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Answer: Both A and B
Event-driven computation and spiking neurons enable efficient processing of sparse, temporal data.
Answer: Cellular / Biological
Cellular computing uses biochemical reactions in cells for parallel computation. Early research stage.
Answer: True
Quantum annealing specializes in optimization via quantum tunneling. Different from gate-based universal quantum computing.
Answer: Federated
Federated Learning trains models across devices/servers holding local data, sharing only model updates.
Answer: Collateralized Lending
Collateralized lending protocols allow borrowing by locking crypto collateral, with automatic liquidation if collateral value drops.
Answer: Ultra-Low Power
Neuromorphic chips achieve ultra-low power via event-driven, sparse computation. Critical for battery-powered edge AI.
Answer: Wetware / Biological
Wetware computing uses biological neurons for computation. Early research stage with potential for ultra-low power AI.
Answer: Homomorphic
Homomorphic Encryption enables computation on ciphertext, producing encrypted results that decrypt to correct output.
Answer: Event-Driven
Event-driven architecture uses message brokers for asynchronous, decoupled service communication.
Answer: Oracle Manipulation
Oracle manipulation feeds false prices to DeFi protocols, triggering incorrect liquidations or arbitrage. Critical vulnerability requiring decentralized, robust oracles.
Answer: Real-time Sensor Processing
Neuromorphic chips excel at processing sparse, event-driven data from sensors: vision, audio, IoT. Critical for edge AI with low power budgets.
Answer: DNA / Molecular
DNA computing uses biochemical reactions for massive parallelism. Research stage with potential for solving complex combinatorial problems.
Answer: Knowledge
Zero-Knowledge Proofs enable proving model properties without revealing weights. Critical for privacy-preserving model verification.
Answer: Both A and B
Yield farming/liquidity mining rewards users with tokens for providing liquidity to DEX pools. Critical DeFi incentive mechanism with impermanent loss risks.
Answer: Both A and B
Neuromorphic chips use spiking neurons that fire only when needed, and event-driven computation that processes only changes. Critical for edge AI efficiency.
Answer: Protein / Molecular
Protein computing uses biomolecular interactions for parallel computation. Research stage with potential for ultra-low power, massive parallelism.
Answer: Differential Privacy / DP
DP-SGD adds calibrated noise to gradients during training, providing differential privacy guarantees. Critical for privacy-preserving ML training.
Answer: Horizontal
Horizontal scaling adds more instances of stateless services to handle increased load. Contrasts with vertical scaling (upgrading instance size).
Answer: All of these
DeFi risks: smart contract bugs enable fund theft, oracle manipulation feeds false prices, impermanent loss affects liquidity providers. Critical for DeFi security understanding.
Answer: Lower Energy Consumption
Neuromorphic chips achieve orders-of-magnitude better energy efficiency by mimicking brain's event-driven, sparse computation. Critical for edge AI and sustainable computing.