The Cato Corporation is an American retailer of women's fashions and accessories. The company is headquartered in Charlotte, North Carolina. As of January 2016, the company operated 1,372 stores under the names Cato, Cato Plus, It's Fashion, It's Fashion Metro and Versona..
Risk-aware portfolio optimization requires balancing return maximization with strict control of tail risk, drawdowns, and regulatory exposure constraints under non-stationary market conditions. While deep reinforcement learning has shown promise for portfolio management, existing approaches typically optimize expected returns or incorporate risk preferences through soft reward shaping, offering no guarantees on constraint satisfaction. This paper presents a constrained distributional policy gradient framework for portfolio optimization that explicitly integrates distributional return modeling with hard risk constraints. The proposed method learns the full return distribution using implicit quantile networks and enforces constraints on Conditional Value-at-Risk (CVaR), maximum drawdown, and asset exposure limits through Lagrangian-based policy gradient optimization. Risk constraints are evaluated directly over the learned return distribution, enabling differentiable and distribution-aware constraint enforcement without reliance on sample-based estimates. Empirical evaluation on real market data from 2010-2023 demonstrates that the proposed approach achieves competitive cumulative returns, improved risk-adjusted performance, and lower constraint violation rates compared to unconstrained and reward-shaped reinforcement learning baselines. The learned return distributions adapt dynamically to changing market regimes, providing interpretable risk quantification and improved robustness during periods of market stress, including the COVID-19 crash. These results highlight the effectiveness of constrained distributional policy gradient methods as a practical foundation for risk-aware portfolio optimization.
Kong Gateway is a cloud-native API management solution widely adopted for securing, managing, and routing microservice traffic. The choice of container orchestration platform such as Amazon Elastic Container Service (ECS) or Amazon Elastic Kubernetes Service (EKS) has a substantial influence on the gateway’s performance, scalability, cost, and operational complexity. This paper presents a comprehensive comparative study of Kong Gateway deployments on ECS and EKS. Real-world workloads were benchmarked across multiple performance dimensions, including throughput, latency, resource utilization, and scaling efficiency, complemented by an analysis of cost and operational overhead. The results reveal clear trade-offs between operational simplicity and architectural flexibility: ECS provides faster provisioning, predictable performance, and lower management overhead, whereas EKS achieves higher peak throughput, more consistent latency under sustained load, and enhanced observability through advanced monitoring capabilities. These findings offer empirical guidance for DevOps engineers and cloud architects in choosing optimal deployment strategies for Kong Gateway based on workload characteristics, organizational maturity, and operational objectives.
Federated learning enables hospitals to collaboratively train machine learning models without transferring sensitive patient data, but deploying multi-site FL infrastructure remains slow, complex, and error-prone due to heterogeneous systems, strict privacy regulations, and extensive security requirements. This work presents FedMed-IaC, an automated Infrastructure-as-Code framework that uses Terraform and AWS to provision secure, reproducible, and HIPAA-aligned FL environments across diverse hospital networks. The framework automates end-to-end tasks including compliance validation, secure connectivity, FL server orchestration, client onboarding, and resource scaling. In a four-hospital evaluation on chest X-ray pneumonia classification, FedMed-IaC reduces deployment time by up to 94%, eliminates 89% of configuration errors, and achieves full verification of targeted HIPAA controls. Federated models trained using the framework improve accuracy by 23% over single-hospital training and reach more than 98% of centralized performance while maintaining data locality. FedMed-IaC further demonstrates clean scalability to more than ten institutions. By encoding security and operational workflows as code, the framework significantly lowers the engineering burden of healthcare FL and enables broader adoption of privacy-preserving clinical collaboration.
