Markovian approximation of the rough Bergomi model for Monte Carlo option pricing

The recently developed rough Bergomi (rBergomi) model is a rough fractional stochastic volatility (RFSV) model which can generate more realistic term structure of at-the-money volatility skews compared with other RFSV models. However, its non-Markovianity brings mathematical and computational challenges for model calibration and simulation. To overcome these difficulties, we show that the rBergomi model can be approximated by the Bergomi model, which has the Markovian property. Our main theoretical result is to establish and describe the affine structure of the rBergomi model. We demonstrate the efficiency and accuracy of our method by implementing a Markovian approximation algorithm based on a hybrid scheme.Comment: 20 pages, 3 figure

Structure-based virtual screening for novel p38 MAPK inhibitors and a biological evaluation

Mitogen-activated protein kinases (MAPKs) are a group of serine-threonine protein kinases that can be activated by extracellular stimuli. MAPK14 (p38α) affects major disease processes, while inhibition of p38α has been shown to have potential therapeutic effects. Many inhibitors targeting p38α have entered clinical trials but have a long development cycle and severe side effects. We developed a multi-step receptor structure-based virtual screening method to screen potential bioactive molecules from SPECS and our MCDB libraries. Compound 10 was identified as a promising p38α inhibitor that may be used in the treatment of p38αMAPK pathway-related diseases, but corollary studies are warranted

Reversible Non-Volatile Electronic Switching in a Near Room Temperature van der Waals Ferromagnet

The ability to reversibly toggle between two distinct states in a non-volatile method is important for information storage applications. Such devices have been realized for phase-change materials, which utilizes local heating methods to toggle between a crystalline and an amorphous state with distinct electrical properties. To expand such kind of switching between two topologically distinct phases requires non-volatile switching between two crystalline phases with distinct symmetries. Here we report the observation of reversible and non-volatile switching between two stable and closely-related crystal structures with remarkably distinct electronic structures in the near room temperature van der Waals ferromagnet Fe$_{5-\delta}$GeTe$_2$. From a combination of characterization techniques we show that the switching is enabled by the ordering and disordering of an Fe site vacancy that results in distinct crystalline symmetries of the two phases that can be controlled by a thermal annealing and quenching method. Furthermore, from symmetry analysis as well as first principle calculations, we provide understanding of the key distinction in the observed electronic structures of the two phases: topological nodal lines compatible with the preserved global inversion symmetry in the site-disordered phase, and flat bands resulting from quantum destructive interference on a bipartite crystaline lattice formed by the presence of the site order as well as the lifting of the topological degeneracy due to the broken inversion symmetry in the site-ordered phase. Our work not only reveals a rich variety of quantum phases emergent in the metallic van der Waals ferromagnets due to the presence of site ordering, but also demonstrates the potential of these highly tunable two-dimensional magnets for memory and spintronics applications

Markovian Approximation of the Rough Bergomi Model for Monte Carlo Option Pricing

The recently developed rough Bergomi (rBergomi) model is a rough fractional stochastic volatility (RFSV) model which can generate a more realistic term structure of at-the-money volatility skews compared with other RFSV models. However, its non-Markovianity brings mathematical and computational challenges for model calibration and simulation. To overcome these difficulties, we show that the rBergomi model can be well-approximated by the forward-variance Bergomi model with wisely chosen weights and mean-reversion speed parameters (aBergomi), which has the Markovian property. We establish an explicit bound on the L2-error between the respective kernels of these two models, which is explicitly controlled by the number of terms in the aBergomi model. We establish and describe the affine structure of the rBergomi model, and show the convergence of the affine structure of the aBergomi model to the one of the rBergomi model. We demonstrate the efficiency and accuracy of our method by implementing a classical Markovian Monte Carlo simulation scheme for the aBergomi model, which we compare to the hybrid scheme of the rBergomi model

Mmwave massive MIMO: one joint beam selection combining cuckoo search and ant colony optimization

Abstract In order to degrade the inter-user interference caused by the same beam selected for different users in mmWave massive MIMO systems, this paper proposes a joint beam selection combining cuckoo search (CS) and ant colony optimization (ACO) (referred to as CSACO). Differently from the existing interference-aware beam selection, a candidate beam set (CBS) for all users is created according to the power distribution of the beamspace channel, thereby all users can be classified into non-interfering users (NIUs) and interfering users (IUs), and NIUs will be assigned the beams with large power directly, while for IUs, the beams are selected by the CSACO; in the proposed CSACO, all beams for IUs are regarded as an optimizable individual, which is continuously evolved towards the direction of sum-rate maximization. Simulation results verify that the proposed beam selection can obtain the higher sum-rate and energy efficiency compared with the existing ones

Analysis on Credit Risk Assessment for Accounts Receivable Supply Chain Financing Based on Credit Insurance

This paper studies the model of accounts receivable supply chain financing based on credit insurance from the perspective of banks. First of all, the paper analyzes two different financing modes of the innovative model - the pledge financing mode and the factoring financing mode. Secondly, the paper explains the sources of credit risks for accounts receivable supply chain financing under credit insurance, and the necessity of using credit insurance. The sources of credit risks mainly include: the enterprises’ comprehensive strength under systemic and non-systemic risks, status of accounts receivable, supply chain operation, performance of insurance companies, and so on. In addition, based on the credit risks explained in this paper, the risk assessment system and the credit risk assessment model are built. At the end, the paper offers three suggestions for the banks’ financing risk control: bank should carefully check the policy’s exclusions clauses; bank must carefully check the authenticity of accounts receivable; bank can use dynamic monitoring on qualification checking for financing enterprises, core enterprises and insurance companies

Method of Measuring the Mismatch of Parasitic Capacitance in MEMS Accelerometer Based on Regulating Electrostatic Stiffness

For the MEMS capacitive accelerometer, parasitic capacitance is a serious problem. Its mismatch will deteriorate the performance of accelerometer. Obtaining the mismatch of the parasitic capacitance precisely is helpful for improving the performance of bias and scale. Currently, the method of measuring the mismatch is limited in the direct measuring using the instrument. This traditional method has low accuracy for it would lead in extra parasitic capacitive and have other problems. This paper presents a novel method based on the mechanism of a closed-loop accelerometer. The strongly linear relationship between the output of electric force and the square of pre-load voltage is obtained through theoretical derivation and validated by experiment. Based on this relationship, the mismatch of parasitic capacitance can be obtained precisely through regulating electrostatic stiffness without other equipment. The results can be applied in the design of decreasing the mismatch and electrical adjusting for eliminating the influence of the mismatch

Graph Signal Processing over Multilayer Networks -- Part I: Foundations and Spectrum Analysis

Signal processing over single-layer graphs has become a mainstream tool owing to its power in revealing obscure underlying structures within data signals. However, many real-life datasets and systems are characterized by more complex interactions among distinct entities, which may represent multi-level interactions that are difficult to be modeled with a single-layer graph, and can instead be captured by multiple layers of graph connections. Such multilayer/multi-level data structure can be modeled more naturally using a high-dimensional multilayer network (MLN). This work generalizes traditional graph signal processing (GSP) over multilayer networks for the analysis of multi-level signal features and their interactions. We propose a tensor-based framework of multilayer network signal processing (M-GSP) in this two-part series. Specifically, Part I introduces the fundamentals of M-GSP and studies spectrum properties of MLN Fourier space. We further describe its connections to traditional digital signal processing and GSP. Part II focuses on the major tools within the M-GSP framework for signal processing and data analysis. We provide results to demonstrate the efficacy and benefits of applying multilayer networks and the M-GSP in practical scenarios

Enhanced Neural Beamformer with Spatial Information for Target Speech Extraction

Recently, deep learning-based beamforming algorithms have shown promising performance in target speech extraction tasks. However, most systems do not fully utilize spatial information. In this paper, we propose a target speech extraction network that utilizes spatial information to enhance the performance of neural beamformer. To achieve this, we first use the UNet-TCN structure to model input features and improve the estimation accuracy of the speech pre-separation module by avoiding information loss caused by direct dimensionality reduction in other models. Furthermore, we introduce a multi-head cross-attention mechanism that enhances the neural beamformer's perception of spatial information by making full use of the spatial information received by the array. Experimental results demonstrate that our approach, which incorporates a more reasonable target mask estimation network and a spatial information-based cross-attention mechanism into the neural beamformer, effectively improves speech separation performance