Two Literal Crypto Bros Built a Real Estate Empire. In 2019, two Canadian brothers blew into Detroit with an irresistible pitch: For $50, almost anyone could become a property owner. When houses decayed and the city intervened, the blame games began. A fire broke out at 10410 Cadieux in March 2025, burning a hole in the roof. The smell hit me first: damp brick, stagnant water, mold, and bleach. I was partway down a flight of wooden stairs that led to the basement of a 1920s duplex in east Detroit, Michigan. Leading the way was Cornell Dorris, a tenant in the building for nearly a decade. Dorris is in his early forties, has two daughters who visit on weekends, and makes a living smoking meat and cooking for events. As my eyes adjusted, I made out rodent droppings and a black puddle that spread across the basement floor. "Anytime it rains, the water comes down," Dorris said. The air was unnaturally heavy, and I felt a nagging urge to leave. Dorris doesn't have a typical landlord. Almost four years ago, his building was acquired by a startup called RealToken, or RealT.

America's drone industry lags behind China's dominance, but Congress and Trump's $2.5 billion investment aims to rebuild U.S. military and commercial drone production by 2027.

Missing records are a perennial problem in analysis of complex data of all types, when the target of inference is some function of the full data law. In simple cases, where data is missing at random or completely at random (Rubin, 1976), well-known adjustments exist that result in consistent estimators of target quantities. Assumptions underlying these estimators are generally not realistic in practical missing data problems. Unfortunately, consistent estimators in more complex cases where data is missing not at random, and where no ordering on variables induces monotonicity of missingness status are not known in general, with some notable exceptions (Robins, 1997), (Tchetgen Tchetgen et al, 2016), (Sadinle and Reiter, 2016). In this paper, we propose a general class of consistent estimators for cases where data is missing not at random, and missingness status is non-monotonic. Our estimators, which are generalized inverse probability weighting estimators, make no assumptions on the underlying full data law, but instead place independence restrictions, and certain other fairly mild assumptions, on the distribution of missingness status conditional on the data. The assumptions we place on the distribution of missingness status conditional on the data can be viewed as a version of a conditional Markov random field (MRF) corresponding to a chain graph. Assumptions embedded in our model permit identification from the observed data law, and admit a natural fitting procedure based on the pseudo likelihood approach of (Besag, 1975). We illustrate our approach with a simple simulation study, and an analysis of risk of premature birth in women in Botswana exposed to highly active anti-retroviral therapy.

Ukrainian President Volodymyr Zelensky is set to visit Prime Minister Sir Keir Starmer in the UK on Tuesday to agree a new defence partnership aimed at tackling cheap attack drones. Downing Street said the deal would bring together Ukrainian expertise and the UK's industrial base to manufacture and supply drones and other capabilities. The two leaders are also expected to discuss further support Ukraine against Russia's full-scale invasion, now in its fourth year. Their meeting comes as the US-Israeli war with Iran enters a third week, during which US President Donald Trump has criticised the UK and other countries over the extent of their response to the conflict. Under the partnership between the UK and Ukraine, closer co-operation in the defence industries will also be sought with third countries as part of efforts to bolster international security.

US President Donald Trump has renewed his criticism of the UK government over its response to the Iran conflict, after Prime Minister Sir Keir Starmer said the country would not be drawn into the wider war. Trump told reporters on Monday he was not happy with the UK, adding it should be involved enthusiastically in efforts to reopen the Strait of Hormuz - a vital oil shipping channel . He later told a press conference there were some countries that greatly disappointed me before he singled out the UK, which he said had been considered the Rolls-Royce of allies. Trump's remarks came after Sir Keir said the UK was working with allies on a viable, collective plan to reopen the strait. Sir Keir also said the UK already had minehunters in the region but there was no decision yet on what action would be taken.

Graves being prepared for the victims of an airstrike on a school in Minab in southern Iran, 2 March 2026. Graves being prepared for the victims of an airstrike on a school in Minab in southern Iran, 2 March 2026. A photo of Iran's bombed schoolgirl graveyard went around the world. T he graves, freshly dug, lie in neat rows of 20 across. More than 60 have already been carved out of the earth, with a few clusters of people standing gathered around them.

India's outsourcing industry is worth $300bn. Indian technology stocks have seen an unprecedented rout over the past few weeks over fears of artificial intelligence upending the traditional outsourcing model that powers the country's $300bn (£223bn) back-office industry. The sell-off - part of a global correction in traditional software and IT stocks - preceded the market nervousness caused by recent geopolitical uncertainty, and is particularly significant for India. Over the past three-and-a-half decades, India's software industry has created millions of white-collar jobs, spawning a new middle class driven by high ambition and strong purchasing power. This, in turn, has fuelled demand for apartments, cars and restaurants across top-tier cities such as Bengaluru, Hyderabad and Gurugram over the past 30 years.

AI firm Anthropic seeks weapons expert to stop users from'misuse' The US artificial intelligence (AI) firm Anthropic is looking to hire a chemical weapons and high-yield explosives expert to try to prevent catastrophic misuse of its software. In other words, it fears that its AI tools might tell someone how to make chemical or radioactive weapons, and wants an expert to ensure its guardrails are sufficiently robust. In the LinkedIn recruitment post, the firm says applicants should have a minimum of five years experience in chemical weapons and/or explosives defence as well as knowledge of radiological dispersal devices - also known as dirty bombs. The firm told the BBC the role was similar to jobs in other sensitive areas that it has already created. Anthropic is not the only AI firm adopting this strategy.

Scaling laws for neural networks, in which the loss decays as a power-law in the number of parameters, data, and compute, depend fundamentally on the spectral structure of the data covariance, with power-law eigenvalue decay appearing ubiquitously in vision and language tasks. A central question is whether this spectral structure is preserved or destroyed when data passes through the basic building block of a neural network: a random linear projection followed by a nonlinear activation. We study this question for the random feature model: given data $x \sim N(0,H)\in \mathbb{R}^v$ where $H$ has $α$-power-law spectrum ($λ_j(H ) \asymp j^{-α}$, $α> 1$), a Gaussian sketch matrix $W \in \mathbb{R}^{v\times d}$, and an entrywise monomial $f(y) = y^{p}$, we characterize the eigenvalues of the population random-feature covariance $\mathbb{E}_{x }[\frac{1}{d}f(W^\top x )^{\otimes 2}]$. We prove matching upper and lower bounds: for all $1 \leq j \leq c_1 d \log^{-(p+1)}(d)$, the $j$-th eigenvalue is of order $\left(\log^{p-1}(j+1)/j\right)^α$. For $ c_1 d \log^{-(p+1)}(d)\leq j\leq d$, the $j$-th eigenvalue is of order $j^{-α}$ up to a polylog factor. That is, the power-law exponent $α$ is inherited exactly from the input covariance, modified only by a logarithmic correction that depends on the monomial degree $p$. The proof combines a dyadic head-tail decomposition with Wick chaos expansions for higher-order monomials and random matrix concentration inequalities.

Asynchronous Bayesian optimization is widely used for gradient-free optimization in domains with independent parallel experiments and varying evaluation times. Existing methods posit that standard acquisitions lead to redundant and repeated queries, proposing complex solutions to enforce diversity in queries. Challenging this fundamental premise, we show that methods, like the Upper Confidence Bound, can in fact achieve theoretical guarantees essentially equivalent to those of sequential Thompson sampling. A conceptual analysis of asynchronous Bayesian optimization reveals that existing works neglect intermediate posterior updates, which we find to be generally sufficient to avoid redundant queries. Further investigation shows that by penalizing busy locations, diversity-enforcing methods can over-explore in asynchronous settings, reducing their performance. Our extensive experiments demonstrate that simple standard acquisition functions match or outperform purpose-built asynchronous methods across synthetic and real-world tasks.