A Vertical Channel Model of Molecular Communication based on Alcohol Molecules

The study of Molecular Communication(MC) is more and more prevalence, and channel model of MC plays an important role in the MC System. Since different propagation environment and modulation techniques produce different channel model, most of the research about MC are in horizontal direction,but in nature the communications between nano machines are in short range and some of the information transportation are in the vertical direction, such as transpiration of plants, biological pump in ocean, and blood transportation from heart to brain. Therefore, this paper we propose a vertical channel model which nano-machines communicate with each other in the vertical direction based on pure diffusion. We first propose a vertical molecular communication model, we mainly considered the gravity as the factor, though the channel model is also affected by other main factors, such as the flow of the medium, the distance between the transmitter and the receiver, the delay or sensitivity of the transmitter and the receiver. Secondly, we set up a test-bed for this vertical channel model, in order to verify the difference between the theory result and the experiment data. At last, we use the data we get from the experiment and the non-linear least squares method to get the parameters to make our channel model more accurate.Comment: 5 pages,7 figures, Accepted for presentation at BICT 2015 Special Track on Molecular Communication and Networking (MCN). arXiv admin note: text overlap with arXiv:1311.6208 by other author

Stealing Links from Graph Neural Networks

Graph data, such as chemical networks and social networks, may be deemed confidential/private because the data owner often spends lots of resources collecting the data or the data contains sensitive information, e.g., social relationships. Recently, neural networks were extended to graph data, which are known as graph neural networks (GNNs). Due to their superior performance, GNNs have many applications, such as healthcare analytics, recommender systems, and fraud detection. In this work, we propose the first attacks to steal a graph from the outputs of a GNN model that is trained on the graph. Specifically, given a black-box access to a GNN model, our attacks can infer whether there exists a link between any pair of nodes in the graph used to train the model. We call our attacks link stealing attacks. We propose a threat model to systematically characterize an adversary's background knowledge along three dimensions which in total leads to a comprehensive taxonomy of 8 different link stealing attacks. We propose multiple novel methods to realize these 8 attacks. Extensive experiments on 8 real-world datasets show that our attacks are effective at stealing links, e.g., AUC (area under the ROC curve) is above 0.95 in multiple cases. Our results indicate that the outputs of a GNN model reveal rich information about the structure of the graph used to train the model.Comment: To appear in the 30th Usenix Security Symposium, August 2021, Vancouver, B.C., Canad

Research on Capturing of Customer Requirements Based on Innovation Theory

AbstractTo exactly and effectively capture customer requirements information, a new customer requirements capturing modeling method was proposed. Based on the analysis of function requirement models of previous products and the application of technology system evolution laws of the Theory of Innovative Problem Solving (TRIZ), the customer requirements could be evolved from existing product designs, through modifying the functional requirement unit and confirming the direction of evolution design. Finally, a case study was provided to illustrate the feasibility of the proposed approach

Effect of viscosity and heterogeneity on dispersion in porous media during miscible flooding processes

In this paper, a mathematical model has been developed to quantitatively examine the effect of viscosity and heterogeneity on dispersion in porous media at the pore scale during miscible flooding processes. More specifically, the Navier-Stokes equation and advection-diffusion equation are coupled with supplementary equations to describe the solvent transport behaviour. Two-dimensional heterogeneous models are numerically developed as a function of porosity and permeability, assuming that the grain sizes satisfy normal distribution. In addition, the performance of miscible hydrocarbon gas injection in heterogeneous porous media is comprehensively evaluated. It is found that a larger aspect ratio (ratio of pore throat size) in the single non-flowing pore model results in a greater asymmetry of the concentration curve. As for single non-flowing pore models and heterogeneous models, the dispersion coefficients increase with the expansion of the non-flowing domain. Both the heterogeneity of porous media and the variable viscosity of th fluid mixture contribute to the asymmetry of the concentration curve in the heterogeneous model. A negative correlation is established between the sorting coefficients of pore throat size and the power-law coefficients. As for slug injection, the injected solvent slug size along the longitudinal direction does not effectively influence the longitudinal length of the mixing zone for a given porous medium and fluids, though the Peclet number and the porosity greatly affect the length and concentration distribution of the mixing zone.Cited as: Bai, Z., Song, K., Fu, H., Shi, Y., Liu, Y., Chen, Z. Effect of viscosity and heterogeneity on dispersion in porous media during miscible flooding processes. Advances in Geo-Energy Research, 2022, 6(6): 460-471. https://doi.org/10.46690/ager.2022.06.0

Addressing Heterogeneity in Federated Learning via Distributional Transformation

Federated learning (FL) allows multiple clients to collaboratively train a deep learning model. One major challenge of FL is when data distribution is heterogeneous, i.e., differs from one client to another. Existing personalized FL algorithms are only applicable to narrow cases, e.g., one or two data classes per client, and therefore they do not satisfactorily address FL under varying levels of data heterogeneity. In this paper, we propose a novel framework, called DisTrans, to improve FL performance (i.e., model accuracy) via train and test-time distributional transformations along with a double-input-channel model structure. DisTrans works by optimizing distributional offsets and models for each FL client to shift their data distribution, and aggregates these offsets at the FL server to further improve performance in case of distributional heterogeneity. Our evaluation on multiple benchmark datasets shows that DisTrans outperforms state-of-the-art FL methods and data augmentation methods under various settings and different degrees of client distributional heterogeneity.Comment: In the Proceedings of European Conference on Computer Vision (ECCV), 202