Learning Green's Functions of Linear Reaction-Diffusion Equations with Application to Fast Numerical Solver

Partial differential equations are often used to model various physical phenomena, such as heat diffusion, wave propagation, fluid dynamics, elasticity, electrodynamics and image processing, and many analytic approaches or traditional numerical methods have been developed and widely used for their solutions. Inspired by rapidly growing impact of deep learning on scientific and engineering research, in this paper we propose a novel neural network, GF-Net, for learning the Green's functions of linear reaction-diffusion equations in an unsupervised fashion. The proposed method overcomes the challenges for finding the Green's functions of the equations on arbitrary domains by utilizing physics-informed approach and the symmetry of the Green's function. As a consequence, it particularly leads to an efficient way for solving the target equations under different boundary conditions and sources. We also demonstrate the effectiveness of the proposed approach by experiments in square, annular and L-shape domains

System Dynamics Modeling-based Study of Contingent Sourcing under Supply Disruptions

AbstractIn this paper, using the methodology of system dynamics modeling, we separately build two models for a supply chain under two circumstances of supply disruptions, without backup supplier, and with a contingent supplier. The retailer's total profits are also compared under these two circumstances of supply disruptions to help the decision-makers better understanding the backup purchasing strategy. The supply chain studied only involves one retailer and two independent suppliers that are referred to as major supplier and backup supplier. The paper contributes to the literature by providing a better understanding of the impacts of supply disruptions on the system performance and by shedding insights into the value of a backup supply

AerialVLN: Vision-and-Language Navigation for UAVs

Recently emerged Vision-and-Language Navigation (VLN) tasks have drawn significant attention in both computer vision and natural language processing communities. Existing VLN tasks are built for agents that navigate on the ground, either indoors or outdoors. However, many tasks require intelligent agents to carry out in the sky, such as UAV-based goods delivery, traffic/security patrol, and scenery tour, to name a few. Navigating in the sky is more complicated than on the ground because agents need to consider the flying height and more complex spatial relationship reasoning. To fill this gap and facilitate research in this field, we propose a new task named AerialVLN, which is UAV-based and towards outdoor environments. We develop a 3D simulator rendered by near-realistic pictures of 25 city-level scenarios. Our simulator supports continuous navigation, environment extension and configuration. We also proposed an extended baseline model based on the widely-used cross-modal-alignment (CMA) navigation methods. We find that there is still a significant gap between the baseline model and human performance, which suggests AerialVLN is a new challenging task. Dataset and code is available at https://github.com/AirVLN/AirVLN.Comment: Accepted by ICCV 202

Teacher Agent: A Non-Knowledge Distillation Method for Rehearsal-based Video Incremental Learning

With the rise in popularity of video-based social media, new categories of videos are constantly being generated, creating an urgent need for robust incremental learning techniques for video understanding. One of the biggest challenges in this task is catastrophic forgetting, where the network tends to forget previously learned data while learning new categories. To overcome this issue, knowledge distillation is a widely used technique for rehearsal-based video incremental learning that involves transferring important information on similarities among different categories to enhance the student model. Therefore, it is preferable to have a strong teacher model to guide the students. However, the limited performance of the network itself and the occurrence of catastrophic forgetting can result in the teacher network making inaccurate predictions for some memory exemplars, ultimately limiting the student network's performance. Based on these observations, we propose a teacher agent capable of generating stable and accurate soft labels to replace the output of the teacher model. This method circumvents the problem of knowledge misleading caused by inaccurate predictions of the teacher model and avoids the computational overhead of loading the teacher model for knowledge distillation. Extensive experiments demonstrate the advantages of our method, yielding significant performance improvements while utilizing only half the resolution of video clips in the incremental phases as input compared to recent state-of-the-art methods. Moreover, our method surpasses the performance of joint training when employing four times the number of samples in episodic memory.Comment: Under review; Do We Really Need Knowledge Distillation for Class-incremental Video Learning

Nanogenerator-based self-powered sensors for data collection

Self-powered sensors can provide energy and environmental data for applications regarding the Internet of Things, big data, and artificial intelligence. Nanogenerators provide excellent material compatibility, which also leads to a rich variety of nanogenerator-based self-powered sensors. This article reviews the development of nanogenerator-based self-powered sensors for the collection of human physiological data and external environmental data. Nanogenerator-based self-powered sensors can be designed to detect physiological data as wearable and implantable devices. Nanogenerator-based self-powered sensors are a solution for collecting data and expanding data dimensions in a future intelligent society. The future key challenges and potential solutions regarding nanogenerator-based self-powered sensors are discussed

Molecular dynamics simulations of oil recovery from dolomite slit nanopores enhanced by CO2 and N2 injection

Shale oil reservoirs are dominated by micro-and nanopores, which greatly impede the oil recovery rates. CO2 and N2 injection have proven to be highly effective approaches to enhance oil recovery from low-permeability shale reservoirs, and also represent great potential for CO2 sequestration. Therefore, a better understanding of the mechanism of shale oil recovery enhanced by CO2 and N2 is of great importance to achieve maximum shale oil productivity. In this paper, the adsorption behavior of shale oil and the mechanism of enhancing shale oil recovery by CO2 and N2 flooding in dolomite slit pores are investigated by performing nonequilibrium molecular dynamics simulations. Considering the shale oil adsorption behavior, mass density distribution is analyzed and the results indicate that a symmetric density distribution of the oil regarding the center in the slit pore along the x-axis can be obtained. The maximum density of the adsorbed layer nearest to the slit wall is 1.310 g/cm3 for C8H18 , which is about 2.0 times of that for bulk oil density in the middle area of slit pore. The interaction energy and radial distribution functions (between oil and CO2 , and between oil and N2 ) are calculated to display the displacement behavior of CO2 and N2 flooding. It is found that CO2 and N2 play different roles: CO2 has strong solubility, diffusivity and a higher interaction energy with dolomite wall, and the oil displacement efficiency of CO2 reaches 100% after 1 ns of flooding; however, during N2 flooding, the oil displacement efficiency is 87.3% after 4 ns of flooding due to the lower interaction energy between N2 and dolomite and that between N2 and oil.Cited as: Guo, H., Wang, Z., Wang, B., Zhang, Y., Meng, H., Sui H. Molecular dynamics simulations of oil recovery from dolomite slit nanopores enhanced by CO2 and N2 injection. Advances in Geo-Energy Research, 2022, 6(4): 306-313. https://doi.org/10.46690/ager.2022.04.0

FR: Folded Rationalization with a Unified Encoder

Conventional works generally employ a two-phase model in which a generator selects the most important pieces, followed by a predictor that makes predictions based on the selected pieces. However, such a two-phase model may incur the degeneration problem where the predictor overfits to the noise generated by a not yet well-trained generator and in turn, leads the generator to converge to a sub-optimal model that tends to select senseless pieces. To tackle this challenge, we propose Folded Rationalization (FR) that folds the two phases of the rationale model into one from the perspective of text semantic extraction. The key idea of FR is to employ a unified encoder between the generator and predictor, based on which FR can facilitate a better predictor by access to valuable information blocked by the generator in the traditional two-phase model and thus bring a better generator. Empirically, we show that FR improves the F1 score by up to 10.3% as compared to state-of-the-art methods.Comment: Accepted at NeurIPS 202