India's communists once ruled millions. For the first time since 1957, India no longer has a single communist-led state government. The defeat of the Communist Party of India (Marxist)-led Left Democratic Front (LDF) in Kerala this month, after a decade in power, marked the end - at least for now - of one of the world's most enduring experiments in democratic communism. At their peak, India's communist parties ruled states stretching from West Bengal to Kerala and Tripura. They impacted the lives of more than 100 million people through trade unions, peasant organisations, student wings and disciplined cadre networks.

Smooth functions on graphs have wide applications in manifold and semi-supervised learning. In this paper, we study a bandit problem where the payoffs of arms are smooth on a graph. This framework is suitable for solving online learning problems that involve graphs, such as content-based recommendation. In this problem, each recommended item is a node and its expected rating is similar to its neighbors. The goal is to recommend items that have high expected ratings. We aim for the algorithms where the cumulative regret would not scale poorly with the number of nodes. In particular, we introduce the notion of an effective dimension, which is small in real-world graphs, and propose two algorithms for solving our problem that scale linearly in this dimension. Our experiments on real-world content recommendation problem show that a good estimator of user preferences for thousands of items can be learned from just tens nodes evaluations.

We consider Constrained Online Convex Optimization (COCO) with adversarially chosen constraints. At each round, the learner chooses an action before observing the loss and constraint function for that round. The goal is to achieve small static regret against the best point satisfying all constraints while also controlling cumulative constraint violation ($\mathsf{CCV}$). For strongly convex losses, state-of-the-art algorithms achieve $O(\log T)$ regret and $O(\sqrt{T \log T})$ $\mathsf{CCV}.$ The corresponding best-known bounds for convex losses is $O(\sqrt{T})$ regret and $O(\sqrt{T} \log T)$ $\mathsf{CCV}$. In this paper, we give a simple projection-based algorithm that simultaneously achieves $O(\log T)$ regret and $O(\log T)$ $\mathsf{CCV}$ for strongly-convex losses, yielding an exponential improvement in the $\mathsf{CCV}$. For the convex losses, our algorithm improves the $\mathsf{CCV}$ to $O(\sqrt{T})$ while maintaining the optimal $O(\sqrt{T})$ regret. The key to our improvement is a recent geometric result for self-contracted curves, which may be of independent interest.

Standard Chartered said it would cut 15% of its corporate function roles by 2030. Standard Chartered said it would cut 15% of its corporate function roles by 2030. Standard Chartered plans to cut more than 7,000 jobs over the next four years as it increasingly uses artificial intelligence. The London-headquartered lender is one of the first major global banks to lay out plans to cut thousands of jobs, citing AI as a driver to make its operations slimmer as it seeks to increase its profitability and tackle competition. StanChart said on Tuesday it would cut 15% of its back-office roles by 2030, which would result in about 7,800 redundancies out of its more than 52,000 staff in such roles.

Tata-ASML deal: How significant is it for India's semiconductor push? India's Tata Electronics has signed a deal with the Dutch technology giant ASML (Advanced Semiconductor Materials Lithography) to build India's first front-end semiconductor fabrication plant as New Delhi pushes to develop a domestic semiconductor manufacturing base. Front-end manufacturing refers to the building of microscopic circuits onto a blank silicon wafer using specialised lithographic machines. ASML is a pioneer of lithographic technology used in the mass production of microchips across the world. Semiconductor chips power modern technology and are critical for everything from smartphones and cars to artificial intelligence systems and defence technology.

India's Tata Electronics has signed a deal with Dutch technology giant ASML to build a major semiconductor plant in western India, as Prime Minister Narendra Modi visited the Netherlands during his European tour. The agreement, announced on Saturday, will support the development of Tata's semiconductor facility in Dholera, Gujarat - Modi's home state. The Dutch company said it would help "establish and ramp up" production at the plant by supplying its cutting-edge chipmaking tools. Tata Electronics plans to invest $11bn in the facility, which is expected to manufacture chips for artificial intelligence, the automotive industry and other sectors. ASML chief executive Christophe Fouquet said the company saw "many compelling opportunities" in India's growing semiconductor industry.

India stands out as one of the biggest losers as the artificial intelligence trade reshapes global investment flows. In a stark shift, the country's stock market is on the verge of dropping out of the world's five biggest for the first time in three years. Without the AI-driven rallies powering Taiwan and South Korea, there's a growing risk that India falls further behind rather than regaining lost ground. The rationale goes far beyond Indian equities being relatively expensive or corporate earnings slowing. Global investors, who not long pushed India close to rivaling China in emerging-market portfolios, are now chasing themes the country's market largely lacks: chip manufacturing, computing infrastructure and AI models. While India has talent, demand and digital scale, few of its corporate champions are directly linked to that buildout.

A Qatar-based professor has pioneered a non-invasive eye scan to detect neurodegenerative diseases years before symptoms appear. The technology uses AI to analyse the eye and can identify early signs of dementia, Parkinson's disease, and other diseases within minutes. Church leaders killed in latest ethnic violence in India's Manipur

Asteroseismology is the study of resonant oscillations of stars to infer their internal structure and dynamics. It is also a powerful tool for precisely determining stellar parameters such as mass, radius, surface gravity, and age. The ongoing TESS mission, with its nearly complete sky coverage, presents a unique opportunity to uniformly probe stellar populations across the Milky Way. TESS is estimated to have observed more than 300,000 oscillating red giants, most of which have one to two months of observations. Given the scale of this dataset, we need a fast, efficient, and robust way to analyse the data. In this work, our objective is to develop a machine learning (ML) based method to infer asteroseismic parameters from short-duration observations. Specifically, we focus on two global seismic parameters, the large frequency separation ($Δν$) and the frequency at maximum power ($ν_{\mathrm{max}}$), from one-month-long TESS observations of red giants. Meanwhile, for K2 data, our focus extends to inferring the period spacings of dipolar gravity modes ($ΔΠ_{1}$), in addition to $Δν$ and $ν_{\mathrm{max}}$. Our findings demonstrate that our machine learning algorithm can accurately infer $Δν$ and $ν_{\mathrm{max}}$ for approximately 50% of samples created by taking one-month Kepler and K2 observations. For TESS one sector data however, we recover reliable $Δν$ for only about 23% of the stars. Additionally, we get reliable $ΔΠ_{1}$ inferences for about 200 young red-giants from K2. For these $ΔΠ_{1}$ inferences, we see a good match with the well known $Δν-ΔΠ_{1}$ degenerate sequence observed in Kepler red-giants.

Urban air quality forecasting is challenging because pollutant concentrations are nonlinear, nonstationary, spatiotemporally dependent, and often affected by anomalous observations caused by traffic congestion, industrial emissions, and seasonal meteorological variability. This study proposes a Graph Convolutional Support Vector Regression (GCSVR) framework for robust spatiotemporal forecasting of urban air pollution. The model combines graph convolutional learning to capture inter-station spatial dependence with support vector regression to model nonlinear temporal dynamics while reducing sensitivity to outlier observations. The proposed framework is evaluated using air quality records from 37 monitoring stations in Delhi and 18 stations in Mumbai, representing inland and coastal metropolitan environments in India. Forecasting performance is assessed across multiple horizons and compared with established temporal and spatiotemporal benchmarks. The results show that GCSVR consistently improves predictive accuracy and maintains stable performance across seasons and outlier-prone pollution episodes. Statistical test further confirms the reliability of the proposed approach across the two cities. Finally, conformal prediction is integrated with GCSVR to generate calibrated prediction intervals, enhancing its practical value for uncertainty-aware air quality monitoring and public health decision-making.