February 2026

Victoria, Seychelles, February 16, 2026 MEXC, the world’s fastest-growing digital asset exchange and a pioneer of true zero-fee trading, released its February Proof of Reserve report, confirming that all major assets maintained reserve ratios above 100%. BTC reserve coverage rose to 267%, demonstrating the platform’s continued commitment to transparency and user asset protection. The February report shows reserve ratios of 267% for BTC, 112% for ETH, 117% for USDT, and 124% for USDC. MEXC wallet assets total 12,003.98 […]

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Why I ditched the standard Python stack to run Qwen3-VL on an OrangePi 4 — and the hardware-specific “horror stories” that nearly broke me. Let’s be real: the AI world is currently high on “clever prompts.” Everyone is a “Prompt Engineer” until they have to build a safety system for a warehouse robot or a real-time monitor for a smart city. In the high-stakes world of Industry 4.0, a prompt is just a string. To actually do something, […]

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We present a novel property-preserving kernel-based operator learning method for incompressible flows governed by the incompressible Navier-Stokes equations. Traditional numerical solvers incur significant computational costs to respect incompressibility. Operator learning offers efficient surrogate models, but current neural operators fail to exactly enforce physical properties such as incompressibility, periodicity, and turbulence. Our method maps input functions to expansion coefficients of output functions in a property-preserving kernel basis, ensuring that predicted velocity fields analytically and simultaneously preserve the aforementioned physical […]

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When there’s authority, it’s easy to come to an agreement. Players can simply have a referee blow the whistle to signal the start of the game, and a bank can confirm balances in transactions. In crypto networks, though, there are no bank intermediaries. Players can join from anywhere in the world, bring their own computers, and join without permission. Some of the players might know one another; most of them don’t. They’re not expected to trust each other. […]

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Federated learning (FL) allows remote clients to train a global model collaboratively while protecting client privacy. Despite its privacy-preserving benefits, FL has significant drawbacks, including slow convergence, high communication cost, and non-independent-and-identically-distributed (non-IID) data. In this work, we present a novel FedAvg variation called Fractional-Order Federated Averaging (FOFedAvg), which incorporates Fractional-Order Stochastic Gradient Descent (FOSGD) to capture long-range relationships and deeper historical information. By introducing memory-aware fractional-order updates, FOFedAvg improves communication efficiency and accelerates convergence while mitigating instability […]

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Generative models have made it possible to synthesize highly realistic images, potentially providing an abundant data source for training machine learning models. Despite the advantages of these synthesizable data sources, the indiscriminate use of generated images as real images for training can even cause mode collapse due to modality discrepancies between real and synthetic domains. In this paper, we propose a novel framework for discriminative use of generated images, coined GMAIL, that explicitly treats generated images as a […]

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