, enhancing their capacity to make informed decisions. We show that AVIS achieves state-of-the-art results on knowledge-intensive visual question answering benchmarks such as Infoseek and OK-VQA.

Producing high-quality forecasts of key climate variables such as temperature and precipitation on subseasonal time scales has long been a gap in operational forecasting. Recent studies have shown promising results using machine learning (ML) models to advance subseasonal forecasting (SSF), but several open questions remain. First, several past approaches use the average of an ensemble of physics-based forecasts as an input feature of these models. However, ensemble forecasts contain information that can aid prediction beyond only the ensemble mean. Second, past methods have focused on average performance, whereas forecasts of extreme events are far more important for planning and mitigation purposes. Third, climate forecasts correspond to a spatially-varying collection of forecasts, and different methods account for spatial variability in the response differently. Trade-offs between different approaches may be mitigated with model stacking. This paper describes the application of a variety of ML methods used to predict monthly average precipitation and two meter temperature using physics-based predictions (ensemble forecasts) and observational data such as relative humidity, pressure at sea level, or geopotential height, two weeks in advance for the whole continental United States. Regression, quantile regression, and tercile classification tasks using linear models, random forests, convolutional neural networks, and stacked models are considered. The proposed models outperform common baselines such as historical averages (or quantiles) and ensemble averages (or quantiles). This paper further includes an investigation of feature importance, trade-offs between using the full ensemble or only the ensemble average, and different modes of accounting for spatial variability.

The Strait of Gibraltar is a region characterized by intricate oceanic sub-mesoscale features, influenced by topography, tidal forces, instabilities, and nonlinear hydraulic processes, all governed by the nonlinear equations of fluid motion. In this study, we aim to uncover the underlying physics of these phenomena within 3D MIT general circulation model simulations, including waves, eddies, and gyres. To achieve this, we employ Dynamic Mode Decomposition (DMD) to break down simulation snapshots into Koopman modes, with distinct exponential growth/decay rates and oscillation frequencies. Our objectives encompass evaluating DMD's efficacy in capturing known features, unveiling new elements, ranking modes, and exploring order reduction. We also introduce modifications to enhance DMD's robustness, numerical accuracy, and robustness of eigenvalues. DMD analysis yields a comprehensive understanding of flow patterns, internal wave formation, and the dynamics of the Strait of Gibraltar, its meandering behaviors, and the formation of a secondary gyre, notably the Western Alboran Gyre, as well as the propagation of Kelvin and coastal-trapped waves along the African coast. In doing so, it significantly advances our comprehension of intricate oceanographic phenomena and underscores the immense utility of DMD as an analytical tool for such complex datasets, suggesting that DMD could serve as a valuable addition to the toolkit of oceanographers.

FOX News' Greg Palkot reports the latest from the Israel-Lebanon border. A drone attack on a U.S. base in Syria was thwarted on Wednesday, according to a report. Two drones targeting Syria's al-Tanf region were disabled or destroyed by the base defense system, an Iraqi government source told Reuters. The thwarted attack comes as U.S. and Coalition Forces at Combined Joint Task Force Operation Inherent Resolve (CJTF-OIR) installations in Iraq and Syria have been attacked at least 27 times between Oct. 17-31. Of these attacks, 16 happened in Iraq and 11 took place in Syria.

Language models (LMs) often exhibit undesirable text generation behaviors, including generating false, toxic, or irrelevant outputs. Reinforcement learning from human feedback (RLHF) - where human preference judgments on LM outputs are transformed into a learning signal - has recently shown promise in addressing these issues. However, such holistic feedback conveys limited information on long text outputs; it does not indicate which aspects of the outputs influenced user preference; e.g., which parts contain what type(s) of errors. In this paper, we use fine-grained human feedback (e.g., which sentence is false, which sub-sentence is irrelevant) as an explicit training signal. We introduce Fine-Grained RLHF, a framework that enables training and learning from reward functions that are fine-grained in two respects: (1) density, providing a reward after every segment (e.g., a sentence) is generated; and (2) incorporating multiple reward models associated with different feedback types (e.g., factual incorrectness, irrelevance, and information incompleteness). We conduct experiments on detoxification and long-form question answering to illustrate how learning with such reward functions leads to improved performance, supported by both automatic and human evaluation. Additionally, we show that LM behaviors can be customized using different combinations of fine-grained reward models. We release all data, collected human feedback, and codes at https://FineGrainedRLHF.github.io.

Russia said Ukrainian drones damaged a nuclear waste storage facility at the Kursk Nuclear Power Plant on Thursday evening. This comes after the press service for the plant told journalists on Friday that there had been no significant damage from the attacks and that operations were continuing as normal. Intense fighting continued close to the city of Avdiivka, in the Donetsk region of eastern Ukraine. Russia's Ministry of Defence said its air defence systems destroyed 36 Ukraine-launched drones over the Black Sea off the Crimean Peninsula overnight. A statement from the ministry on Telegram did not provide much additional detail.