Stake Shift in Major Cryptocurrencies: An Empirical Study

In the proof-of-stake (PoS) paradigm for maintaining decentralized, permissionless cryptocurrencies, Sybil attacks are prevented by basing the distribution of roles in the protocol execution on the stake distribution recorded in the ledger itself. However, for various reasons this distribution cannot be completely up-to-date, introducing a gap between the present stake distribution, which determines the parties' current incentives, and the one used by the protocol. In this paper, we investigate this issue, and empirically quantify its effects. We survey existing provably secure PoS proposals to observe that the above time gap between the two stake distributions, which we call stake distribution lag, amounts to several days for each of these protocols. Based on this, we investigate the ledgers of four major cryptocurrencies (Bitcoin, Bitcoin Cash, Litecoin and Zcash) and compute the average stake shift (the statistical distance of the two distributions) for each value of stake distribution lag between 1 and 14 days, as well as related statistics. We also empirically quantify the sublinear growth of stake shift with the length of the considered lag interval. Finally, we turn our attention to unusual stake-shift spikes in these currencies: we observe that hard forks trigger major stake shifts and that single real-world actors, mostly exchanges, account for major stake shifts in established cryptocurrency ecosystems.Comment: 20 pages, 8 figures, 2 tables, paper accepted for publication at Financial Cryptography and Data Security 2020 (FC 2020, see https://fc20.ifca.ai

The diaspora model for human migration

Migration's impact spans various social dimensions, including demography, sustainability, politics, economy and gender disparities. Yet, the decision-making process behind migrants choosing their destination remains elusive. Existing models primarily rely on population size and travel distance to explain flow fluctuations, overlooking significant population heterogeneities. Paradoxically, migrants often travel long distances and to smaller destinations if their diaspora is present in those locations. To address this gap, we propose the diaspora model of migration, incorporating intensity (the number of people moving to a country) and assortativity (the destination within the country). Our model considers only the existing diaspora sizes in the destination country, influencing the probability of migrants selecting a specific residence. Despite its simplicity, our model accurately reproduces the observed stable flow and distribution of migration in Austria (postal code level) and US metropolitan areas, yielding precise estimates of migrant inflow at various geographic scales. Given the increase in international migrations due to recent natural and societal crises, this study enlightens our understanding of migration flow heterogeneities, helping design more inclusive, integrated cities.Comment: 19 pages, 9 figure

Safety assessment of unsignalized pedestrian crossings by means of advanced movement tracking – The OBSERVE project

Pedestrians are among the most vulnerable victims of road traffic accidents. Establishing an unsignalized"br" pedestrian crossing at intersections occasionally results in a high crash risk due to the fact that many vehicle"br" drivers do not heed the legitimate right of way of pedestrians, either deliberately or because of some kind of"br" distraction, speeding or deficiencies in the traffic environment. The primary objective of the OBSERVE project"br" was to develop a novel approach for evaluating crosswalks based on data from observed pedestrian-vehicle"br" driver interactions and local site conditions. Within the project, 85 unsignalized pedestrian crossings in the cities"br" of Graz and Vienna were investigated by means of video observation. The trajectories of different road user"br" categories were analysed to obtain information on driving and walking speeds, traffic behaviour, time gaps etc."br" That information was subsequently used to model driving behaviour. For the modelling process, data from 54"br" zebra crossings were used. A beta-regression model identified the parameters ‘pedestrian crossing type’ and"br" ‘pedestrian crossing width’ having the highest influence on the stopping probability

How to Peel a Million: Validating and Expanding Bitcoin Clusters

One of the defining features of Bitcoin and the thousands of cryptocurrencies that have been derived from it is a globally visible transaction ledger. While Bitcoin uses pseudonyms as a way to hide the identity of its participants, a long line of research has demonstrated that Bitcoin is not anonymous. This has been perhaps best exemplified by the development of clustering heuristics, which have in turn given rise to the ability to track the flow of bitcoins as they are sent from one entity to another. In this paper, we design a new heuristic that is designed to track a certain type of flow, called a peel chain, that represents many transactions performed by the same entity; in doing this, we implicitly cluster these transactions and their associated pseudonyms together. We then use this heuristic to both validate and expand the results of existing clustering heuristics. We also develop a machine learning-based validation method and, using a ground-truth dataset, evaluate all our approaches and compare them with the state of the art. Ultimately, our goal is to not only enable more powerful tracking techniques but also call attention to the limits of anonymity in these systems

Quantifying the impact of risk factors at railway level crossings using accident prediction models: A cross-country study

Railway level crossings are critical elements in railway and road networks with accident occurrences resulting in"br" fatal and severe injuries. In addition to the human loss, level crossing accidents also negatively impact rail"br" transport reliability and transport speed. For safety management, specific risk factors should be identified and"br" their impact on overall safety quantified. To this end, multivariate regression equations, commonly known as"br" accident prediction models, have been used in the study. The paper describes the development of accident"br" prediction models in three Central European countries (Czech Republic, Hungary and Austria), using samples of"br" data on railway level crossings with flashing lights. The models were used to quantify the impact of several risk"br" factors. The cross-country study design enabled comparison of obtained experience and drawing conclusions for"br" further development of both road and railway network safety management

All that Glitters is not Bitcoin - Unveiling the Centralized Nature of the BTC (IP) Network

peer reviewedBlockchains are typically managed by peer-to-peer (P2P) networks providing the support and substrate to the so-called distributed ledger (DLT), a replicated, shared, and synchronized data structure, geographically spread across multiple nodes. The Bitcoin (BTC) blockchain is by far the most well-known DLT, used to record transactions among peers, based on the BTC digital currency. In this paper we focus on the network side of the BTC P2P network, analyzing its nodes from a purely network measurements-based approach. We present a BTC crawler able to discover and track the BTC P2P network through active measurements, and use it to analyze its main properties. Through the combined analysis of multiple snapshots of the BTC network as well as by using other publicly available data sources on the BTC network and DLT, we unveil the BTC P2P network, locate its active nodes, study their performance, and track the evolution of the network over the past two years. Among other relevant findings, we show that (i) the size of the BTC network has remained almost constant during the last 12 months -- since the major BTC price drop in early 2018, (ii) most of the BTC P2P network resides in US and EU countries, and (iii) despite this western network locality, most of the mining activity and corresponding revenue is controlled by major mining pools located in China. By additionally analyzing the distribution of BTC coins among independent BTC entities (i.e., single BTC addresses or groups of BTC addresses controlled by the same actor), we also conclude that (iv) BTC is very far from being the decentralized and uncontrolled system it is so much advertised to be, with only 4.5% of all the BTC entities holding about 85% of all circulating BTC coin

Integrative genomic analyses reveal an androgen-driven somatic alteration landscape in early-onset prostate cancer

SummaryEarly-onset prostate cancer (EO-PCA) represents the earliest clinical manifestation of prostate cancer. To compare the genomic alteration landscapes of EO-PCA with “classical” (elderly-onset) PCA, we performed deep sequencing-based genomics analyses in 11 tumors diagnosed at young age, and pursued comparative assessments with seven elderly-onset PCA genomes. Remarkable age-related differences in structural rearrangement (SR) formation became evident, suggesting distinct disease pathomechanisms. Whereas EO-PCAs harbored a prevalence of balanced SRs, with a specific abundance of androgen-regulated ETS gene fusions including TMPRSS2:ERG, elderly-onset PCAs displayed primarily non-androgen-associated SRs. Data from a validation cohort of > 10,000 patients showed age-dependent androgen receptor levels and a prevalence of SRs affecting androgen-regulated genes, further substantiating the activity of a characteristic “androgen-type” pathomechanism in EO-PCA