The governor of Russia's Belgorod region has said that Ukraine fired cluster munitions at a village near the Ukrainian border on Friday, but that there were no casualties or damage. The governor made the statement on Saturday during a daily briefing on his Telegram channel, without providing visual evidence. There was no immediate comment from Ukrainian authorities. "In Belgorod district, 21 artillery shells and three cluster munitions from a multiple-launch rocket system were fired at the village of Zhuravlevka," Governor Vyacheslav Gladkov said. Ukraine received cluster bombs from the United States this month, but it has pledged to use them only to dislodge concentrations of enemy soldiers. They contain dozens of small bomblets that rain shrapnel over a wide area, but are banned in many countries due to the potential danger they pose to civilians.

Large-dimensional empirical data in science and engineering frequently has low-rank structure and can be represented as a combination of just a few eigenmodes. Because of this structure, we can use just a few spatially localized sensor measurements to reconstruct the full state of a complex system. The quality of this reconstruction, especially in the presence of sensor noise, depends significantly on the spatial configuration of the sensors. Multiple algorithms based on gappy interpolation and QR factorization have been proposed to optimize sensor placement. Here, instead of an algorithm that outputs a singular "optimal" sensor configuration, we take a thermodynamic view to compute the full landscape of sensor interactions induced by the training data. The landscape takes the form of the Ising model in statistical physics, and accounts for both the data variance captured at each sensor location and the crosstalk between sensors. Mapping out these data-induced sensor interactions allows combining them with external selection criteria and anticipating sensor replacement impacts.

"Oppenheimer" director Christopher Nolan spoke of the historical significance in artificial intelligence and compared it to the creation of the atomic bomb. 'OPPENHEIMER MOMENT': Hollywood director Christopher Nolan spoke with Fox News Digital on artificial intelligence's "Oppenheimer moment." Nolan compared AI to the creation of the atomic bomb and stated, "It's really the looking back through Oppenheimer's story and saying, 'Okay, what could have been done differently? What are the responsibilities of people who create technology that can go out and have unintended impacts?'" Continue reading… 'MINING OUR PERSONHOODS': Companies like OpenAI and Google have taken your data to train AI systems, attorney Ryan J. Clarkson writes in an op-ed. If you posted it, its most likely been taken.

The leader of the United Kingdom's Secret Intelligence Service, commonly known as MI6, gave a rare speech in Prague Wednesday during which he encouraged Russians opposed to the war in Ukraine to spy for the British, telling any defectors from the Kremlin, "Our door is always open." "There are many Russians today who are silently appalled by the sight of their armed forces pulverizing Ukrainian cities, expelling innocent families from their homes and kidnapping thousands of children," MI6 chief Richard Moore said from the British embassy in Prague, according to The Telegraph. "They are watching in horror as their soldiers ravage a kindred country. They know in their hearts that Putin's case for attacking a fellow Slavic nation is fraudulent, a miasma of lies and fantasy." Moore stated that "many Russians are wrestling with the same dilemmas and the same tugs of conscience" as those a generation ago did in 1968 when Soviet tanks crushed the Prague spring uprisings. "I invite them to do what others have already done this past 18 months and join hands with us. Our door is always open," the U.K. Secret Intelligence Service chief said.

Russia launched overnight air attacks on Ukraine's south and east using drones and possibly ballistic missiles, Ukrainian officials said. The southern port of Odesa and the Mykolaiv, Donetsk, Kherson, Zaporizhia and Dnipropetrovsk regions were under threat of Russian drone attacks. Ukraine's air force said it downed 31 out of 36 Iranian-made Shahed kamikaze drones, all six Kalibr cruise missiles and one reconnaissance drone launched by Russia overnight. Russia's defence ministry said it carried out overnight attacks on two Ukrainian port cities in what it called "a mass revenge strike", a day after an attack on the Crimean Bridge. The ministry said in a statement it struck Odesa and Mykolaiv and hit all targets.

With the increasing application of robots, stable and efficient Visual Odometry (VO) algorithms are becoming more and more important. Based on the Fourier Mellin Transformation (FMT) algorithm, the extended Fourier Mellin Transformation (eFMT) is an image registration approach that can be applied to downward-looking cameras, for example on aerial and underwater vehicles. eFMT extends FMT to multi-depth scenes and thus more application scenarios. It is a visual odometry method which estimates the pose transformation between three overlapping images. On this basis, we develop an optimized eFMT algorithm that improves certain aspects of the method and combines it with back-end optimization for the small loop of three consecutive frames. For this we investigate the extraction of uncertainty information from the eFMT registration, the related objective function and the graph-based optimization. Finally, we design a series of experiments to investigate the properties of this approach and compare it with other VO and SLAM (Simultaneous Localization and Mapping) algorithms. The results show the superior accuracy and speed of our o-eFMT approach, which is published as open source.

Pawleys Island, South Carolina, Mayor Brian Henry tells "Your World" that Hurricane Ian was different and brought a significant storm surge to the island. A high-tech sailing drone was deployed onto the Atlantic Ocean near Charleston, South Carolina, this past weekend to collect weather data directly from wicked hurricanes. The autonomous ocean drone, known as a saildrone, was redeployed by California-based company Saildrone Inc., which designs and operates autonomous ocean drones, in partnership with the National Oceanic and Atmospheric Administration (NOAA) to assist the agency in data collection on hurricanes. The same saildrone made international headlines in 2021 when it captured the "first-ever video from inside a major hurricane at sea" when Hurricane Sam barreled across the Atlantic. NOAA has previously incorporated drones into its research of hurricanes and 2023 will see an even larger and more high-tech fleet.

Functional data analysis (FDA) (Ramsay and Silverman 2005) is naturally apt to represent and model this kind of data, as it allows preserving their continuous nature, and provides a rigorous mathematical framework. Among the others, Zhou and Pan 2014 analyzed temperature surfaces, presenting two approaches for Functional Principal Component Analysis (FPCA) of functions defined on a non-rectangular domain, Porro-Muñoz et al. 2014 focuses on image processing using FDA, while a novel regularization technique for Gaussian random fields on a rectangular domain has been proposed by Rakêt 2010 and applied to 2D electrophoresis images. Another bivariate smoothing approach in a penalized regression framework has been introduced by Ivanescu and Andrada 2013, allowing for the estimation of functional parameters of two-dimensional functional data. As shown by Gervini 2010, even mortality rates can be interpreted as two-dimensional functional data. Whereas in all the reviewed works functions are assumed to be realization of iid or at least exchangeable random objects, to the best of our knowledge there is no literature focusing on forecasting time-dependent two-dimensional functional data. In this work, we focus on time series of surfaces, representing them as two-dimensional Functional Time Series (FTS).

Large language models (LLMs) are trained to imitate humans to explain human decisions. However, do LLMs explain themselves? Can they help humans build mental models of how LLMs process different inputs? To answer these questions, we propose to evaluate $\textbf{counterfactual simulatability}$ of natural language explanations: whether an explanation can enable humans to precisely infer the model's outputs on diverse counterfactuals of the explained input. For example, if a model answers "yes" to the input question "Can eagles fly?" with the explanation "all birds can fly", then humans would infer from the explanation that it would also answer "yes" to the counterfactual input "Can penguins fly?". If the explanation is precise, then the model's answer should match humans' expectations. We implemented two metrics based on counterfactual simulatability: precision and generality. We generated diverse counterfactuals automatically using LLMs. We then used these metrics to evaluate state-of-the-art LLMs (e.g., GPT-4) on two tasks: multi-hop factual reasoning and reward modeling. We found that LLM's explanations have low precision and that precision does not correlate with plausibility. Therefore, naively optimizing human approvals (e.g., RLHF) may not be a sufficient solution.

Since their first applications, Convolutional Neural Networks (CNNs) have solved problems that have advanced the state-of-the-art in several domains. CNNs represent information using real numbers. Despite encouraging results, theoretical analysis shows that representations such as hyper-complex numbers can achieve richer representational capacities than real numbers, and that Hamilton products can capture intrinsic interchannel relationships. Moreover, in the last few years, experimental research has shown that Quaternion-Valued CNNs (QCNNs) can achieve similar performance with fewer parameters than their real-valued counterparts. This paper condenses research in the development of QCNNs from its very beginnings. We propose a conceptual organization of current trends and analyze the main building blocks used in the design of QCNN models. Based on this conceptual organization, we propose future directions of research.