High-speed, in-band performance measurement instrumentation for next generation IP networks

Facilitating always-on instrumentation of Internet traffic for the purposes of performance measurement is crucial in order to enable accountability of resource usage and automated network control, management and optimisation. This has proven infeasible to date due to the lack of native measurement mechanisms that can form an integral part of the network‟s main forwarding operation. However, Internet Protocol version 6 (IPv6) specification enables the efficient encoding and processing of optional per-packet information as a native part of the network layer, and this constitutes a strong reason for IPv6 to be adopted as the ubiquitous next generation Internet transport. In this paper we present a very high-speed hardware implementation of in-line measurement, a truly native traffic instrumentation mechanism for the next generation Internet, which facilitates performance measurement of the actual data-carrying traffic at small timescales between two points in the network. This system is designed to operate as part of the routers' fast path and to incur an absolutely minimal impact on the network operation even while instrumenting traffic between the edges of very high capacity links. Our results show that the implementation can be easily accommodated by current FPGA technology, and real Internet traffic traces verify that the overhead incurred by instrumenting every packet over a 10 Gb/s operational backbone link carrying a typical workload is indeed negligible

Forecasting foreign exchange rates with adaptive neural networks using radial basis functions and particle swarm optimization

The motivation for this paper is to introduce a hybrid Neural Network architecture of Particle Swarm Optimization and Adaptive Radial Basis Function (ARBF-PSO), a time varying leverage trading strategy based on Glosten, Jagannathan and Runkle (GJR) volatility forecasts and a Neural Network fitness function for financial forecasting purposes. This is done by benchmarking the ARBF-PSO results with those of three different Neural Networks architectures, a Nearest Neighbors algorithm (k-NN), an autoregressive moving average model (ARMA), a moving average convergence/divergence model (MACD) plus a naïve strategy. More specifically, the trading and statistical performance of all models is investigated in a forecast simulation of the EUR/USD, EUR/GBP and EUR/JPY ECB exchange rate fixing time series over the period January 1999 to March 2011 using the last two years for out-of-sample testing

Modelling and Simulating the Noisy Behaviour of Near-term Quantum Computers

Noise dominates every aspect of near-term quantum computers, rendering it exceedingly difficult to carry out even small computations. In this paper we are concerned with the modelling of noise in Noisy Intermediate-Scale Quantum (NISQ) computers. We focus on three error groups that represent the main sources of noise during a computation and present quantum channels that model each source. We engineer a noise model that combines all three noise channels and simulates the evolution of the quantum computer using its calibrated error rates. We run various experiments of our model, showcasing its behaviour compared to other noise models and an IBM quantum computer. We find that our model provides a better approximation of the quantum computer's behaviour than the other models. Following this, we use a genetic algorithm to optimize the parameters used by our noise model, bringing the behaviour of the model even closer to the quantum computer. Finally, a comparison between the pre and postoptimization parameters reveals that, according to our model, certain operations can be more or less erroneous than the hardware-calibrated parameters show.Comment: 16 pages; 7 figures; changes for journal publicatio

A Comparison of Quantum Walk Implementations on NISQ Computers

This paper explores two circuit approaches for quantum walks: the first consists of generalised controlled inversions, whereas the second one effectively replaces them with rotation operations around the basis states. We show the theoretical foundation of the rotational implementation. The rotational approach nullifies the large amount of ancilla qubits required to carry out the computation when using the inverter implementation. Our results concentrate around the comparison of the two architectures in terms of structure, benefits and detriments, as well as the computational resources needed for each approach. We show that the inverters approach requires exponentially fewer gates than the rotations but almost half the number of qubits in the system. Finally, we execute a number of experiments using an IBM quantum computer. The experiments show the effects of noise in our circuits. Small two-qubit quantum walks evolve closer to our expectations, whereas for a larger number of steps or state space the evolution is severely affected by noise.Comment: Formatting changes post-acceptance (PRA); added an appendix on quantum volu

LightningNet: Distributed Graph-based Cellular Network Performance Forecasting for the Edge

The cellular network plays a pivotal role in providing Internet access, since it is the only global-scale infrastructure with ubiquitous mobility support. To manage and maintain large-scale networks, mobile network operators require timely information, or even accurate performance forecasts. In this paper, we propose LightningNet, a lightweight and distributed graph-based framework for forecasting cellular network performance, which can capture spatio-temporal dependencies that arise in the network traffic. LightningNet achieves a steady performance increase over state-of-the-art forecasting techniques, while maintaining a similar resource usage profile. Our architecture ideology also excels in the respect that it is specifically designed to support IoT and edge devices, giving us an even greater step ahead of the current state-of-the-art, as indicated by our performance experiments with NVIDIA Jetson

Prediction of atrial fibrillation development and progression: Current perspectives

Atrial fibrillation (AF) is the most common arrhythmia in clinical practice. Several conventional and novel predictors of AF development and progression (from paroxysmal to persistent and permanent types) have been reported. The most important predictor of AF progression is possibly the arrhythmia itself. The electrical, mechanical and structural remodeling determines the perpetuation of AF and the progression from paroxysmal to persistent and permanent forms. Common clinical scores such as the hypertension, age ≥ 75 years, transient ischemic attack or stroke, chronic obstructive pulmonary disease, and heart failure and the congestive heart failure, hypertension, age ≥ 75 years, diabetes mellitus, stroke/ transient ischemic attack, vascular disease, age 65-74 years, sex category scores as well as biomarkers related to inflammation may also add important information on this topic. There is now increasing evidence that even in patients with so-called lone or idiopathic AF, the arrhythmia is the manifestation of a structural atrial disease which has recently been defined and described as fibrotic atrial cardiomyopathy. Fibrosis results from a broad range of factors related to AF inducing pathologies such as cell stretch, neurohumoral activation, and oxidative stress. The extent of fibrosis as detected either by late gadolinium enhancement-magnetic resonance imaging or electroanatomic voltage mapping may guide the therapeutic approach based on the arrhythmia substrate. The knowledge of these risk factors may not only delay arrhythmia progression, but also reduce the arrhythmia burden in patients with first detected AF. The present review highlights on the conventional and novel risk factors of development and progression of AF

Meta-analysis of T peak –T end and T peak –T end /QT ratio for risk stratification in congenital long QT syndrome

Background and objectives: Congenital long QT syndrome (LQTS) predisposes affected individuals to ventricular tachycardia/fibrillation (VF/VF), potentially resulting in sudden cardiac death. The Tpeak–Tend interval and the Tpeak–Tend/QT ratio, electrocardiographic markers of dispersion of ventricular repolarization, were proposed for risk stratification but their predictive values in LQTS have been controversial. A systematic review and meta-analysis was conducted to examine the value of Tpeak–Tend intervals and Tpeak–Tend/QT ratios in predicting arrhythmic and mortality outcomes in congenital LQTS. Method: PubMed and Embase databases were searched until 9th May 2017, identifying 199 studies. Results: Five studies on long QT syndrome were included in the final meta-analysis. Tpeak–Tend intervals were longer (mean difference [MD]: 13 ms, standard error [SE]: 4 ms, P = 0.002; I2 = 34%) in congenital LQTS patients with adverse events [syncope, ventricular arrhythmias or sudden cardiac death] compared to LQTS patients without such events. By contrast, Tpeak–Tend/QT ratios were not significantly different between the two groups (MD: 0.02, SE: 0.02, P = 0.26; I2 = 0%). Conclusion: This meta-analysis showed that Tpeak–Tend interval is significant higher in individuals who are at elevated risk of adverse events in congenital LQTS, offering incremental value for risk stratification

The Tpeak – Tend interval as an electrocardiographic risk marker of arrhythmic and mortality outcomes: a systematic review and meta-analysis

Background: The Tpeak – Tend interval, an electrocardiographic marker reflecting transmural dispersion of repolarization, has been used to predict ventricular tachycardia/fibrillation (VT/VF) and sudden cardiac death (SCD) in different clinical settings. Objective: This systematic review and meta-analysis evaluated the significance of Tpeak – Tend interval in predicting arrhythmic and/or mortality endpoints. Methods: PubMed, Embase, Cochrane Library and CINAHL Plus databases were searched through 30th November 2016.Results: Of the 854 studies identified initially, 33 observational studies involving 155856 patients were included in our meta-analysis. Tpeak – Tend interval prolongation (mean cut-off: 103.3 ± 17.4 ms) was a significant predictor of the arrhythmic or mortality outcomes (odds ratio (OR): 1.14, 95% CI: 1.11 to 1.17, p < 0.001). When different end-points were analyzed, the ORs are as follows: VT/VF (1.10, 95% CI: 1.06 to 1.13, p < 0.0001), SCD (1.27, 95% CI 1.17 to 1.39, p < 0.0001), cardiovascular death (1.40, 95% CI 1.19 to 1.64, p < 0.0001), and all-cause mortality (4.56, 95% CI 0.62 to 33.68, p < 0.0001). Subgroup analysis for each disease revealed that the risk of VT/VF or death was highest for Brugada syndrome (OR: 5.68, 95% CI: 1.57 to 20.53, p < 0.01), followed by hypertension (OR: 1.52, 95% CI: 1.26 to 1.85, p < .0001), heart failure (OR: 1.07, 95% CI: 1.04 to 1.11, p < .0001) and ischemic heart disease (OR: 1.06, 95% CI: 1.02 to 1.10, p = 0.001). In the general population, a prolonged Tpeak – Tend interval also predicted arrhythmic or mortality outcomes (OR: 1.59, 95% CI: 1.21 to 2.09, p < 0.001).Conclusion: The Tpeak – Tend interval is useful risk stratification tool in different diseases and in the general population

Riociguat treatment in patients with chronic thromboembolic pulmonary hypertension: Final safety data from the EXPERT registry

Objective: The soluble guanylate cyclase stimulator riociguat is approved for the treatment of adult patients with pulmonary arterial hypertension (PAH) and inoperable or persistent/recurrent chronic thromboembolic pulmonary hypertension (CTEPH) following Phase