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Cargo documents appear to show Sightline has shipped its technology to Elbit Systems, an Israeli arms manufacturer that provides drones to the country's military. Cargo documents appear to show Sightline has shipped its technology to Elbit Systems, an Israeli arms manufacturer that provides drones to the country's military. A nti-war activists in Portland, Oregon, are pushing city authorities to ensure no local resources, tax breaks or investments support a local company that appears to be supplying artificial intelligence software to the Israeli military. The company, Sightline Intelligence, manufactures AI-supported video technology that is used in drones to interpret target movements and make quick decisions based on the perceived threat level. Cargo documents appear to show Sightline has shipped its technology to Elbit Systems, an Israeli arms manufacturer that provides drones to that country's military and exports to others.

What are Russia's gains from the Iran war? 'We are not losers; we are winners' Russian air attacks kill five at Ukraine's Naftogaz gas facilities At least five people have been killed in Russian air strikes on Ukrainian state-run gas facilities in the Poltava and Kharkiv regions, officials said, a day after Kyiv and Moscow announced unilateral ceasefires to take effect later this week. Three employees and two rescue workers were killed and 37 people were wounded in the overnight missile and drone barrage, Serhiy Koretskyi, the CEO of Ukraine's state energy company Naftogaz said on Tuesday. This was a combined strike involving UAVs (unmanned aerial vehicles) and ballistic missiles," said Koretskyi. He added that the attack cut gas supply to nearly 3,500 customers. Ukrainian President Volodymyr Zelenskyy said Russian authorities had shown "utter cynicism" by announcing a ceasefire and then launching missile and drone attacks on his country. "Russia could cease fire at any moment, and this would stop the war and our responses.

How well do you know Iran? The United Arab Emirates has said its air defences intercepted ballistic and cruise missiles fired from Iran, while a fire was reported at an oil facility in Fujairah after a suspected drone attack. Tehran has not officially commented. Qatar, Jordan, Saudi Arabia and Kuwait, along with the Gulf Cooperation Council (GCC) and the European Union, have condemned the suspected Iranian strike on the UAE. The incident comes as tensions rise, with United States President Donald Trump warning Iran would be "blown off the face of the earth" if US Navy ships are targeted in the Strait of Hormuz.

AI's hottest private companies have booming crypto shadow market Crypto platforms are offering trades tied to the most valuable private artificial intelligence companies on earth -- such as Anthropic -- that ordinary investors have almost no other way to access. The race to sell retail investors a piece of the artificial intelligence boom has gone mainstream -- closed-end funds, interval funds, special-purpose vehicles (SPVs). Now, crypto platforms are offering trades tied to the most valuable private AI companies on earth -- ones ordinary investors have almost no other way to access. The result is a new frontier in the financialization of private markets: crypto infrastructure, once the domain of digital token speculation, being redeployed to give traders a way to bet on Anthropic, OpenAI and SpaceX -- in real time, 24 hours a day, with leverage. Ventuals and PreStocks, two crypto venues riding that shift, have seen their trading activity -- measured by open interest and market value combined -- surge more than threefold since the start of the year to last month.

A blast at a fireworks factory in China's Hunan province has killed 21 people and left 61 wounded, according to state media. The explosion at the Changsha Liuyang Huasheng Fireworks plant happened at around 16:40 local time (08:40 GMT) on Monday, in the city of Liuyang, leading rescuers to evacuate everyone within a 3km (1.9mi) radius of the plant. Authorities deployed nearly 500 personnel to conduct search and rescue operations and treat the injured, while robots were used to help find those trapped within the building. Police, who are investigating the cause of the blast, have taken control measures against the person in charge of the fireworks company, Chinese state media reported. Authorities said that two gunpowder warehouses within the factory area posed a high risk amid rescue efforts, state media reported.

We study high-dimensional LASSO under differential privacy via objective perturbation with heterogeneous covariate scales. In practical scenarios, covariates often exhibit diverse scales; however, standard preprocessing is problematic under privacy constraints, as it consumes additional privacy budget. This heterogeneity induces effective anisotropy in the objective perturbation via the inverse Gram matrix of covariates, which can degrade the stability and accuracy of algorithms. To address this, we propose a Gram-based anisotropic objective perturbation, a ``pre-distortion" strategy that counteracts the distortion from the covariate structure to restore isotropy in the estimation process. Using an Approximate Message Passing (AMP) framework and state evolution analysis, we demonstrate that our proposed perturbation significantly stabilizes convergence and improves both statistical efficiency and privacy performance compared to standard uniform noise injection. Our results provide theoretical insights into designing stable and efficient private estimators without relying on data-dependent preprocessing.

In 2011, Judea Pearl received the Turing Award, considered the Nobel Prize in Computing, for fundamental contributions to artificial intelligence through the development of a calculus for probabilistic and causal reasoning. It includes pioneering the development of causal discovery algorithms. These computer algorithms can analyze large multivariate datasets and automatically discover the causal relationships among the constituent variables. They have been widely used in many critical fields such as medicine and economics to support decisions. However, to our knowledge, they have not been leveraged in law. This paper attempts to alleviate this gap by investigating whether causal discovery algorithms can be leveraged for automated generation of legal arguments. To that end, a novel legal dataset is prepared by identifying 17 legal concepts, such as physical assault and property dispute. A curated collection of 150 homicide cases are annotated with these concepts, e.g., a case is annotated with physical assault only if a physical assault had been reported in that case. Subsequently, a selected set of widely-used causal discovery algorithms is applied to the annotated dataset to discover the causal relationships between the legal concepts. Additionally, the degrees of belief associated with the discovered relationships are quantified in mathematical probabilities. It is shown that some of the causal relationships help generate viable legal arguments, e.g., if one could establish that a physical assault has not taken place during a homicide, it should be a sufficient condition (with probability 1) to establish that the homicide has not been committed due to a property-related dispute. Thus, this paper shows that causal discovery algorithms can be helpful in generating legal arguments, opening up avenues for promising future endeavors.

The proliferation of large-scale and structurally complex data has spurred the integration of machine learning methods into statistical modeling. Recurrent neural networks (RNNs), a foundational class of models for time-dependent data, can be viewed as nonlinear extensions of classical autoregressive moving average models. Despite their flexibility and empirical success in machine learning, RNNs often suffer from limited interpretability and slow training, which hinders their use in statistics. This paper proposes the Parallelized RNN (ParaRNN), a novel model composed of multiple small recurrent units. ParaRNN admits an additive representation that decouples recurrent dynamics into interpretable components, whose behavior can be characterized through recurrence features. This interpretability enables its applications in nonparametric regression for time-dependent data, while the design also allows efficient parallelization. The approximation capacity and non-asymptotic prediction error bounds in a nonparametric regression setting are established for ParaRNN. Empirical results on three sequential modeling tasks further demonstrate that ParaRNN achieves performance comparable to vanilla RNNs while offering improved interpretability and efficiency.

A text scam impersonating state courts demands drivers pay $6.99 for fake traffic violations via QR codes. The scheme has hit residents in at least eight U.S. states.