The Stream How does targeting water supply during war worsen the scarcity crisis? We explore why water infrastructure is increasingly being targeted in the midst of war and conflict. Water sustains life, but what happens when it is weaponised? In the ongoing US-Israel war on Iran, desalination plants supplying millions in the Gulf have become targets. This reflects a growing pattern: water infrastructure is increasingly vulnerable as global scarcity intensifies.

Learning disentangled representations for user intentions from multi-feedback (i.e., positive and negative feedback) can enhance the accuracy and explainability of recommendation algorithms. However, learning such disentangled representations from multi-feedback data is challenging because i) multi-feedback is complex: there exist complex relations among different types of feedback (e.g., click, unclick, and dislike, etc) as well as various user intentions, and ii) multi-feedback is noisy: there exists noisy (useless) information both in features and labels, which may deteriorate the recommendation performance. Existing disentangled recommendation works only focus on positive feedback, failing to handle the complex relations and noise hidden in multi-feedback data. To solve this problem, in this work we propose a Curriculum Disentangled Recommendation (CDR) model that is capable of efficiently learning disentangled representations from complex and noisy multi-feedback for better recommendation.

What are Russia's gains from the Iran war? 'We are not losers; we are winners' Ukrainian officials say Russian drones have again attacked the southern port city of Odesa, injuring at least 11 people, including two children, and damaging homes and important infrastructure. Odesa Governor Oleh Kiper said the attack affected three districts, hitting residential buildings, vehicles and civilian facilities, including a hotel, warehouses and funicular railway. Windows shattered in many buildings and the port area sustained damage. Law enforcement agencies are documenting the latest war crimes committed by Russia against the peaceful population of [the] Odesa region," Kiper said. Russian attacks killed one person in the southeastern Zaporizhzhia region, according to Governor Ivan Fedorov. "A 59-year-old man died as a result of an enemy attack on the Zaporizhzhia region," Fedorov wrote on Telegram. A Ukrainian drone attack killed an employee at the Zaporizhzhia nuclear power plant, which was captured by Russian forces and is shut down. "A driver was killed today when a Ukrainian Armed Forces drone struck the transport department at the Zaporizhzhia Nuclear Power Plant," said a statement from plant managers who were installed by Russia. Regional governor Fedorov said Russian forces launched 629 strikes across 45 settlements in the region in a single day, with at least 50 reports of damage to homes and infrastructure. Russian officials reported Ukrainian drone attacks in the Belgorod border region, where at least one person was killed and four women injured, alongside damage to buildings and vehicles. The attacks come as diplomatic efforts to end the war remain stalled. Donald Trump said on Sunday that he has had "good conversations" with Presidents Vladimir Putin and Volodymyr Zelenskyy. "We're working on the Russia situation, Russia and Ukraine, and hopefully we're going to get it," Trump said on Fox News. "I do have conversations with him, and I do have conversations with President Zelenskyy, and good conversations," he said. "The hatred between President Putin and President Zelenskyy is ridiculous.

Recovering the skeletal shape of an animal from a monocular video is a longstanding challenge. Prevailing animal reconstruction methods often adopt a control-point driven animation model and optimize bone transforms individually without considering skeletal topology, yielding unsatisfactory shape and articulation. In contrast, humans can easily infer the articulation structure of an unknown animal by associating it with a seen articulated character in their memory. Inspired by this fact, we present CASA, a novel Category-Agnostic Skeletal Animal reconstruction method consisting of two major components: a video-to-shape retrieval process and a neural inverse graphics framework. During inference, CASA first retrieves an articulated shape from a 3D character assets bank so that the input video scores highly with the rendered image, according to a pretrained language-vision model. CASA then integrates the retrieved character into an inverse graphics framework and jointly infers the shape deformation, skeleton structure, and skinning weights through optimization.

It is a challenge to detect complicated data relationships thoroughly. Here, we propose a new statistical measure, named the absolute neighbour difference based neighbour correlation coefficient, to detect the associations between variables through examining the heteroscedasticity of the unpredictable variation of dependent variables. Different from previous studies, the new method concentrates on measuring nonfunctional relationships rather than functional or mixed associations. Either used alone or in combination with other measures, it enables not only a convenient test of heteroscedasticity, but also measuring functional and nonfunctional relationships separately that obviously leads to a deeper insight into the data associations. The method is concise and easy to implement that does not rely on explicitly estimating the regression residuals or the dependencies between variables so that it is not restrict to any kind of model assumption. The mechanisms of the correlation test are proved in theory and demonstrated with numerical analyses.

Quantum neural networks (QNNs) have emerged as a leading strategy to establish applications in machine learning, chemistry, and optimization. While the applications of QNN have been widely investigated, its theoretical foundation remains less understood. In this paper, we formulate a theoretical framework for the expressive ability of data re-uploading quantum neural networks that consist of interleaved encoding circuit blocks and trainable circuit blocks. First, we prove that single-qubit quantum neural networks can approximate any univariate function by mapping the model to a partial Fourier series. We in particular establish the exact correlations between the parameters of the trainable gates and the Fourier coefficients, resolving an open problem on the universal approximation property of QNN. Second, we discuss the limitations of single-qubit native QNNs on approximating multivariate functions by analyzing the frequency spectrum and the flexibility of Fourier coefficients. We further demonstrate the expressivity and limitations of single-qubit native QNNs via numerical experiments. We believe these results would improve our understanding of QNNs and provide a helpful guideline for designing powerful QNNs for machine learning tasks.