Portrait of a Miner in a Landscape

Mining is one of the core elements of the proof-of-work based cryptocurrency economy. In this paper we investigate the generic landscape and hierarchy of miners on the example of Ethereum and Zcash, two blockchains that are among the top 5 in terms of USD value of created coins. Both chains used ASIC resistant proofs-of-work which favors GPU mining in order to keep mining decentralized. This however has changed with recent introduction of ASIC miners for these chains. This transition allows us to develop methods that might detect hidden ASIC mining in a chain (if it exists), and to study how the introduction of ASICs effects the decentralization of mining power. Finally, we describe how an attacker might use public blockchain information to invalidate the privacy of miners, deducing the mining hardware of individual miners and their mining rewards

Privacy and Linkability of Mining in Zcash

With the growth in popularity for cryptocurrencies the need for privacy preserving blockchains is growing as well. Zcash is such a blockchain, providing transaction privacy through zero-knowledge proofs. In this paper we analyze transaction linkability in Zcash based on the currency minting transactions (mining). Using predictable usage patterns and clustering heuristics on mining transactions an attacker can link to publicly visible addresses over 84% of the volume of the transactions that use a ZK-proof. Since majority of Zcash transactions are not yet using ZK-proofs, we show that overall 95.5% of the total number of Zcash transactions are potentially linkable to public addresses by just observing the mining activity

Automated Truncation of Differential Trails and Trail Clustering in ARX

We propose a tool for automated truncation of differential trails in ciphers using modular addition, bitwise rotation, and XOR (ARX). The tool takes as input a differential trail and produces as output a set of truncated differential trails. The set represents all possible truncations of the input trail according to certain predefined rules. A linear-time algorithm for the exact computation of the differential probability of a truncated trail that follows the truncation rules is proposed. We further describe a method to merge the set of truncated trails into a compact set of non-overlapping truncated trails with associated probability and we demonstrate the application of the tool on block cipher Speck64. We have also investigated the effect of clustering of differential trails around a fixed input trail. The best cluster that we have found for 15 rounds has probability 2^−55.03 (consisting of 389 unique output differences) which allows us to build a distinguisher using 128 times less data than the one based on just the single best trail, which has probability 2^−62. Moreover, we show examples for Speck64 where a cluster of trails around a suboptimal (in terms of probability) input trail results in higher overall probability compared to a cluster obtained around the best differential trail

Guru: Universal Reputation Module for Distributed Consensus Protocols

In this paper we describe how to couple reputation systems with distributed consensus protocols to provide high-throughput highly-scalable consensus for large peer-to-peer networks of untrusted validators. We introduce reputation module Guru, which can be laid on top of various consensus protocols such as PBFT or HoneyBadger. It ranks nodes based on the outcomes of consensus rounds run by a small committee, and adaptively selects the committee based on the current reputation. The protocol can also take external reputation ranking as input. Guru can tolerate larger threshold of malicious nodes (up to slightly above 1/2) compared to the 1/3 limit of BFT consensus algorithms

Effectiveness of Liraglutide and Lixisenatide in the Treatment of Type 2 Diabetes: Real-World Evidence from The Health Improvement Network (THIN) Database in the United Kingdom.

INTRODUCTION: The glucagon-like peptide-1 receptor agonists liraglutide and lixisenatide are effective at reducing glycated hemoglobin (HbA1c) levels in patients with type 2 diabetes mellitus (T2DM). Although liraglutide has demonstrated superior efficacy in head-to-head clinical trials, real-world evidence of comparative effectiveness is lacking. This observational study aimed to assess the effectiveness of liraglutide versus lixisenatide in UK clinical practice. METHODS: Electronic medical records from The Health Improvement Network (THIN) UK primary care database were analyzed. Patients aged ≥18 years, diagnosed with T2DM, and prescribed liraglutide or lixisenatide between 01 May 2013 and 31 December 2015 were included in the study. Adjusted linear regression models compared the difference in mean change in HbA1c, body mass index (BMI), and systolic blood pressure (SBP) after 12-month follow-up. The proportion of patients achieving glycemic control (HbA1c 1%; and weight reduction ≥3% within 12 months were determined. Cox proportional hazards modeling was used to evaluate the effect of treatment on time to achieving HbA1c and weight reduction targets. Healthcare resource use (HCRU) (GP, secondary care, hospitalizations) was compared using analysis of covariance. RESULTS: The primary outcome was assessed in 579 liraglutide and 213 lixisenatide new users. Fully adjusted linear regression indicated that liraglutide reduced HbA1c significantly more than lixisenatide (mean treatment difference -0.30; 95% CI -0.56, -0.04; p = 0.025). Compared to lixisenatide, liraglutide recipients were 2.5 times more likely to achieve HbA1c 1% HbA1c reduction (HR 1.29; p = 0.0002). BMI and SBP reductions were greater for the liraglutide group but results were not significant. HCRU was comparable between treatment groups. CONCLUSION: These results from the THIN database indicate that liraglutide treatment provided better outcomes related to glycemic control. FUNDING: Novo Nordisk

Automated Truncation of Differential Trails and Trail Clustering in ARX

We propose a tool for automated truncation of differential trails in ciphers using modular addition, bitwise rotation, and XOR (ARX). The tool takes as input a differential trail and produces as output a set of truncated differential trails. The set represents all possible truncations of the input trail according to certain predefined rules. A linear-time algorithm for the exact computation of the differential probability of a truncated trail that follows the truncation rules is proposed. We further describe a method to merge the set of truncated trails into a compact set of non-overlapping truncated trails with associated probability and we demonstrate the application of the tool on block cipher Speck64. We have also investigated the effect of clustering of differential trails around a fixed input trail. The best cluster that we have found for 15 rounds has probability 2^−55.03 (consisting of 389 unique output differences) which allows us to build a distinguisher using 128 times less data than the one based on just the single best trail, which has probability 2^−62. Moreover, we show examples for Speck64 where a cluster of trails around a suboptimal (in terms of probability) input trail results in higher overall probability compared to a cluster obtained around the best differential trail

Refining Our Understanding of the Flow Through Coronary Artery Branches; Revisiting Murray's Law in Human Epicardial Coronary Arteries

Background: Quantification of coronary blood flow is used to evaluate coronary artery disease, but our understanding of flow through branched systems is poor. Murray’s law defines coronary morphometric scaling, the relationship between flow (Q) and vessel diameter (D) and is the basis for minimum lumen area targets when intervening on bifurcation lesions. Murray’s original law (Q α D(P)) dictates that the exponent (P) is 3.0, whilst constant blood velocity throughout the system would suggest an exponent of 2.0. In human coronary arteries, the value of Murray’s exponent remains unknown. Aim: To establish the exponent in Murray’s power law relationship that best reproduces coronary blood flows (Q) and microvascular resistances (Rmicro) in a bifurcating coronary tree. Methods and Results: We screened 48 cases, and were able to evaluate inlet Q and Rmicro in 27 branched coronary arteries, taken from 20 patients, using a novel computational fluid dynamics (CFD) model which reconstructs 3D coronary anatomy from angiography and uses pressure-wire measurements to compute Q and Rmicro distribution in the main- and side-branches. Outputs were validated against invasive measurements using a Rayflow™ catheter. A Murray’s power law exponent of 2.15 produced the strongest correlation and closest agreement with inlet Q (zero bias, r = 0.47, p = 0.006) and an exponent of 2.38 produced the strongest correlation and closest agreement with Rmicro (zero bias, r = 0.66, p = 0.0001). Conclusions: The optimal power law exponents for Q and Rmicro were not 3.0, as dictated by Murray’s Law, but 2.15 and 2.38 respectively. These data will be useful in assessing patient-specific coronary physiology and tailoring revascularisation decisions

Validation of a novel numerical model to predict regionalized blood flow in the coronary arteries

Aims: Ischaemic heart disease results from insufficient coronary blood flow. Direct measurement of absolute flow (mL/min) is feasible, but has not entered routine clinical practice in most catheterization laboratories. Interventional cardiologists, therefore, rely on surrogate markers of flow. Recently, we described a computational fluid dynamics (CFD) method for predicting flow that differentiates inlet, side branch, and outlet flows during angiography. In the current study, we evaluate a new method that regionalizes flow along the length of the artery. Methods and results: Three-dimensional coronary anatomy was reconstructed from angiograms from 20 patients with chronic coronary syndrome. All flows were computed using CFD by applying the pressure gradient to the reconstructed geometry. Side branch flow was modelled as a porous wall boundary. Side branch flow magnitude was based on morphometric scaling laws with two models: a homogeneous model with flow loss along the entire arterial length; and a regionalized model with flow proportional to local taper. Flow results were validated against invasive measurements of flow by continuous infusion thermodilution (Coroventis™, Abbott). Both methods quantified flow relative to the invasive measures: homogeneous (r 0.47, P 0.006; zero bias; 95% CI -168 to +168 mL/min); regionalized method (r 0.43, P 0.013; zero bias; 95% CI -175 to +175 mL/min). Conclusion: During angiography and pressure wire assessment, coronary flow can now be regionalized and differentiated at the inlet, outlet, and side branches. The effect of epicardial disease on agreement suggests the model may be best targeted at cases with a stenosis close to side branches.</p

Quantum computing with antiferromagnetic spin clusters

We show that a wide range of spin clusters with antiferromagnetic intracluster exchange interaction allows one to define a qubit. For these spin cluster qubits, initialization, quantum gate operation, and readout are possible using the same techniques as for single spins. Quantum gate operation for the spin cluster qubit does not require control over the intracluster exchange interaction. Electric and magnetic fields necessary to effect quantum gates need only be controlled on the length scale of the spin cluster rather than the scale for a single spin. Here, we calculate the energy gap separating the logical qubit states from the next excited state and the matrix elements which determine quantum gate operation times. We discuss spin cluster qubits formed by one- and two-dimensional arrays of s=1/2 spins as well as clusters formed by spins s>1/2. We illustrate the advantages of spin cluster qubits for various suggested implementations of spin qubits and analyze the scaling of decoherence time with spin cluster size.Comment: 15 pages, 7 figures; minor change

Electron spin dynamics in quantum dots and related nanostructures due to hyperfine interaction with nuclei

We review and summarize recent theoretical and experimental work on electron spin dynamics in quantum dots and related nanostructures due to hyperfine interaction with surrounding nuclear spins. This topic is of particular interest with respect to several proposals for quantum information processing in solid state systems. Specifically, we investigate the hyperfine interaction of an electron spin confined in a quantum dot in an s-type conduction band with the nuclear spins in the dot. This interaction is proportional to the square modulus of the electron wave function at the location of each nucleus leading to an inhomogeneous coupling, i.e. nuclei in different locations are coupled with different strength. In the case of an initially fully polarized nuclear spin system an exact analytical solution for the spin dynamics can be found. For not completely polarized nuclei, approximation-free results can only be obtained numerically in sufficiently small systems. We compare these exact results with findings from several approximation strategies.Comment: 26 pages, 9 figures. Topical Review to appear in J. Phys.: Condens. Matte