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Disaggregated memory architecture decouples computing and memory resources into separate pools connected via high-speed interconnect technologies, offering substantial advantages in scalability and resource utilization. However, this architecture also poses unique challenges in designing effective index structures and concurrency protocols due to increased remote memory access overhead and its shared-everything nature. In this paper, we present DART, a lock-free two-layer hashed Adaptive Radix Tree (ART) designed to minimize remote memory access while ensuring high concurrency and crash consistency in the disaggregated memory architecture. DART incorporates a hash-based Express Skip Table at its upper layer, which reduces the round trips of remote memory access during index traversal. In the base layer, DART employs an Adaptive Hashed Layout within ART nodes, confining remote memory accesses during in-node searches to small hash buckets. By further leveraging Decoupled Metadata Organization, DART achieves lock-free atomic updates, enabling high scalability and ensuring crash consistency. Our evaluation demonstrates that DART outperforms state-of-the-art counterparts by up to 5.8X in YCSB workloads. 

Algorithmic bias in COVID-19 detection systems poses aserious threat to equitable pandemic response, asdemographicdisparities in model performance risk worsening healthoutcomes across vulnerable populations. We present anadoptedCausal Concept Bottleneck Model (C2BM) framework thatsystematically addresses fairness in multimodal COVID-19detection by learning interpretable concepts from chest CTscans and patient metadata. Our approach targets theCountry → Institution → COVID causal pathway throughprincipledinterventions, achieving substantial bias reduction: age andgender demographic parity differences decrease from 51.15%to 18.50% (64% reduction), gender disparate impact improvesfrom 0.6475 to 0.9812 (51% improvement), whilepreserving 98.45% diagnostic F1-score. Throughcomprehensive evaluation across four model variants, wedemonstrate that causal interventions enable stable andreproduciblefairness improvements without compromising clinicalutility. Our work establishes that principled causalreasoning canachieve practical fairness-accuracy trade-offs in COVID-19detection systems, providing actionable guidance forequitable healthcare AI deployment.