January 2026

In today’s cloud-native and AI-driven enterprise landscape, system failures are no longer caused by simple outages but by complex interactions between microservices, automation, and machine-learning models. To understand how explainable AI can transform reliability engineering, we spoke with Adithya Jakkaraju who authored the IEEE International Conference on Advances in Next-Generation Computer Science (ICANCS) 2025 Best Paper, “Explainable AI for Resilient Microservices: A Transparency-Driven Approach,” which presents a practical framework for building trustworthy, auditable AI-driven resilience in large-scale systems. […]

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Spiking neural networks (SNNs) employing unsupervised learning methods inspired by neural plasticity are expected to be a new framework for artificial intelligence. In this study, we investigated the effect of multiple types of neural plasticity, such as spike-time-dependent plasticity (STDP) and synaptic scaling, on the learning in a winner-take-all (WTA) network composed of spiking neurons. We implemented a WTA network with multiple types of neural plasticity using Python. The MNIST and the Fashion-MNIST datasets were used for training […]

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For linear partial differential equations with known fundamental solutions, this work introduces a novel operator learning framework that relies exclusively on domain boundary data, including solution values and normal derivatives, rather than full-domain sampling. By integrating the previously developed Mathematical Artificial Data (MAD) method, which enforces physical consistency, all training data are synthesized directly from the fundamental solutions of the target problems, resulting in a fully data-driven pipeline without the need for external measurements or numerical simulations. We […]

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Unplanned engine failures in helicopters can lead to severe operational disruptions, safety hazards, and costly repairs. To mitigate these risks, this study compares two predictive maintenance strategies for helicopter engines: a supervised classification pipeline and an unsupervised anomaly detection approach based on autoencoders (AEs). The supervised method relies on labelled examples of both normal and faulty behaviour, while the unsupervised approach learns a model of normal operation using only healthy engine data, flagging deviations as potential faults. Both […]

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Credit risk models are a critical decision-support tool for financial institutions, yet tightening data-protection rules (e.g., GDPR, CCPA) increasingly prohibit cross-border sharing of borrower data, even as these models benefit from cross-institution learning. Traditional default prediction suffers from two limitations: binary classification ignores default timing, treating early defaulters (high loss) equivalently to late defaulters (low loss), and centralized training violates emerging regulatory constraints. We propose a Federated Survival Learning framework with Bayesian Differential Privacy (FSL-BDP) that models time-to-default […]

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Hi! I’ve tried to implement PPO for Mujoco based only on the paper and resources available at the time of publication, without looking at any existing implementations of the algorithm. I have now compared my implementation to the relevant details listed in The 37 Implementation Details of Proximal Policy Optimization, and it turns out I missed most details, see below. My question is: Were these details documented somewhere, or have they been known implicitly in the community at […]

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