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It began with a fear familiar to millions of young Indians: if the examinations that determine their future cannot be trusted, what can? Weeks later, on Monday, tens of thousands of young men and women tried to march on India's parliament. Police responded with tear gas and baton charges. Thousands more have returned to the streets in the days since. The Cockroach Janta Party (CJP), a youth-led movement born out of anger over repeated examination paper leaks, has quickly become India's biggest student mobilisation in years. It is demanding education reforms and the resignation of Education Minister Dharmendra Pradhan.

Follow this section to personalize your feed and get instant alerts. Follow Go to your personalized feed WHY FOLLOW? Smart Alerts: Get notified about major news as it happens. Follow this tag to personalize your feed and get instant alerts. Follow Go to your personalized feed WHY FOLLOW? Smart Alerts: Get notified about major news as it happens.

India has successfully tested its first private-sector orbital rocket, marking a milestone in New Delhi's ambition to become a major player in the global space economy. The three-stage 22-metre Vikram-1 was launched from the Satish Dhawan Space Centre in Sriharikota and deployed customer payloads into a 450km (280-mile) low-Earth orbit, making India the third country to achieve orbital launch capability through private enterprise. It also carried experimental equipment, a lab-grown diamond and a miniature 18-carat gold sculpture commemorating India's national space programme. India Prime Minister Narendra Modi hailed the achievement, saying that it will "encourage countless youngsters to dream bigger and innovate fearlessly". Founded in 2018, Skyroot is among a new generation of Indian space startups that have attracted backing from global investors following the sector's liberalisation.

Netflix says it's already used AI in'roughly 300' titles this year Netflix says it's already used AI in'roughly 300' titles this year Don't expect that number to shrink any time soon. Netflix hasn't made any secret of its interest in artificial intelligence, and now we have a sense of how those tools are being used in its content. In 2026, GenAI workflows have been used in roughly 300 of our titles, with the largest concentration of work in post-production, according to the shareholder letter detailing its second-quarter financials. The company named three projects -- (India), (Brazil) and (US) -- that used generative AI to create highly complex sequences, but the tech is becoming more widespread at this point. We already knew that Netflix had applied generative AI in at least one original show as of last July, but between making acquisitions and launching new specialized studios, its ambitions clearly extended further.

Follow this section to personalize your feed and get instant alerts. Follow Go to your personalized feed WHY FOLLOW? Smart Alerts: Get notified about major news as it happens. Follow this tag to personalize your feed and get instant alerts. Follow Go to your personalized feed WHY FOLLOW? Smart Alerts: Get notified about major news as it happens. In early June, Tenzin Gyatso, the 14th Dalai Lama, was wheeled into an operating room at Apollo Hospital in New Delhi. Spiritually, His Holiness is an emanation, or of the bodhisattva Chenrezig, who renounced nirvana to help mankind.

How AI is being weaponised against India's Muslim women NewsFeed How AI is being weaponised against India's Muslim women For years, India's Muslim women have been subject to online abuse. Now researchers warn that generative AI is taking that harassment to a new level, making it easier to create fake sexualised imagery and propaganda targeting Muslim women. Trump made $1.4B from crypto ventures in first year back in office

Deep neural networks (DNNs) frequently fail to generalize to out-of-distribution (OOD) medical images because of variations in scanners and acquisition protocols. Retraining DNN models to address these distribution shifts is often impractical due to the high cost of acquiring and annotating new medical datasets. To address this, we introduce VarDeepPCA, a novel lightweight variational DNN framework designed to restore/refine degraded segmentation maps by leveraging intrinsic geometric priors. Unlike existing approaches that require target-domain data or extensive pre-training, our VarDeepPCA explicitly learns a distribution of valid anatomical geometries using only small in-distribution (ID) datasets. Theoretically, our novel variational learning framework leverages a reinterpretation of the softmax mapping to implicitly perform exact distribution modeling, thereby enabling computationally efficient, sampling-free learning and inference. This also enables VarDeepPCA to provide uncertainty estimates associated with its restored segmentation maps. We empirically validate our framework across 4 distinct clinical applications, using 14 publicly available datasets, involving segmentation of the myocardium, neuroretinal rim, prostate, and fetal head. Comparisons against 15 existing methods demonstrate that VarDeepPCA consistently restores segmentation maps produced by the existing methods on OOD data to (i) significantly improve anatomical plausibility of geometries and clinical utility of the segmentations, and (ii) significantly reduce errors, without needing any more training data than that used by existing methods.

Air pollution was responsible for 2.6 million deaths across South Asia in 2021 alone, with brick manufacturing contributing significantly to this burden. In particular, the Indo-Gangetic Plain; a densely populated and highly polluted region spanning northern India, Pakistan, Bangladesh, and parts of Afghanistan sees brick kilns contributing 8-14% of ambient air pollution. Traditional monitoring approaches, such as field surveys and manual annotation using tools like Google Earth Pro, are time and labor-intensive. Prior ML-based efforts for automated detection have relied on costly high-resolution commercial imagery and non-public datasets, limiting reproducibility and scalability. In this work, we introduce SENTINELKILNDB, a publicly available, hand-validated benchmark of 62,671 brick kilns spanning three kiln types Fixed Chimney Bull's Trench Kiln (FCBK), Circular FCBK (CFCBK), and Zigzag kilns - annotated with oriented bounding boxes (OBBs) across 2.8 million km2 using free and globally accessible Sentinel-2 imagery. We benchmark state-of-the-art oriented object detection models and evaluate generalization across in-region, out-of-region, and super-resolution settings. SENTINELKILNDB enables rigorous evaluation of geospatial generalization and robustness for low-resolution object detection, and provides a new testbed for ML models addressing real-world environmental and remote sensing challenges at a continental scale. Datasets and code are available in SentinelKilnDB Dataset and SentinelKilnDB Benchmark, under the Creative Commons Attribution-NonCommercial 4.0 International License.

Multi-agent systems (MAS) are central to applications such as swarm robotics and traffic routing, where agents must coordinate in a decentralized manner to achieve a common objective. Stochastic Shortest Path (SSP) problems provide a natural framework for modeling decentralized control in such settings. While the problem of learning in SSP has been extensively studied in single-agent settings, the decentralized multi-agent variant remains largely unexplored. In this work, we take a step towards addressing that gap. We study decentralized multi-agent SSPs (Dec-MASSPs) under linear function approximation, where the transition dynamics and costs are represented using linear models. Applying novel symmetry-based arguments, we identify the structure of optimal policies. Our main contribution is the first regret lower bound for this setting based on the construction of hard-tolearn instances for any number of agents, n. Our regret lower bound of Ω( K), over K episodes, highlights the inherent learning difficulty in Dec-MASSPs. These insights clarify the learning complexity of decentralized control and can further guide the design of efficient learning algorithms in multi-agent systems.