Anchal Gupta

Jonas Buchli, Brendan Tracey, Tomislav Andric et al. · deepmind

Improved low-frequency sensitivity of gravitational wave observatories would unlock study of intermediate-mass black hole mergers, binary black hole eccentricity, and provide early warnings for multi-messenger observations of binary neutron star mergers. Today's mirror stabilization control injects harmful noise, constituting a major obstacle to sensitivity improvements. We eliminated this noise through Deep Loop Shaping, a reinforcement learning method using frequency domain rewards. We proved our methodology on the LIGO Livingston Observatory (LLO). Our controller reduced control noise in the 10--30Hz band by over 30x, and up to 100x in sub-bands surpassing the design goal motivated by the quantum limit. These results highlight the potential of Deep Loop Shaping to improve current and future GW observatories, and more broadly instrumentation and control systems.

Anchal Gupta, Gleb Pappyshev, James T Kwok

"Double, double toil and trouble; Fire burn and cauldron bubble." As Shakespeare's witches foretold chaos through cryptic prophecies, modern capital markets grapple with systemic risks concealed by opaque AI systems. According to IMF, the August 5, 2024, plunge in Japanese and U.S. equities can be linked to algorithmic trading yet ab-sent from existing AI incidents database exemplifies this transparency crisis. Current AI incident databases, reliant on crowdsourcing or news scraping, systematically over-look capital market anomalies, particularly in algorithmic and high-frequency trading. We address this critical gap by proposing a regulatory-grade global database that elegantly synthesises post-trade reporting frameworks with proven incident documentation models from healthcare and aviation. Our framework's temporal data omission technique masking timestamps while preserving percent-age-based metrics enables sophisticated cross-jurisdictional analysis of emerging risks while safeguarding confidential business information. Synthetic data validation (modelled after real life published incidents , sentiments, data) reveals compelling pat-terns: systemic risks transcending geographical boundaries, market manipulation clusters distinctly identifiable via K-means algorithms, and AI system typology exerting significantly greater influence on trading behaviour than geographical location, This tripartite solution empowers regulators with unprecedented cross-jurisdictional oversight, financial institutions with seamless compliance integration, and investors with critical visibility into previously obscured AI-driven vulnerabilities. We call for immediate action to strengthen risk management and foster resilience in AI-driven financial markets against the volatile "cauldron" of AI-driven systemic risks., promoting global financial stability through enhanced transparency and coordinated oversight.