Cellular non-nonlinear network model of microbial fuel cell

A cellular non-linear network (CNN) is a uniform regular array of locally connected continuous-state machines, or nodes, which update their states simultaneously in discrete time. A microbial fuel cell (MFC) is an electro-chemical reactor using the metabolism of bacteria to drive an electrical current. In a CNN model of the MFC, each node takes a vector of states which represent geometrical characteristics of the cell, like the electrodes or impermeable borders, and quantify measurable properties like bacterial population, charges produced and hydrogen ions concentrations. The model allows the study of integral reaction of the MFC, including temporal outputs, to spatial disturbances of the bacterial population and supply of nutrients. The model can also be used to evaluate inhomogeneous configurations of bacterial populations attached on the electrode biofilms

Deep Learning on Graphs: A Survey

Deep learning has been shown to be successful in a number of domains, ranging from acoustics, images, to natural language processing. However, applying deep learning to the ubiquitous graph data is non-trivial because of the unique characteristics of graphs. Recently, substantial research efforts have been devoted to applying deep learning methods to graphs, resulting in beneficial advances in graph analysis techniques. In this survey, we comprehensively review the different types of deep learning methods on graphs. We divide the existing methods into five categories based on their model architectures and training strategies: graph recurrent neural networks, graph convolutional networks, graph autoencoders, graph reinforcement learning, and graph adversarial methods. We then provide a comprehensive overview of these methods in a systematic manner mainly by following their development history. We also analyze the differences and compositions of different methods. Finally, we briefly outline the applications in which they have been used and discuss potential future research directions.Comment: Accepted by Transactions on Knowledge and Data Engineering. 24 pages, 11 figure

Deep Learning for Real-Time Crime Forecasting and its Ternarization

Real-time crime forecasting is important. However, accurate prediction of when and where the next crime will happen is difficult. No known physical model provides a reasonable approximation to such a complex system. Historical crime data are sparse in both space and time and the signal of interests is weak. In this work, we first present a proper representation of crime data. We then adapt the spatial temporal residual network on the well represented data to predict the distribution of crime in Los Angeles at the scale of hours in neighborhood-sized parcels. These experiments as well as comparisons with several existing approaches to prediction demonstrate the superiority of the proposed model in terms of accuracy. Finally, we present a ternarization technique to address the resource consumption issue for its deployment in real world. This work is an extension of our short conference proceeding paper [Wang et al, Arxiv 1707.03340].Comment: 14 pages, 7 figure

IMEXnet: A Forward Stable Deep Neural Network

Deep convolutional neural networks have revolutionized many machine learning and computer vision tasks, however, some remaining key challenges limit their wider use. These challenges include improving the network's robustness to perturbations of the input image and the limited ``field of view'' of convolution operators. We introduce the IMEXnet that addresses these challenges by adapting semi-implicit methods for partial differential equations. Compared to similar explicit networks, such as residual networks, our network is more stable, which has recently shown to reduce the sensitivity to small changes in the input features and improve generalization. The addition of an implicit step connects all pixels in each channel of the image and therefore addresses the field of view problem while still being comparable to standard convolutions in terms of the number of parameters and computational complexity. We also present a new dataset for semantic segmentation and demonstrate the effectiveness of our architecture using the NYU Depth dataset

Unrolled Optimization with Deep Priors

A broad class of problems at the core of computational imaging, sensing, and low-level computer vision reduces to the inverse problem of extracting latent images that follow a prior distribution, from measurements taken under a known physical image formation model. Traditionally, hand-crafted priors along with iterative optimization methods have been used to solve such problems. In this paper we present unrolled optimization with deep priors, a principled framework for infusing knowledge of the image formation into deep networks that solve inverse problems in imaging, inspired by classical iterative methods. We show that instances of the framework outperform the state-of-the-art by a substantial margin for a wide variety of imaging problems, such as denoising, deblurring, and compressed sensing magnetic resonance imaging (MRI). Moreover, we conduct experiments that explain how the framework is best used and why it outperforms previous methods.Comment: First two authors contributed equall

Artificial Intelligence for Science in Quantum, Atomistic, and Continuum Systems

Advances in artificial intelligence (AI) are fueling a new paradigm of discoveries in natural sciences. Today, AI has started to advance natural sciences by improving, accelerating, and enabling our understanding of natural phenomena at a wide range of spatial and temporal scales, giving rise to a new area of research known as AI for science (AI4Science). Being an emerging research paradigm, AI4Science is unique in that it is an enormous and highly interdisciplinary area. Thus, a unified and technical treatment of this field is needed yet challenging. This work aims to provide a technically thorough account of a subarea of AI4Science; namely, AI for quantum, atomistic, and continuum systems. These areas aim at understanding the physical world from the subatomic (wavefunctions and electron density), atomic (molecules, proteins, materials, and interactions), to macro (fluids, climate, and subsurface) scales and form an important subarea of AI4Science. A unique advantage of focusing on these areas is that they largely share a common set of challenges, thereby allowing a unified and foundational treatment. A key common challenge is how to capture physics first principles, especially symmetries, in natural systems by deep learning methods. We provide an in-depth yet intuitive account of techniques to achieve equivariance to symmetry transformations. We also discuss other common technical challenges, including explainability, out-of-distribution generalization, knowledge transfer with foundation and large language models, and uncertainty quantification. To facilitate learning and education, we provide categorized lists of resources that we found to be useful. We strive to be thorough and unified and hope this initial effort may trigger more community interests and efforts to further advance AI4Science

PATTERN FORMATION AND PHASE TRANSITION OF CONNECTIVITY IN TWO DIMENSIONS

This dissertation is devoted to the study and analysis of different types of emergent behavior in physical systems. Emergence is a phenomenon that has fascinated researchers from various fields of science and engineering. From the emergence of global pandemics to the formation of reaction-diffusion patterns, the main feature that connects all these diverse systems is the appearance of a complex global structure as a result of collective interactions of simple underlying components. This dissertation will focus on two types of emergence in physical systems: emergence of long-range connectivity in networks and emergence and analysis of complex patterns. The most prominent theory which deals with the emergence of long-range connectivity is the percolation theory. This dissertation employs many concepts from the percolation theory to study connectivity transitions in various systems. Ordinary percolation theory is founded upon two main assumptions, namely locality and independence of the underlying components. In Chapters 2 and 3, we relax these assumptions in different manners and show that relaxing these assumptions leads to irregular behaviors such as appearance of different universality classes and, in some instances, violation of universality. Chapter 2 deals with relaxing the assumption of locality of interactions. In this Chapter, we define a hierarchy of various measures of robust connectivity. We study the phase transition of these robustness metrics as a function of site/bond occupation/removal probability on the square lattice. Furthermore, we perform extensive numerical analysis and extract these robustness metrics\u27 critical thresholds and critical behaviors. We show that some of these robustness metrics do not fall under the regular percolation universality class. The extensive numerical results in this work can serve as a foundation for any researcher who aims to design/study various degrees of connectivity in networks. In Chapter 3, we study the non-equilibrium phase transition of long-range connectivity in a multi-particle interacting system on the square lattice. The interactions between different particles translate to relaxing the assumption of independence in the percolation theory. Using extensive numerical simulations, we show that the phase transition observed in this system violates the regular concept of universality. However, it conforms well with the concept of weak-universality recently introduced in the literature. We observe that by varying inter-particle interaction strength in our model, one can control the critical behavior of this phase transition. These observations could be pivotal in studying phase transitions and universality classes. Chapter 4 focuses on the analysis of reaction-diffusion patterns. We utilize a multitude of machine learning algorithms to analyze reaction-diffusion patterns. In particular, we address two main problems using these techniques, namely, pattern regression and pattern classification. Given an observed instance of a pattern with a known generative function, in the pattern regression task, we aim to predict the specific set of reaction-diffusion parameters (i.e. diffusion constant) which can reproduce the observed pattern. We employ supervised learning techniques to successfully solve this problem and show the performance of our model in some real-world instances. We also address the task of pattern classification. In this task, we are interested in grouping different instances of similar patterns together. This task is usually performed visually by the researcher studying certain natural phenomena. However, this method is tedious and can be inconsistent among different researchers. We utilize supervised and unsupervised machine learning algorithms to classify patterns of the Gray-Scott model. We show that our methods show outstanding performance both in supervised and unsupervised settings. The methods introduced in this Chapter could bridge the gaps between researchers studying patterns in different fields of science and engineering

A deep learning modeling framework to capture mixing patterns in reactive-transport systems

Prediction and control of chemical mixing are vital for many scientific areas such as subsurface reactive transport, climate modeling, combustion, epidemiology, and pharmacology. Due to the complex nature of mixing in heterogeneous and anisotropic media, the mathematical models related to this phenomenon are not analytically tractable. Numerical simulations often provide a viable route to predict chemical mixing accurately. However, contemporary modeling approaches for mixing cannot utilize available spatial-temporal data to improve the accuracy of the future prediction and can be compute-intensive, especially when the spatial domain is large and for long-term temporal predictions. To address this knowledge gap, we will present in this paper a deep-learning (DL) modeling framework applied to predict the progress of chemical mixing under fast bimolecular reactions. This framework uses convolutional neural networks (CNN) for capturing spatial patterns and long short-term memory (LSTM) networks for forecasting temporal variations in mixing. By careful design of the framework -- placement of non-negative constraint on the weights of the CNN and the selection of activation function, the framework ensures non-negativity of the chemical species at all spatial points and for all times. Our DL-based framework is fast, accurate, and requires minimal data for training

Developing High-Performance 2D Heterostructured Electrocatalysts and Photocatalysts for Hydrogen Production and Utilizationsts and Photocatalysts for Hydrogen Production and Utilization

H2 is a pivotal chemical in modern society, not only as a clean energy carrier but also as a versatile chemical reactant. However, traditional hydrogen production and utilization heavily rely on thermocatalysis, which is highly energy-intensive and can result in heavy carbon emission and severe environmental problems. Photocatalysis and electrocatalysis are greener alternatives to thermocatalysis that can capitalize on the renewable sunlight and electricity and thus dramatically reduce energy requirements. However, heterogeneous electro/photocatalysts are still far from application to hydrogen economy due to the lack of design principles that can lead to sufficient efficiency. To address this challenge, the dissertation primarily focuses on developing high-performance electrocatalysts and photocatalysts by understanding the impact of surface defects and interactions between different phases on catalytic performance. With the obtained understanding, electro/photocatalysts with high efficiency in H2 production and utilization (herein, transfer hydrogenation) can be facilely fabricated. To better achieve an in-depth understanding of fabricating electro/photocatalysts used for the hydrogen economy, my thesis work starts with the research on H2 evolution reaction (HER) via electrocatalysis, and then moves to the HER using the more challenging photocatalytic approach and finally proceeds to the most challenging part, photocatalytic transfer hydrogenation. For electrocatalytic HER, MoS2 nanosheets are in situ grown on carbon fiber paper for the fabrication of the proton exchange membrane (PEM) cell electrode. Impressively, this integrated electrode with an ultralow MoS2 loading of 0.14 mg/cm2 can achieve small cell voltages of 1.96 and 2.25 V under 1 and 2 A/cm2, respectively, in a practical PEM cell, which is superior to most cells using noble-metal-free HER electrocatalysts even with extremely high catalyst loadings of 3~6 mg/cm2 under the similar cell operation conditions. The ultrahigh activity of the as-synthesized electrode is attributed to the intimate contact between MoS2 and CFP, vertical alignment of MoS2 nanosheets on CFP, the coexistence of 1T and 2H multiphase MoS2 and the existence of various defects on MoS2. For photocatalytic HER, an Au nanocage/MoS2 system is investigated to understand the effect of localized surface plasmon resonance (LSPR) on photocatalysis. The match between the LSPR wavelength of Au nanocages and the optical absorption edge of MoS2 is found to be critical to the activity of the composite. When the match is achieved, a 40-fold activity increase over the bare MoS2 is observed, while the other unmatched counterparts show much less activity enhancement (~15-fold). The near field enhancement (NFE) is proposed to govern the LSPR process with the energy of surface plasmon transferred from Au to MoS2 to promote electron excitation in MoS2, the efficiency of which maximized when the LSPR wavelength of Au matches the MoS2 absorption edge. In the photocatalytic transfer hydrogenation case, phenylacetylene (PA) semi-hydrogenation is selected as a model reaction to understand how vacancies in 2D semiconductors may be utilized to manipulate photocatalytic efficiency. 2D g-C3N4 nanosheets loaded with Ni single-atoms (SAs) are used as the catalyst for this reaction. By controlling both the Ni loading and the density of surface vacancies on g-C3N4, it is found that the numbers of vacancies and Ni SAs had a synergistic impact on the activity of the catalyst. Therefore, a fine tuning of both factors should be important to achieve an optimal hydrogenation activity. Overall, all research examples highlight the important role played by surface defects and metal-semiconductor interactions, and the findings from the research can be potentially used to guide the design of high-performance photocatalysts for hydrogen evolution and hydrogenation reactions