Autoencoding Conditional GAN for Portfolio Allocation Diversification

Over the decades, the Markowitz framework has been used extensively in portfolio analysis though it puts too much emphasis on the analysis of the market uncertainty rather than on the trend prediction. While generative adversarial network (GAN) and conditional GAN (CGAN) have been explored to generate financial time series and extract features that can help portfolio analysis. The limitation of the CGAN framework stands in putting too much emphasis on generating series rather than keeping features that can help this generator. In this paper, we introduce an autoencoding CGAN (ACGAN) based on deep generative models that learns the internal trend of historical data while modeling market uncertainty and future trends. We evaluate the model on several real-world datasets from both the US and Europe markets, and show that the proposed ACGAN model leads to better portfolio allocation and generates series that are closer to true data compared to the existing Markowitz and CGAN approaches

Speech Dereverberation Based on Integrated Deep and Ensemble Learning Algorithm

Reverberation, which is generally caused by sound reflections from walls, ceilings, and floors, can result in severe performance degradation of acoustic applications. Due to a complicated combination of attenuation and time-delay effects, the reverberation property is difficult to characterize, and it remains a challenging task to effectively retrieve the anechoic speech signals from reverberation ones. In the present study, we proposed a novel integrated deep and ensemble learning algorithm (IDEA) for speech dereverberation. The IDEA consists of offline and online phases. In the offline phase, we train multiple dereverberation models, each aiming to precisely dereverb speech signals in a particular acoustic environment; then a unified fusion function is estimated that aims to integrate the information of multiple dereverberation models. In the online phase, an input utterance is first processed by each of the dereverberation models. The outputs of all models are integrated accordingly to generate the final anechoic signal. We evaluated the IDEA on designed acoustic environments, including both matched and mismatched conditions of the training and testing data. Experimental results confirm that the proposed IDEA outperforms single deep-neural-network-based dereverberation model with the same model architecture and training data

Metal-free sp(3) C-H functionalization: a novel approach for the syntheses of selenide ethers and thioesters from methyl arenes

A DTBP-promoted metal-free and solvent-free formation of C-Se and C-S bonds through sp(3) C-H functionalization of methyl arenes with diselenides and disulfides is described

Short-term association between black carbon exposure and cardiovascular diseases in Pakistan’s largest megacity

This study investigated the association between black carbon (BC) exposure and hospital admissions (HAs) and outpatient department/emergency room (OPD/ER) visits for cardiovascular diseases (CVD) among residents of Karachi, the largest city in Pakistan. We measured daily concentrations of BC in fine particulate matter (PM2.5) and collected records of HAs and OPD/ER visits for CVD from 2 major tertiary care hospitals serving Karachi for 6 weeks continuously during each quarter over 1 year (August 2008–August 2009). We subsequently analyzed daily counts of hospital and BC data over 0–3 lag days. Daily mean BC concentrations varied from 1 to 32 µg/m3 . Results suggest that BC concentrations are associated with CVD HAs and OPD/ER visits. However, associations were generally only observed when modeled with BC from Tibet Center, the commercial-residential site, as compared to Korangi, the industrial-residential site. Overall, low statistical significance suggests that while BC may be a valuable indicator for CVD health risks from combustion-derived particles, further evaluation of the constituents of PM2.5 and their relative contributions to CVD health impacts is necessary