The author uses extreme value theory to study time-varying idiosyncratic tail risk for a large panel of US stocks. The author demonstrates a significant performance gain by using forward-looking information extracted from implied volatilities and nonlinear ...
Despite the large body of academic work on machine learning security, little is known about the occurrence of attacks on machine learning systems in the wild. In this paper, we report on a quantitative study with 139 industrial practitioners. We analyze at ...
Correct prediction of particle transport by surface waves is crucial in many practical applications such as search and rescue or salvage operations and pollution tracking and clean-up efforts. Recent results by Deike et al. (J. Fluid Mech., vol. 829, 2017, ...
The increasing integration of renewable energy requires a good model of the existing power distribution infrastructure, represented by its admittance matrix. However, a reliable estimate may either be missing or quickly become obsolete, as distribution gri ...
Due to the autonomous characteristic and heterogeneity of the individual agents in active distribution network (ADN) with multi-microgrids (MMG), this paper proposes a fully decentralized adjustable robust operation framework achieving the coordinated oper ...
Laser powder bed fusion (LPBF) has great potential for the fabricating complex geometries with improved functionality. In combination with nickel alloys such as Hastelloy X, parts produced with this technology find usage in high-temperature applications. M ...
Automatic speech recognition and understanding (ASRU) for air traffic control (ATC) has been investigated in different ATC environments and applications. The objective of this study was to quantify the effect of ASRU support for air traffic controllers (AT ...
Semantic segmentation for remote sensing images (RSI) is critical for the Earth monitoring system. However, the covariate shift between RSI datasets under different capture conditions cannot be alleviated by directly using the unsupervised domain adaptatio ...
We consider multi-agent networks that aim at solving, cooperatively and online, distributed optimization problems under communication constraints. We propose the ACTC (Adapt-Compress-Then-Combine) diffusion strategy, which leverages differential randomized ...
This paper presents a charge controlling circuit technique to integrate a charge balancer circuitry in the output-stage of electrical neural stimulators with the aim to reduce the area and the power consumption of the charge balancing circuitry. The propos ...
Deep neural networks (DNNs) have achieved great success in image classification and recognition compared to previous methods. However, recent works have reported that DNNs are very vulnerable to adversarial examples that are intentionally generated to misl ...
The shear stiffness of headed stud connector is a critical parameter for the calculation of deflection and inter-facial shear force for steel-concrete composite structure. Thus, this study presented a promising data-driven model auto-tuning Deep Forest (AT ...
This article addresses the problem of simultaneous distributed state estimation, and control of linear systems with linear state feedback, subjected to process, and measurement noise, under the constraints of quantized, and rate-limited network data transm ...
We propose a novel unsupervised deep-learning-based algorithm for dynamic magnetic resonance imaging (MRI) reconstruction. Dynamic MRI requires rapid data acquisition for the study of moving organs such as the heart. We introduce a generalized version of t ...
Institute of Electrical and Electronics Engineers2021
