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.

Recovering the random graph model from an observed collection of networks is known to present significant challenges in the setting, where the networks do not share a common node set and have different sizes. More specifically, the goal is the estimation of the graphon function that parametrizes the nonparametric exchangeable random graph model. Existing methods typically suffer from either limited accuracy or high computational complexity. We introduce a new histogram-based estimator with low algorithmic complexity that achieves high accuracy by jointly aligning the nodes of all graphs, in contrast to most conventional methods that order nodes graph by graph. Consistency results of the proposed graphon estimator are established. A numerical study shows that the proposed estimator outperforms existing methods in terms of accuracy, especially when the dataset comprises only small and variable-size networks. Moreover, the computing time of the new method is considerably shorter than that of other consistent methodologies. Additionally, when applied to a graph neural network classification task, the proposed estimator enables more effective data augmentation, yielding improved performance across diverse real-world datasets.

We study best-arm identification in stochastic dueling bandits under the sole assumption that a Condorcet winner exists, i.e., an arm that wins each noisy pairwise comparison with probability at least $1/2$. We introduce a new identification procedure that exploits the full gap matrix $Δ_{i,j}=q_{i,j}-\tfrac12$ (where $q_{i,j}$ is the probability that arm $i$ beats arm $j$), rather than only the gaps between the Condorcet winner and the other arms. We derive high-probability, instance-dependent sample-complexity guarantees that (up to logarithmic factors) improve the best known ones by leveraging informative comparisons beyond those involving the winner. We complement these results with new lower bounds which, to our knowledge, are the first for Condorcet-winner identification in stochastic dueling bandits. Our lower-bound analysis isolates the intrinsic cost of locating informative entries in the gap matrix and estimating them to the required confidence, establishing the optimality of our non-asymptotic bounds. Overall, our results reveal new regimes and trade-offs in the sample complexity that are not captured by asymptotic analyses based only on the expected budget.

Your Out-of-Distribution Detection Method is Not Robust!

Could Iran be using China's BeiDou system? Drone strike hits Al-Rasheed hotel in Baghdad's Green Zone near US embassy, no casualties reported A prominent hotel in central Baghdad's heavily fortified Green Zone was struck by a drone, amid reports that Iraqi air defences intercepted an attack over the United States Embassy. The strike on Monday evening hit the top floor of Al-Rasheed Hotel, causing damage but no casualties, according to two Iraqi security officials cited by The Associated Press (AP) news agency. Security sources told the Reuters news agency that two Katyusha rockets had been intercepted that evening near the US Embassy in the Green Zone, which houses diplomatic missions as well as international institutions and government offices. Earlier Monday, the Iran-backed Kataib Hezbollah announced that Abu Ali Al-Askari, a prominent security official with the paramilitary group, had been killed, without giving details on the circumstances.

Trump announces White House Chief of Staff Susie Wiles diagnosed with'early stage' breast cancer Trump's billionaire adviser publicly rebukes Iran war as JD Vance camp erupts over Israel nuke threat Kristi Noem referred for criminal investigation after'lying under oath' about $220M vanity scheme You don't have to fly to Turkey or Thailand... and can do it on your lunch break! Diet that cures pain and inflammation, devised by experts: Constant sickness and aching joints are the first signs of problems that left unchecked can turn deadly. Timothee Chalamet and Kylie Jenner's strained Oscars chat decoded by lip-reader as he gets snubbed and mocked The snubbed A-lister, drunken pics and C-List stars who plagued the most'exclusive' party: All the Oscars gossip Hollywood didn't want you to see at very messy afterparty Proof Leonardo DiCaprio sent a CLONE to the Oscars... alarming truth about Teyana Taylor's blow up... and a very dirty Barbra Streisand rumor: KENNEDY's most brutal review yet NYC's smiling socialist mayor is VERY different behind the scenes, as progressives who crossed him allege tyrannical and ruthless behavior Awful Timothee Chalamet's ego is bigger than Kylie's inflated butt... but it's so clear what's really going on here. Trump stunned by lurid rumor about Iran's new'gay' ayatollah Chilling new details of dismembered Emily Pike's final hours after she was snatched in Arizona desert and man detectives now believe murdered her'It's like he was possessed': Terrifying moment Alexander brother turned into a'monster' and raped me... and the four chilling words he said after horror attack - alleged victim claims After Oscars 2026, the whispered fear among Hollywood doctors is now massive... this is so much bigger than Ozempic. A-list stars ditch formal Oscars red carpet dresses for sexy party looks - with Jeff Goldblum's wife Emilie Livingston, Heidi Klum, Amelia Gray Hamlin and Kate Hudson turning up the heat at Vanity Fair bash Shock as man begs for death penalty for HIMSELF after pinning dead pastor's hands to wall and targeting other religious leaders How Oscars 2026 proved Hollywood has overdosed on Ozempic: Leading doctors name stars now at'extreme' risk... and reveal terrifying new side effects Billionaire Peter Thiel holds secret'Antichrist' meetings on the Vatican's doorstep READ MORE: Catholic priest warns'the stage is set' for the rise of the Antichrist US billionaire Peter Thiel is hosting a series of closed-door lectures in Rome on the doorstep of the Vatican, focused on the concept of the Antichrist.

Hundreds of passengers flying to Dubai spent 11 hours on a flight to nowhere after their plane was turned back over Egypt. The Emirates flight EK24 set off from Edinburgh at 21:26 on Sunday and was due to land in Dubai at 06:49 on Monday. However, as the plane flew over Egypt, flights at Dubai International Airport were suspended following a fire caused by an Iranian drone hitting a fuel tank. The plane was forced to return to Edinburgh. Travel journalist Simon Calder told the BBC's Radio Scotland Breakfast programme that although Dubai was on the UK Foreign Office's No go list, many people were still taking the risk of flying there. No injuries were reported following the drone strike but officials said they had taken all necessary measures to ensure public safety.