Portfolio Strategy of Financial Market with Regime Switching Driven by Geometric Lévy Process

The problem of a portfolio strategy for financial market with regime switching driven by geometric Lévy process is investigated in this paper. The considered financial market includes one bond and multiple stocks which has few researches up to now. A new and general Black-Scholes (B-S) model is set up, in which the interest rate of the bond, the rate of return, and the volatility of the stocks vary as the market states switching and the stock prices are driven by geometric Lévy process. For the general B-S model of the financial market, a portfolio strategy which is determined by a partial differential equation (PDE) of parabolic type is given by using Itô formula. The PDE is an extension of existing result. The solvability of the PDE is researched by making use of variables transformation. An application of the solvability of the PDE on the European options with the final data is given finally

Molecular dynamics simulation of oil displacement using surfactant in a nano-silica pore

This work was supported by National Natural Science Foundation of China (52074347) Open Access via the Elsevier agreementPeer reviewedPublisher PD

Discovering Predictable Latent Factors for Time Series Forecasting

Modern time series forecasting methods, such as Transformer and its variants, have shown strong ability in sequential data modeling. To achieve high performance, they usually rely on redundant or unexplainable structures to model complex relations between variables and tune the parameters with large-scale data. Many real-world data mining tasks, however, lack sufficient variables for relation reasoning, and therefore these methods may not properly handle such forecasting problems. With insufficient data, time series appear to be affected by many exogenous variables, and thus, the modeling becomes unstable and unpredictable. To tackle this critical issue, in this paper, we develop a novel algorithmic framework for inferring the intrinsic latent factors implied by the observable time series. The inferred factors are used to form multiple independent and predictable signal components that enable not only sparse relation reasoning for long-term efficiency but also reconstructing the future temporal data for accurate prediction. To achieve this, we introduce three characteristics, i.e., predictability, sufficiency, and identifiability, and model these characteristics via the powerful deep latent dynamics models to infer the predictable signal components. Empirical results on multiple real datasets show the efficiency of our method for different kinds of time series forecasting. The statistical analysis validates the predictability of the learned latent factors

Visualized-experimental investigation on the melting performance of PCM in 3D printed metal foam

© 2022 Elsevier Ltd. All rights reserved. This is the accepted manuscript version of an article which has been published in final form at https://doi.org/10.1016/j.tsep.2022.101298In this article, a new composite phase change material (PCM) with metal foam based on three-dimensional (3D) printed technology has been proposed to reduce the structural parameter uncertainties of metal foam. The composite PCM melting performance was visualized systematically, while both the phase and temperature fields were obtained using photographic and infrared technology. The temperature variations of PCM at different distances from the heating surface on the same level as the internal wall were also captured. Experimental results indicated that: (i) the melting rate of the composite PCM could be significantly improved by 2.5 times when adding 3D printed ALSI10MG aluminium alloy metal foam with a porosity of 0.838; (ii) the phase change interface (PCI) and the temperature contour is similar to that of the metal foam frame; (iii) the enhancement of heat conduction of metal foam is greater than its hindrance to natural convection in composite PCM; (iv) the contact thermal resistance perpendicular to the heat transfer direction could slow down the phase change rate; (v) local thermal non-equilibrium exists between the 3D printed metal foam and PCM. The metal foam fabricated by 3D printed technology has the ability of enhance the heat transfer and the phase change for composite PCM.Peer reviewe

Unsupervised Discovery of Interpretable Directions in h-space of Pre-trained Diffusion Models

We propose the first unsupervised and learning-based method to identify interpretable directions in h-space of pre-trained diffusion models. Our method is derived from an existing technique that operates on the GAN latent space. Specifically, we employ a shift control module that works on h-space of pre-trained diffusion models to manipulate a sample into a shifted version of itself, followed by a reconstructor to reproduce both the type and the strength of the manipulation. By jointly optimizing them, the model will spontaneously discover disentangled and interpretable directions. To prevent the discovery of meaningless and destructive directions, we employ a discriminator to maintain the fidelity of shifted sample. Due to the iterative generative process of diffusion models, our training requires a substantial amount of GPU VRAM to store numerous intermediate tensors for back-propagating gradient. To address this issue, we propose a general VRAM-efficient training algorithm based on gradient checkpointing technique to back-propagate any gradient through the whole generative process, with acceptable occupancy of VRAM and sacrifice of training efficiency. Compared with existing related works on diffusion models, our method inherently identifies global and scalable directions, without necessitating any other complicated procedures. Extensive experiments on various datasets demonstrate the effectiveness of our method