January 2026

Deep learning networks excel at classification, yet identifying minimal architectures that reliably solve a task remains challenging. We present a computational methodology for systematically exploring and analyzing the relationships among convergence, pruning, and quantization. The workflow first performs a structured design sweep across a large set of architectures, then evaluates convergence behavior, pruning sensitivity, and quantization robustness on representative models. Focusing on well-known image classification of increasing complexity, and across Deep Neural Networks, Convolutional Neural Networks, and Vision […]

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Training of multimodal foundation models is currently restricted to centralized data centers containing massive, aligned datasets (e.g., image-text pairs). However, in realistic federated environments, data is often unpaired and fragmented across disjoint nodes; one node may hold sensor data, while another holds textual logs. These datasets are strictly private and share no common samples. Current federated learning (FL) methods fail in this regime, as they assume local clients possess aligned pairs or require sharing raw feature embeddings, which […]

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Every investor talks about risk, but few can explain what truly drives it. We measure volatility, beta, or max drawdown, and assume we understand exposure. Yet these numbers are only outcomes. They tell us how much pain existed, not why it happened. Sometimes a high P/E stock crashes harder. Sometimes a low-margin business stays resilient. Correlation alone can’t answer what actually causes those reactions. That’s where most models stop, and where this story begins. This experiment tries to […]

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Modern healthcare technology demands more than incremental improvements—it requires fundamental architectural innovation that balances performance, security, and clinical usability. Brevity AI, Inc. has developed a groundbreaking clinical documentation platform that transforms how healthcare professionals interact with patient data, compressing hours of preparation and documentation into minutes via Artificial Intelligence. As Co-Founder and CTO, Purv Rakeshkumar Chauhan designed and implemented the entire software architecture, demonstrating exceptional software engineering capabilities by building a production-grade healthcare system from concept to reality. […]

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You’re not risk-averse. You’re math-averse. The reason you haven’t started that business, published that work, or made that career move isn’t because you’re scared. It’s because you’re running the wrong calculation. Most people think about risk like accountants. They see a potential move – start a podcast, launch a product, quit their job – and immediately calculate what they could lose. Time. Money. Reputation. Stability. The numbers add up to “too risky.” So they don’t move. They stay […]

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In the increasingly complex landscape of enterprise service management, the remarkable transformation led by Aravind Barla at a global Fortune 100 company stands as a compelling testament to visionary leadership and strategic technical execution. This next-generation Employee Experience Platform (EXP) implementation, serving over 100,000 employees across multiple continents, has redefined industry standards for service delivery integration and AI-driven automation while establishing new benchmarks for what’s possible in large-scale digital transformation initiatives. Inheriting a Fragmented Ecosystem When Aravind Barla […]

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Virtual Asset Service Providers (VASPs) face a fundamental tension between regulatory compliance and user privacy when detecting cross-institutional money laundering. Current approaches require either sharing sensitive transaction data or operating in isolation, leaving critical cross-chain laundering patterns undetected. We present FedGraph-VASP, a privacy-preserving federated graph learning framework that enables collaborative anti-money laundering (AML) without exposing raw user data. Our key contribution is a Boundary Embedding Exchange protocol that shares only compressed, non-invertible graph neural network representations of boundary […]

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We propose a perspective in which learning is an intrinsically dissipative process. Forgetting and regularization are not heuristic add-ons but structural requirements for adaptive systems. Drawing on information theory, thermodynamics, and information geometry, we introduce the BEDS (Bayesian Emergent Dissipative Structures) framework, modeling learning as the evolution of compressed belief states under dissipation constraints. A central contribution is the Conditional Optimality Theorem, showing that Fisher-Rao regularization measuring change via information divergence rather than Euclidean distance is the unique […]

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