Collective Investment Algorithms (CoinAlgs) are increasingly popular systems that deploy shared trading strategies for investor communities. Their goal is to democratize sophisticated – often AI-based – investing tools. We identify and demonstrate a fundamental profitability-fairness tradeoff in CoinAlgs that we call the CoinAlg Bind: CoinAlgs cannot ensure economic fairness without losing profit to arbitrage. We present a formal model of CoinAlgs, with definitions of privacy (incomplete algorithm disclosure) and economic fairness (value extraction by an adversarial insider). We prove two complementary results that together demonstrate the CoinAlg Bind. First, privacy in a CoinAlg is a precondition for insider attacks on economic fairness. Conversely, in a game-theoretic model, lack of privacy, i.e., transparency, enables arbitrageurs to erode the profitability of a CoinAlg. Using data from Uniswap, a decentralized exchange, we empirically study both sides of the CoinAlg Bind. We quantify the impact of arbitrage against transparent CoinAlgs. We show the risks posed by a private CoinAlg: Even low-bandwidth covert-channel information leakage enables unfair value extraction.
Distributed Acoustic Sensing (DAS), a photonic technology that converts a fibre-optic cable into a long (tens of kilometres) high-linear-density (every few metres) array of seismo-acoustic sensors, can provide high-density, high-resolution strain measurements along the entire cable. The potential of such a distributed measurement has gained increasing attention in the seismology community for a wide range of applications. It has been shown that DAS has a subwavelength sensitivity to heterogeneities near the fibre-optic cable. This sensitivity is linked to the fact that the DAS measures deformation, as opposed to the displacements that seismometers measure. However, this sensitivity can create difficulties for many DAS applications, such as source location or distant imaging. Regardless, it can be advantageous in obtaining information about the subsurface near the cable. Here we present a method to locate small heterogeneities near the fibre-optic cable by inverting an indicator of the small-scale heterogeneities: the homogenized first-order corrector. We show that this first-order corrector can be used to locate heterogeneities near the fibre-optic cable at the gauge length precision, independent of the wavelength.
Conèixer com és i com funciona el cos humà és un objectiu clau de les ciències a l'ESO que ha de permetre, entre d’altres, aconseguir ciutadans amb el coneixement bàsic sobre el funcionament del seu cos i responsables del manteniment de la seva salut, alhora que ciutadans lliures per decidir ajudar altres persones en el futur, per exemple en la donació d’òrgans o de sang. La problemàtica d’un adolescent protagonista d’una sèrie televisiva serà una situació d’aprenentatge en la que es treballarà com és i com funciona el cos humà: des d’algunes molècules rellevants implicades en el seu funcionament, fins als òrgans i sistemes. Es dona molta rellevància a processos com la difusió de molècules a través de les membranes naturals, que permet connectar òrgans i mantenir l’equilibri homeostàtic de l’organisme, d’importància vital per evitar malalties.
Inherent in the world of cryptocurrency systems and their security models is the notion that private keys, and thus assets, are controlled by individuals or individual entities. We present Liquefaction, a wallet platform that demonstrates the dangerous fragility of this foundational assumption by systemically breaking it. Liquefaction uses trusted execution environments (TEEs) to encumber private keys, i.e., attach rich, multi-user policies to their use. In this way, it enables the cryptocurrency credentials and assets of a single end-user address to be freely rented, shared, or pooled. It accomplishes these things privately, with no direct on-chain traces. Liquefaction demonstrates the sweeping consequences of TEE-based key encumbrance for the cryptocurrency landscape. Liquefaction can undermine the security and economic models of many applications and resources, such as locked tokens, DAO voting, airdrops, loyalty points, soulbound tokens, and quadratic voting. It can do so with no on-chain and minimal off-chain visibility. Conversely, we also discuss beneficial applications of Liquefaction, such as privacy-preserving, cost-efficient DAOs and a countermeasure to dusting attacks. Importantly, we describe an existing TEE-based tool that applications can use as a countermeasure to Liquefaction. Our work prompts a wholesale rethinking of existing models and enforcement of key and asset ownership in the cryptocurrency ecosystem.
Decentralized Autonomous Organizations (DAOs) use smart contracts to foster communities working toward common goals. Existing definitions of decentralization, however—the 'D' in DAO—fall short of capturing the key properties characteristic of diverse and equitable participation. This work proposes a new framework for measuring DAO decentralization called Voting-Bloc Entropy (VBE, pronounced "vibe"). VBE is based on the idea that voters with closely aligned interests act as a centralizing force and should be modeled as such. VBE formalizes this notion by measuring the similarity of participants' utility functions across a set of voting rounds. Unlike prior, ad hoc definitions of decentralization, VBE derives from first principles: We introduce a simple (yet powerful) reinforcement learning-based conceptual model for voting, that in turn implies VBE. We first show VBE's utility as a theoretical tool. We prove a number of results about the (de)centralizing effects of vote delegation, proposal bundling, bribery, etc. that are overlooked in previous notions of DAO decentralization. Our results lead to practical suggestions for enhancing DAO decentralization. We also show how VBE can be used empirically by presenting measurement studies and VBE-based governance experiments. We make the tools we developed for these results available to the community in the form of open-source artifacts in order to facilitate future study of DAO decentralization.
