I've developed a LeetCode-style platform specifically for system design, which I've been passionate about for a long time. This new tool allows users to practice system design without the hassle of searching through countless resources online. Existing tools were either too expensive or no longer maintained, so I took the initiative to create a solution that fills this gap. This platform not only enhances learning but also fosters a community of enthusiasts eager to improve their skills.

A new approach to designing opto-electronic convolutional neural networks (CNNs) promises faster and more energy-efficient vision systems. By first training a standard electronic CNN and then optimizing the optical components, researchers aim to overcome the limitations of traditional methods that rely on expensive simulations.

The paper discusses submodular maximization in a federated learning context, addressing challenges posed by decentralized clients with varying quality definitions. It highlights the importance of aggregating local information to optimize representation from large datasets, showcasing potential advancements in machine learning applications.

This paper explores the connection between accelerated optimization and gradient-variation online learning, focusing on H"older smooth functions. It highlights how understanding smoothness can enhance performance in both offline and online settings, offering valuable insights for researchers and practitioners in the field.

This paper presents a novel approach to machine unlearning by framing it as a constrained optimization problem. It introduces feasible updates that enhance the model's ability to unlearn without compromising its initial performance, offering a promising direction for future research.

A recent study highlights the importance of optimizing conversions in online advertising, emphasizing the need for flexible optimization windows to accurately predict conversion events. This approach addresses the challenges posed by varying time delays between user interactions and actual conversions, ultimately helping advertisers deliver more relevant products and improve business outcomes.

A recent paper on arXiv presents a comprehensive review of simulation-based problems that involve complex hierarchical input spaces. It highlights the challenges these structures pose for data representation and optimization. The authors propose a unified framework that aims to simplify and generalize existing methods, making it easier for researchers and practitioners to tackle these intricate problems. This advancement is significant as it could enhance the efficiency and effectiveness of architecture design processes, ultimately leading to better outcomes in various engineering fields.

The article discusses the significance of tracking solutions for time-varying variational inequalities, highlighting its relevance in fields like game theory, optimization, and machine learning. It emphasizes existing research on time-varying games and optimization problems, noting that strong convexity and monotonicity can lead to effective tracking guarantees when variations are controlled.