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Daniel Perez

Publications and source records attributed to Daniel Perez.

At least 19 recordsLinked to original sources
arXiv2024

Design and Implementation of Smart Infrastructures and Connected Vehicles in A Mini-city Platform

This paper presents a 1/10th scale mini-city platform used as a testing bed for evaluating autonomous and connected vehicles. Using the mini-city platform, we can evaluate different driving scenarios including human-driven and autonomous driving. We provide a unique, visual feature-rich environment for evaluating computer vision methods. The conducted experiments utilize onboard sensors mounted on a robotic platform we built, allowing them to navigate in a controlled real-world urban environment. The designed city is occupied by cars, stop signs, a variety of residential and business buildings, and complex intersections mimicking an urban area. Furthermore, We have designed an intelligent infrastructure at one of the intersections in the city which helps safer and more efficient navigation in the presence of multiple cars and pedestrians. We have used the mini-city platform for the analysis of three different applications: city mapping, depth estimation in challenging occluded environments, and smart infrastructure for connected vehicles. Our smart infrastructure is among the first to develop and evaluate Vehicle-to-Infrastructure (V2I) communication at intersections. The intersection-related result shows how inaccuracy in perception, including mapping and localization, can affect safety. The proposed mini-city platform can be considered as a baseline environment for developing research and education in intelligent transportation systems.

cs.RO↗
arXiv2023

Auto.gov: Learning-based Governance for Decentralized Finance (DeFi)

Decentralized finance (DeFi) is an integral component of the blockchain ecosystem, enabling a range of financial activities through smart-contract-based protocols. Traditional DeFi governance typically involves manual parameter adjustments by protocol teams or token holder votes, and is thus prone to human bias and financial risks, undermining the system's integrity and security. While existing efforts aim to establish more adaptive parameter adjustment schemes, there remains a need for a governance model that is both more efficient and resilient to significant market manipulations. In this paper, we introduce "Auto$.$gov", a learning-based governance framework that employs a deep Qnetwork (DQN) reinforcement learning (RL) strategy to perform semi-automated, data-driven parameter adjustments. We create a DeFi environment with an encoded action-state space akin to the Aave lending protocol for simulation and testing purposes, where Auto$.$gov has demonstrated the capability to retain funds that would have otherwise been lost to price oracle attacks. In tests with real-world data, Auto$.$gov outperforms the benchmark approaches by at least 14% and the static baseline model by tenfold, in terms of the preset performance metric--protocol profitability. Overall, the comprehensive evaluations confirm that Auto$.$gov is more efficient and effective than traditional governance methods, thereby enhancing the security, profitability, and ultimately, the sustainability of DeFi protocols.

q-fin.RM↗
arXiv2022

On the persistent homology of almost surely $C^0$ stochastic processes

This paper investigates the propreties of the persistence diagrams stemming from almost surely continuous random processes on $[0,t]$. We focus our study on two variables which together characterize the barcode : the number of points of the persistence diagram inside a rectangle $]\!-\!\infty,x]\times [x+\varepsilon,\infty[$, $N^{x,x+\varepsilon}$ and the number of bars of length $\geq \varepsilon$, $N^\varepsilon$. For processes with the strong Markov property, we show both of these variables admit a moment generating function and in particular moments of every order. Switching our attention to semimartingales, we show the asymptotic behaviour of $N^\varepsilon$ and $N^{x,x+\varepsilon}$ as $\varepsilon \to 0$ and of $N^\varepsilon$ as $\varepsilon \to \infty$. Finally, we study the repercussions of the classical stability theorem of barcodes and illustrate our results with some examples, most notably Brownian motion and empirical functions converging to the Brownian bridge.

math.PR↗
arXiv2022

Introducing emotions in the reasoning cycle ofnormative aware agents

Human relationships are complex processes that often involve following certain rules that regulate interactions and/or expected outcomes. These rules may be imposed by an authority or established by society. In multi-agent systems, normative systems have extensively addressed aspects such as norm synthesis, norm conflict detection, as well as norm emergence. However, if human behaviour is to be adequately simulated, not only normative aspects but also emotional aspects have to be taken into account. In this paper, we propose a Jason agent architecture that incorporates norms and emotions in its reasoning process to determine which plan (actions) to execute. The proposal is evaluated through a scenario based on a social network, which allows us to analyse the benefits of using emotional normative agents to achieve simulations closer to real human world.

cs.MA↗
arXiv2022

SoK: Decentralized Finance (DeFi)

Decentralized Finance (DeFi), a blockchain powered peer-to-peer financial system, is mushrooming. Two years ago the total value locked in DeFi systems was approximately 700m USD, now, as of April 2022, it stands at around 150bn USD. The frenetic evolution of the ecosystem has created challenges in understanding the basic principles of these systems and their security risks. In this Systematization of Knowledge (SoK) we delineate the DeFi ecosystem along the following axes: its primitives, its operational protocol types and its security. We provide a distinction between technical security, which has a healthy literature, and economic security, which is largely unexplored, connecting the latter with new models and thereby synthesizing insights from computer science, economics and finance. Finally, we outline the open research challenges in the ecosystem across these security types.

cs.CR↗
arXiv2022

On $C^0$-persistent homology and trees

In this paper we give a metric construction of a tree which correctly identifies connected components of superlevel sets of $\mathbb{R}$-valued continuous functions $f$ on $X$ and show that it is possible to retrieve the $H_0$-persistent diagram from this tree. We revisit the notion of homological dimension previously introduced by Schweinhart and give some bounds for the latter in terms of the upper-box dimension of $X$, thereby partially answering a question of the same author. We prove a quantitative version of the Wasserstein stability theorem valid for regular enough $X$ and $α$-Hölder functions and discuss some applications of this theory to random fields and the topology of their superlevel sets.

math.AT↗
arXiv2022

$ζ$-functions and the topology of superlevel sets of stochastic processes

We describe the topology of superlevel sets of ($α$-stable) Lévy processes X by introducing so-called stochastic $ζ$-functions, which are defined in terms of the widely used $\text{Pers}_p$-functional in the theory of persistence modules. The latter share many of the properties commonly attributed to $ζ$-functions in analytic number theory, among others, we show that for $α$-stable processes, these (tail) $ζ$-functions always admit a meromorphic extension to the entire complex plane with a single pole at $α$, of known residue and that the analytic properties of these $ζ$-functions are related to the asymptotic expansion of a dual variable, which counts the number of variations of X of size $\geq \varepsilon$. Using these results, we devise a new statistical parameter test using the topology of these superlevel sets. We further develop an analogous theory, whereby we consider the dual variable to be the number of points in the persistence diagram inside the rectangle $]\infty, x]\times[x+\varepsilon, \infty[$.

math.PR↗
arXiv2022

Dissimilar Redundancy in DeFi

The meteoric rise of Decentralized Finance (DeFi) has been accompanied by a plethora of frequent and often financially devastating attacks on its protocols There have been over 70 exploits of DeFi protocols, with the total of lost funds amounting to approximately 1.5bn USD. In this paper, we introduce a new approach to minimizing the frequency and severity of such attacks: dissimilar redundancy for smart contracts. In a nutshell, the idea is to implement a program logic more than once, ideally using different programming languages. Then, for each implementation, the results should match before allowing the state of the blockchain to change. This is inspired by and has clear parallels to the field of avionics, where on account of the safety-critical environment, flight control systems typically feature multiple redundant implementations. We argue that the high financial stakes in DeFi protocols merit a conceptually similar approach, and we provide a novel algorithm for implementing dissimilar redundancy for smart contracts.

cs.CR↗
arXiv2021

Liquidations: DeFi on a Knife-edge

The trustless nature of permissionless blockchains renders overcollateralization a key safety component relied upon by decentralized finance (DeFi) protocols. Nonetheless, factors such as price volatility may undermine this mechanism. In order to protect protocols from suffering losses, undercollateralized positions can be liquidated. In this paper, we present the first in-depth empirical analysis of liquidations on protocols for loanable funds (PLFs). We examine Compound, one of the most widely used PLFs, for a period starting from its conception to September 2020. We analyze participants' behavior and risk-appetite in particular, to elucidate recent developments in the dynamics of the protocol. Furthermore, we assess how this has changed with a modification in Compound's incentive structure and show that variations of only 3% in an asset's dollar price can result in over 10m USD becoming liquidable. To further understand the implications of this, we investigate the efficiency of liquidators. We find that liquidators' efficiency has improved significantly over time, with currently over 70% of liquidable positions being immediately liquidated. Lastly, we provide a discussion on how a false sense of security fostered by a misconception of the stability of non-custodial stablecoins, increases the overall liquidation risk faced by Compound participants.

q-fin.GN↗
arXiv2021

Data-driven analysis of central bank digital currency (CBDC) projects drivers

In this paper, we use a variety of machine learning methods to quantify the extent to which economic and technological factors are predictive of the progression of Central Bank Digital Currencies (CBDC) within a country, using as our measure of this progression the CBDC project index (CBDCPI). We find that a financial development index is the most important feature for our model, followed by the GDP per capita and an index of the voice and accountability of the country's population. Our results are consistent with previous qualitative research which finds that countries with a high degree of financial development or digital infrastructure have more developed CBDC projects. Further, we obtain robust results when predicting the CBDCPI at different points in time.

cs.LG↗
arXiv2021

Step on the Gas? A Better Approach for Recommending the Ethereum Gas Price

In the Ethereum network, miners are incentivized to include transactions in a block depending on the gas price specified by the sender. The sender of a transaction therefore faces a trade-off between timely inclusion and cost of his transaction. Existing recommendation mechanisms aggregate recent gas price data on a per-block basis to suggest a gas price. We perform an empirical analysis of historic block data to motivate the use of a predictive model for gas price recommendation. Subsequently, we propose a novel mechanism that combines a deep-learning based price forecasting model as well as an algorithm parameterized by a user-specific urgency value to recommend gas prices. In a comprehensive evaluation on real-world data, we show that our approach results on average in costs savings of more than 50% while only incurring an inclusion delay of 1.3 blocks, when compared to the gas price recommendation mechanism of the most widely used Ethereum client.

cs.DC↗
arXiv2020

Neural language models for text classification in evidence-based medicine

The COVID-19 has brought about a significant challenge to the whole of humanity, but with a special burden upon the medical community. Clinicians must keep updated continuously about symptoms, diagnoses, and effectiveness of emergent treatments under a never-ending flood of scientific literature. In this context, the role of evidence-based medicine (EBM) for curating the most substantial evidence to support public health and clinical practice turns essential but is being challenged as never before due to the high volume of research articles published and pre-prints posted daily. Artificial Intelligence can have a crucial role in this situation. In this article, we report the results of an applied research project to classify scientific articles to support Epistemonikos, one of the most active foundations worldwide conducting EBM. We test several methods, and the best one, based on the XLNet neural language model, improves the current approach by 93\% on average F1-score, saving valuable time from physicians who volunteer to curate COVID-19 research articles manually.

cs.CL↗
arXiv2020

Smart Contract Vulnerabilities: Vulnerable Does Not Imply Exploited

In recent years, we have seen a great deal of both academic and practical interest in the topic of vulnerabilities in smart contracts, particularly those developed for the Ethereum blockchain. While most of the work has focused on detecting *vulnerable* contracts, in this paper, we focus on finding how many of these vulnerable contracts have actually been *exploited*. We survey the 23,327 vulnerable contracts reported by six recent academic projects and find that, despite the amounts at stake, only 1.98% of them have been exploited since deployment. This corresponds to at most 8,487 ETH (~1.7 million USD), or only 0.27% of the 3 million ETH (600 million USD) at stake. We explain these results by demonstrating that the funds are very concentrated in a small number of contracts which are *not exploitable* in practice.

cs.CR↗
arXiv2020

DeFi Protocols for Loanable Funds: Interest Rates, Liquidity and Market Efficiency

We coin the term *Protocols for Loanable Funds (PLFs)* to refer to protocols which establish distributed ledger-based markets for loanable funds. PLFs are emerging as one of the main applications within Decentralized Finance (DeFi), and use smart contract code to facilitate the intermediation of loanable funds. In doing so, these protocols allow agents to borrow and save programmatically. Within these protocols, interest rate mechanisms seek to equilibrate the supply and demand for funds. In this paper, we review the methodologies used to set interest rates on three prominent DeFi PLFs, namely Compound, Aave and dYdX. We provide an empirical examination of how these interest rate rules have behaved since their inception in response to differing degrees of liquidity. We then investigate the market efficiency and inter-connectedness between multiple protocols, examining first whether Uncovered Interest Parity holds within a particular protocol and second whether the interest rates for a particular token market show dependence across protocols, developing a Vector Error Correction Model for the dynamics.

q-fin.GN↗
arXiv2020

Revisiting Transactional Statistics of High-scalability Blockchains

Scalability has been a bottleneck for major blockchains such as Bitcoin and Ethereum. Despite the significantly improved scalability claimed by several high--profile blockchain projects, there has been little effort to understand how their transactional throughput is being used. In this paper, we examine recent network traffic of three major high-scalability blockchains--EOSIO, Tezos and XRP Ledger (XRPL)--over a period of seven months. Our analysis reveals that only a small fraction of the transactions are used for value transfer purposes. In particular, 96% of the transactions on EOSIO were triggered by the airdrop of a currently valueless token; on Tezos, 76% of throughput was used for maintaining consensus; and over 94% of transactions on XRPL carried no economic value. We also identify a persisting airdrop on EOSIO as a DoS attack and detect a two-month-long spam attack on XRPL. The paper explores the different designs of the three blockchains and sheds light on how they could shape user behavior.

cs.CR↗
arXiv2020

Fast-Fourier-Forecasting Resource Utilisation in Distributed Systems

Distributed computing systems often consist of hundreds of nodes, executing tasks with different resource requirements. Efficient resource provisioning and task scheduling in such systems are non-trivial and require close monitoring and accurate forecasting of the state of the system, specifically resource utilisation at its constituent machines. Two challenges present themselves towards these objectives. First, collecting monitoring data entails substantial communication overhead. This overhead can be prohibitively high, especially in networks where bandwidth is limited. Second, forecasting models to predict resource utilisation should be accurate and need to exhibit high inference speed. Mission critical scheduling and resource allocation algorithms use these predictions and rely on their immediate availability. To address the first challenge, we present a communication-efficient data collection mechanism. Resource utilisation data is collected at the individual machines in the system and transmitted to a central controller in batches. Each batch is processed by an adaptive data-reduction algorithm based on Fourier transforms and truncation in the frequency domain. We show that the proposed mechanism leads to a significant reduction in communication overhead while incurring only minimal error and adhering to accuracy guarantees. To address the second challenge, we propose a deep learning architecture using complex Gated Recurrent Units to forecast resource utilisation. This architecture is directly integrated with the above data collection mechanism to improve inference speed of our forecasting model. Using two real-world datasets, we demonstrate the effectiveness of our approach, both in terms of forecasting accuracy and inference speed. Our approach resolves challenges encountered in resource provisioning frameworks and can be applied to other forecasting problems.

cs.DC↗
arXiv2020

The Decentralized Financial Crisis

The Global Financial Crisis of 2008, caused by the accumulation of excessive financial risk, inspired Satoshi Nakamoto to create Bitcoin. Now, more than ten years later, Decentralized Finance (DeFi), a peer-to-peer financial paradigm which leverages blockchain-based smart contracts to ensure its integrity and security, contains over 702m USD of capital as of April 15th, 2020. As this ecosystem develops, it is at risk of the very sort of financial meltdown it is supposed to be preventing. In this paper we explore how design weaknesses and price fluctuations in DeFi protocols could lead to a DeFi crisis. We focus on DeFi lending protocols as they currently constitute most of the DeFi ecosystem with a 76% market share by capital as of April 15th, 2020. First, we demonstrate the feasibility of attacking Maker's governance design to take full control of the protocol, the largest DeFi protocol by market share, which would have allowed the theft of 0.5bn USD of collateral and the minting of an unlimited supply of DAI tokens. In doing so, we present a novel strategy utilizing so-called flash loans that would have in principle allowed the execution of the governance attack in just two transactions and without the need to lock any assets. Approximately two weeks after we disclosed the attack details, Maker modified the governance parameters mitigating the attack vectors. Second, we turn to a central component of financial risk in DeFi lending protocols. Inspired by stress-testing as performed by central banks, we develop a stress-testing framework for a stylized DeFi lending protocol, focusing our attention on the impact of a drying-up of liquidity on protocol solvency. Based on our parameters, we find that with sufficiently illiquidity a lending protocol with a total debt of 400m USD could become undercollateralized within 19 days.

cs.CR↗