Bayesian framework for characterizing cryptocurrency market dynamics, structural dependency, and volatility using potential field

Identifying the structural dependence between the cryptocurrencies and predicting market trend are fundamental for effective portfolio management in cryptocurrency trading. In this paper, we present a unified Bayesian framework based on potential field theory and Gaussian Process to characterize the structural dependency of various cryptocurrencies, using historic price information. The following are our significant contributions: (i) Proposed a novel model for cryptocurrency price movements as a trajectory of a dynamical system governed by a time-varying non-linear potential field. (ii) Validated the existence of the non-linear potential function in cryptocurrency market through Lyapunov stability analysis. (iii) Developed a Bayesian framework for inferring the non-linear potential function from observed cryptocurrency prices. (iv) Proposed that attractors and repellers inferred from the potential field are reliable cryptocurrency market indicators, surpassing existing attributes, such as, mean, open price or close price of an observation window, in the literature. (v) Analysis of cryptocurrency market during various Bitcoin crash durations from April 2017 to November 2021, shows that attractors captured the market trend, volatility, and correlation. In addition, attractors aids explainability and visualization. (vi) The structural dependence inferred by the proposed approach was found to be consistent with results obtained using the popular wavelet coherence approach. (vii) The proposed market indicators (attractors and repellers) can be used to improve the prediction performance of state-of-art deep learning price prediction models. As, an example, we show improvement in Litecoin price prediction up to a horizon of 12 days

Incentive Based Relaying in D2D Social Networks

© 2019 IEEE. Co-operative data transmission between Device-to-Device (D2D) user terminals is a challenging task due to the selfish behavior of the D2D Users (DUs). Incentivizing the DUs can promote communication which in turn even reduce the transmission burden on the base station (eNB). In this work, we consider a scenario, in which eNB pays incentive to DUs to relay the data among its best neighbors; while the communication is regulated by wireless channel, and social influence factors. In case, DUs refrain from relaying, the eNB ought to transmit directly which increase the cost of the eNB several folds. We model this problem as a Stackelberg game, in which eNB plays the role of a leader to minimize its cost and DUs will be the followers who aim at maximizing their utility. We propose an iterative algorithm to establish the existence of equilibrium. We also prove the equilibrium of DUs subgame for a special case by relating it to a 0/1 knapsack problem. The simulation results show that the proposed algorithm has a better performance in terms of Utility and total base station cost than the conventional schemes

Characterization of PARIS LaBr3_3(Ce)-NaI(Tl) phoswich detectors upto EγE_\gamma ∼\sim 22 MeV

In order to understand the performance of the PARIS (Photon Array for the studies with Radioactive Ion and Stable beams) detector, detailed characterization of two individual phoswich (LaBr$_3$(Ce)-NaI(Tl)) elements has been carried out. The detector response is investigated over a wide range of $E_{\gamma}$ = 0.6 to 22.6 MeV using radioactive sources and employing $^{11}B(p,\gamma)$ reaction at $E_p$ = 163 keV and $E_p$ = 7.2 MeV. The linearity of energy response of the LaBr$_3$(Ce) detector is tested upto 22.6 MeV using three different voltage dividers. The data acquisition system using CAEN digitizers is set up and optimized to get the best energy and time resolution. The energy resolution of $\sim$ 2.1% at $E_\gamma$ = 22.6~MeV is measured for the configuration giving best linearity upto high energy. Time resolution of the phoswich detector is measured with a $^{60}$Co source after implementing CFD algorithm for the digitized pulses and is found to be excellent (FWHM $\sim$ 315~ps). In order to study the effect of count rate on detectors, the centroid position and width of the $E_{\gamma}$ = 835~keV peak were measured upto 220 kHz count rate. The measured efficiency data with radioactive sources are in good agreement with GEANT4 based simulations. The total energy spectrum after the add-back of energy signals in phoswich components is also presented.Comment: Accepted in JINS

Impact of noncardiac findings in patients undergoing CT coronary angiography:a substudy of the Scottish computed tomography of the heart (SCOT-HEART) trial

Objectives Noncardiac findings are common on coronary computed tomography angiography (CCTA). We assessed the clinical impact of noncardiac findings, and potential changes to surveillance scans with the application of new lung nodule guidelines. Methods This substudy of the SCOT-HEART randomized controlled trial assessed noncardiac findings identified on CCTA. Clinically significant noncardiac findings were those causing symptoms or requiring further investigation, follow-up or treatment. Lung nodule follow-up was undertaken following the 2005 Fleischner guidelines. The potential impact of the 2015 British Thoracic Society (BTS) and the 2017 Fleischner guidelines was assessed. Results CCTA was performed in 1,778 patients and noncardiac findings were identified in 677 (38%). In 173 patients (10%) the abnormal findings were clinically significant and in 55 patients (3%) the findings were the cause of symptoms. Follow-up imaging was recommended in 136 patients (7.6%) and additional clinic consultations were organized in 46 patients (2.6%). Malignancy was diagnosed in 7 patients (0.4%). Application of the new lung nodule guidelines would have reduced the number of patients undergoing a follow-up CT scan: 68 fewer with the 2015 BTS guidelines and 78 fewer with the 2017 Fleischner guidelines; none of these patients subsequently developed malignancy. Conclusions Clinically significant noncardiac findings are identified in 10% of patients undergoing CCTA. Application of new lung nodule guidelines will reduce the cost of surveillance, without the risk of missing malignancy

Adverse prognosis associated with asymmetric myocardial thickening in aortic stenosis

Aims: Asymmetric wall thickening has been described in patients with aortic stenosis. However, it remains poorly characterized and its prognostic implications are unclear. We hypothesized this pattern of adaptation is associated with advanced remodelling, left ventricular decompenzation, and a poor prognosis. Methods and results: In a prospective observational cohort study, 166 patients with aortic stenosis (age 69, 69% males, mean aortic valve area 1.0 ± 0.4 cm2) and 37 age and sex-matched healthy volunteers underwent phenotypic characterization with comprehensive clinical, imaging, and biomarker evaluation. Asymmetric wall thickening on both echocardiography and cardiovascular magnetic resonance was defined as regional wall thickening ≥ 13 mm and > 1.5-fold the thickness of the opposing myocardial segment. Although no control subject had asymmetric wall thickening, it was observed in 26% (n = 43) of patients with aortic stenosis using magnetic resonance and 17% (n = 29) using echocardiography. Despite similar demographics, co-morbidities, valve narrowing, myocardial hypertrophy, and fibrosis, patients with asymmetric wall thickening had increased cardiac troponin I and brain natriuretic peptide concentrations (both P < 0.001). Over 28 [22, 33] months of follow-up, asymmetric wall thickening was an independent predictor of aortic valve replacement (AVR) or death whether detected by magnetic resonance [hazard ratio (HR) = 2.15; 95% confidence interval (CI) 1.29-3.59; P = 0.003] or echocardiography (HR = 1.79; 95% CI 1.08-3.69; P = 0.021). Conclusion: Asymmetric wall thickening is common in aortic stenosis and is associated with increased myocardial injury, left ventricular decompenzation, and adverse events. Its presence may help identify patients likely to proceed quickly towards AVR. Clinical Trial Registration: https://clinicaltrials.gov/show/NCT01755936: NCT01755936