Daily Archives: September 28, 2026

Authors: Pratik Agarwal

Abstract: The securities market forms the backbone of capital formation and resource allocation in a modern economy, channelling household savings into productive investment while creating avenues for wealth generation. In India, this market has undergone considerable structural and regulatory transformation over the past three decades under the oversight of the Securities and Exchange Board of India (SEBI). This paper presents an analytical study of the Indian securities market, examining its institutional structure, its evolving regulatory architecture — including the proposed Securities Markets Code, 2025 — and the unprecedented surge in retail investor participation, with demat accounts rising from roughly 3.6 crore in 2019 to over 19 crore by 2025. The study further examines the regulatory response to algorithmic and technology-driven trading, with particular reference to SEBI's 2025 framework governing retail participation in algorithmic trading. Drawing on regulatory publications and secondary market data, the paper identifies key challenges relating to investor protection, market integrity and technological risk, and concludes with observations on the likely future trajectory of India's capital markets.

DOI: http://doi.org/10.5281/zenodo.23008007

Published by: vikaspatanker

Authors: Faaiq Mushtaq

Abstract: Silent data corruptions or SDCs are computational errors that produce a wrong but plausible result with no crash, no exception and no flag of any kind. For decades the working assumption in systems design was that a tested processor either computes correctly or fails loudly. Recent measurement studies from Meta, Google and Alibaba have overturned that assumption: mercurial cores that occasionally miscompute at rates far above what fault injection studies predicted are now a documented, recurring phenomenon in hyperscale fleets. This paper argues that the drivers behind SDCs, transistor density, lower operating voltages and sheer deployment scale, are structural rather than transient and that the problem will intensify over the next decade as workloads move toward quantized, low precision AI training and toward radiation exposed space computing. Because full hardware correction is prohibitively expensive and the space of possible fault sites is too large to fully screen, this paper surveys algorithmic fault tolerance, particularly checksum based approaches descended from classical ABFT, as a lightweight, mathematically grounded complement to hardware mitigation. It closes by proposing a concrete thesis direction: characterizing when a modest, bounded performance cost is worth paying to catch a corrupted core before its errors propagate through a distributed system or a multi week training run.

Published by: vikaspatanker