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Player-replaceability is a property of a blockchain protocol that ensures every step of the protocol is executed by an unpredictably random (small) set of players; this guarantees security against a fully adaptive adversary and is a crucial property in building permissionless blockchains. Forensic Support is a property of a blockchain protocol that provides the ability, with cryptographic integrity, to identify malicious parties when there is a safety violation; this provides the ability to enforce punishments for adversarial behavior and is a crucial component of incentive mechanism designs for blockchains. Player-replaceability and strong forensic support are both desirable properties, yet, none of the existing blockchain protocols have both properties. Our main result is to construct a new BFT protocol that is player-replaceable and has maximum forensic support. The key invention is the notion of a ``transition certificate'', without which we show that natural adaptations of extant BFT and longest chain protocols do not lead to the desired goal of simultaneous player-replaceability and forensic support. 

Gating is a key feature in modern neural networks including LSTMs, GRUs and sparselygated deep neural networks. The backbone of such gated networks is a mixture-of-experts layer, where several experts make regression decisions and gating controls how to weigh the decisions in an input-dependent manner. Despite having such a prominent role in both modern and classical machine learning, very little is understood about parameter recovery of mixture-of-experts since gradient descent and EM algorithms are known to be stuck in local optima in such models. In this paper, we perform a careful analysis of the optimization landscape and show that with appropriately designed loss functions, gradient descent can indeed learn the parameters of a MoE accurately. A key idea underpinning our results is the design of two distinct loss functions, one for recovering the expert parameters and another for recovering the gating parameters. We demonstrate the first sample complexity results for parameter recovery in this model for any algorithm and demonstrate significant performance gains over standard loss functions in numerical experiments 

The concept of a blockchain was invented by Satoshi Nakamoto to maintain a distributed ledger. In addition to its security, important performance measures of a blockchain protocol are its transaction throughput and confirmation latency. In a decentralized setting, these measures are limited by two underlying physical network attributes: communication capacity and speed-of-light propagation delay. In this work we introduce Prism, a new proof-of-work blockchain protocol, which can achieve 1) security against up to 50% adversarial hashing power; 2) optimal throughput up to the capacity C of the network; 3) confirmation latency for honest transactions proportional to the propagation delay D, with confirmation error probability exponentially small in the bandwidth-delay product CD; 4) eventual total ordering of all transactions. Our approach to the design of this protocol is based on deconstructing Nakamoto’s blockchain into its basic functionalities and systematically scaling up these functionalities to approach their physical limits.