Co-Training Realized Volatility Prediction Model with Neural Distributional Transformation

This paper shows a novel machine learning model for realized volatility (RV) prediction using a normalizing flow, an invertible neural network. Since RV is known to be skewed and have a fat tail, previous methods transform RV into values that follow a latent distribution with an explicit shape and then apply a prediction model. However, knowing that shape is non-trivial, and the transformation result influences the prediction model. This paper proposes to jointly train the transformation and the prediction model. The training process follows a maximum-likelihood objective function that is derived from the assumption that the prediction residuals on the transformed RV time series are homogeneously Gaussian. The objective function is further approximated using an expectation-maximum algorithm. On a dataset of 100 stocks, our method significantly outperforms other methods using analytical or naive neural-network transformations.Comment: Accepted at ICAIF'2

Identifying the nature of the QCD transition in heavy-ion collisions with deep learning

In this proceeding, we review our recent work using deep convolutional neural network (CNN) to identify the nature of the QCD transition in a hybrid modeling of heavy-ion collisions. Within this hybrid model, a viscous hydrodynamic model is coupled with a hadronic cascade “after-burner”. As a binary classification setup, we employ two different types of equations of state (EoS) of the hot medium in the hydrodynamic evolution. The resulting final-state pion spectra in the transverse momentum and azimuthal angle plane are fed to the neural network as the input data in order to distinguish different EoS. To probe the effects of the fluctuations in the event-by-event spectra, we explore different scenarios for the input data and make a comparison in a systematic way. We observe a clear hierarchy in the predictive power when the network is fed with the event-by-event, cascade-coarse-grained and event-fine-averaged spectra. The carefully-trained neural network can extract high-level features from pion spectra to identify the nature of the QCD transition in a realistic simulation scenario.publishedVersio

A new empirical approach to estimate temperature effects on strut loads in braced excavation

In deep excavation designs, strut loads play a key role to ensure excavation safety. During the construction, temperature fluctuation inevitably leads to a variation in strut loads. Therefore, how to quantitatively estimate the effects of temperature on strut loads is a matter of concern. In this study, the incremental changes in wall deflection due to temperature fluctuation were assumed to be piecewise linear. Based on the beam-on-elastic-foundation (BEF) model, an empirical approach accounting for the variation in temperature-induced strut loads at all levels was established. This model was further calibrated against a reported case study for a more precise predictive performance

Gadolinium‐Doped Iron Oxide Nanoprobe as Multifunctional Bioimaging Agent and Drug Delivery System

Peer Reviewedhttp://deepblue.lib.umich.edu/bitstream/2027.42/116012/1/adfm201502868.pdfhttp://deepblue.lib.umich.edu/bitstream/2027.42/116012/2/adfm201502868-sup-0001-S1.pd

Identify truly high-risk TP53-mutated diffuse large B cell lymphoma patients and explore the underlying biological mechanisms

TP53 mutation (TP53-mut) correlates with inferior survival in many cancers, whereas its prognostic role in diffuse large B-cell lymphoma (DLBCL) is still in controversy. Therefore, more precise risk stratification needs to be further explored for TP53-mut DLBCL patients. A set of 2637 DLBCL cases from multiple cohorts, was enrolled in our analysis. Among the 2637 DLBCL patients, 14.0% patients (370/2637) had TP53-mut. Since missense mutations account for the vast majority of TP53-mut DLBCL patients, and most non-missense mutations affect the function of the P53 protein, leading to worse survival rates, we distinguished patients with missense mutations. A TP53 missense mutation risk model was constructed based on a 150-combination machine learning computational framework, demonstrating excellent performance in predicting prognosis. Further analysis revealed that patients with high-risk missense mutations are significantly associated with early progression and exhibit dysregulation of multiple immune and metabolic pathways at the transcriptional level. Additionally, the high-risk group showed an absolutely suppressed immune microenvironment. To stratify the entire cohort of TP53-mut DLBCL, we combined clinical characteristics and ultimately constructed the TP53 Prognostic Index (TP53PI) model. In summary, we identified the truly high-risk TP53-mut DLBCL patients and explained this difference at the mutation and transcriptional levels

The Role of Macrolide Antibiotics in Increasing Cardiovascular Risk

AbstractBackgroundLarge cohort studies provide conflicting evidence regarding the potential for oral macrolide antibiotics to increase the risk of serious cardiac events.ObjectivesThis study performed a meta-analysis to examine the link between macrolides and risk of sudden cardiac death (SCD) or ventricular tachyarrhythmias (VTA), cardiovascular death, and death from any cause.MethodsWe performed a search of published reports by using MEDLINE (January 1, 1966, to April 30, 2015) and EMBASE (January 1, 1980, to April 30, 2015) with no restrictions. Studies that reported relative risk (RR) estimates with 95% confidence intervals (CIs) for the associations of interest were included.ResultsThirty-three studies involving 20,779,963 participants were identified. Patients taking macrolides, compared with those who took no macrolides, experienced an increased risk of developing SCD or VTA (RR: 2.42; 95% CI: 1.61 to 3.63), SCD (RR: 2.52; 95% CI: 1.91 to 3.31), and cardiovascular death (RR: 1.31; 95% CI: 1.06 to 1.62). No association was found between macrolides use and all-cause death or any cardiovascular events. The RRs associated with SCD or VTA were 3.40 for azithromycin, 2.16 for clarithromycin, and 3.61 for erythromycin, respectively. RRs for cardiovascular death were 1.54 for azithromycin and 1.48 for clarithromycin. No association was noted between roxithromycin and adverse cardiac outcomes. Treatment with macrolides is associated with an absolute risk increase of 118.1 additional SCDs or VTA, and 38.2 additional cardiovascular deaths per 1 million treatment courses.ConclusionsAdministration of macrolide antibiotics is associated with increased risk for SCD or VTA and cardiovascular death but not increased all-cause mortality