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Hongbo Zhao

Publications and source records attributed to Hongbo Zhao.

At least 55 records · Page 3Linked to original sources
arXiv2023

Theory of chemically driven pattern formation in phase-separating liquids and solids

Motivated by recent experimental and theoretical work on the control of phase separation by (electro-)autocatalytic reactions, we analyze pattern formation in externally driven phase separating systems described by a generalization of the Cahn-Hilliard and Allen-Cahn equations combining nonlinear reaction kinetics with diffusive transport. The theory predicts that phase separation can be suppressed by driven autoinhibitory reactions when chemically driven at a sufficiently high reaction rate and low diffusivity, while autocatalytic reactions enhance phase separation. Analytical stability criteria for predicting the critical condition of suppressed phase separation based on linear stability analysis track the history dependence of pattern formation and agree well with numerical simulations. By including chemo-mechanical coupling in the model, we extend the theory to solids, where coherency strain alters the morphology and dynamics of driven phase separation. We apply this model to lithium iron phosphate nanoparticles and simulate their rate-dependent electrochemical charging and discharging patterns, paving the way for a quantitative understanding of the effect of reaction kinetics, diffusion, and mechanics on the electrochemical performance of energy materials. The theory may also find applications to microstructure formation in hardening cement paste, as well as membraneless organelle formation in biological cells by chemically controlled liquid-liquid phase separation.

physics.chem-ph↗
arXiv2023

Self-Training Guided Disentangled Adaptation for Cross-Domain Remote Sensing Image Semantic Segmentation

Deep convolutional neural networks (DCNNs) based remote sensing (RS) image semantic segmentation technology has achieved great success used in many real-world applications such as geographic element analysis. However, strong dependency on annotated data of specific scene makes it hard for DCNNs to fit different RS scenes. To solve this problem, recent works gradually focus on cross-domain RS image semantic segmentation task. In this task, different ground sampling distance, remote sensing sensor variation and different geographical landscapes are three main factors causing dramatic domain shift between source and target images. To decrease the negative influence of domain shift, we propose a self-training guided disentangled adaptation network (ST-DASegNet). We first propose source student backbone and target student backbone to respectively extract the source-style and target-style feature for both source and target images. Towards the intermediate output feature maps of each backbone, we adopt adversarial learning for alignment. Then, we propose a domain disentangled module to extract the universal feature and purify the distinct feature of source-style and target-style features. Finally, these two features are fused and served as input of source student decoder and target student decoder to generate final predictions. Based on our proposed domain disentangled module, we further propose exponential moving average (EMA) based cross-domain separated self-training mechanism to ease the instability and disadvantageous effect during adversarial optimization. Extensive experiments and analysis on benchmark RS datasets show that ST-DASegNet outperforms previous methods on cross-domain RS image semantic segmentation task and achieves state-of-the-art (SOTA) results. Our code is available at https://github.com/cv516Buaa/ST-DASegNet.

cs.CV↗
arXiv2023

Chemotactic motility-induced phase separation

Collectives of actively-moving particles can spontaneously separate into dilute and dense phases -- a fascinating phenomenon known as motility-induced phase separation (MIPS). MIPS is well-studied for randomly-moving particles with no directional bias. However, many forms of active matter exhibit collective chemotaxis, directed motion along a chemical gradient that the constituent particles can generate themselves. Here, using theory and simulations, we demonstrate that collective chemotaxis strongly competes with MIPS -- in some cases, arresting or completely suppressing phase separation, or in other cases, generating fundamentally new dynamic instabilities. We establish quantitative principles describing this competition, thereby helping to reveal and clarify the rich physics underlying active matter systems that perform chemotaxis, ranging from cells to robots.

physics.bio-ph↗
arXiv2022

PGGANet: Pose Guided Graph Attention Network for Person Re-identification

Person re-identification (reID) aims at retrieving a person from images captured by different cameras. For deep-learning-based reID methods, it has been proved that using local features together with global feature could help to give robust representation for person retrieval. Human pose information could provide the locations of human skeleton to effectively guide the network to pay more attention on these key areas and could also help to reduce the noise distractions from background or occlusion. However, methods proposed by previous pose-based works might not be able to fully exploit the benefits of pose information and few of them take into consideration the different contributions of separate local features. In this paper, we propose a pose guided graph attention network, a multi-branch architecture consisting of one branch for global feature, one branch for mid-granular body features and one branch for fine-granular key point features. We use a pre-trained pose estimator to generate the key-point heatmaps for local feature learning and carefully design a graph attention convolution layer to re-assign the contribution weights of extracted local features by modeling the similarities relations. Experiment results demonstrate the effectiveness of our approach on discriminative feature learning and we show that our model achieves state-of-the-art performances on several mainstream evaluation datasets. We also conduct a plenty of ablation studies and design different kinds of comparison experiments for our network to prove its effectiveness and robustness, including occluded experiments and cross-domain tests.

cs.CV↗
arXiv2021

Correlative image learning of chemo-mechanics in phase-transforming solids

Constitutive laws underlie most physical processes in nature. However, learning such equations in heterogeneous solids (e.g., due to phase separation) is challenging. One such relationship is between composition and eigenstrain, which governs the chemo-mechanical expansion in solids. In this work, we developed a generalizable, physically-constrained image-learning framework to algorithmically learn the chemo-mechanical constitutive law at the nanoscale from correlative four-dimensional scanning transmission electron microscopy and X-ray spectro-ptychography images. We demonstrated this approach on Li$_X$FePO$_4$, a technologically-relevant battery positive electrode material. We uncovered the functional form of composition-eigenstrain relation in this two-phase binary solid across the entire composition range (0 $\leq$ X $\leq$ 1), including inside the thermodynamically-unstable miscibility gap. The learned relation directly validates Vegard's law of linear response at the nanoscale. Our physics-constrained data-driven approach directly visualizes the residual strain field (by removing the compositional and coherency strain), which is otherwise impossible to quantify. Heterogeneities in the residual strain arise from misfit dislocations and were independently verified by X-ray diffraction line profile analysis. Our work provides the means to simultaneously quantify chemical expansion, coherency strain and dislocations in battery electrodes, which has implications on rate capabilities and lifetime. Broadly, this work also highlights the potential of integrating correlative microscopy and image learning for extracting material properties and physics.

cond-mat.mtrl-sci↗
arXiv2021

AdaL: Adaptive Gradient Transformation Contributes to Convergences and Generalizations

Adaptive optimization methods have been widely used in deep learning. They scale the learning rates adaptively according to the past gradient, which has been shown to be effective to accelerate the convergence. However, they suffer from poor generalization performance compared with SGD. Recent studies point that smoothing exponential gradient noise leads to generalization degeneration phenomenon. Inspired by this, we propose AdaL, with a transformation on the original gradient. AdaL accelerates the convergence by amplifying the gradient in the early stage, as well as dampens the oscillation and stabilizes the optimization by shrinking the gradient later. Such modification alleviates the smoothness of gradient noise, which produces better generalization performance. We have theoretically proved the convergence of AdaL and demonstrated its effectiveness on several benchmarks.

cs.LG↗
arXiv2021

Image Inversion and Uncertainty Quantification for Constitutive Laws of Pattern Formation

The forward problems of pattern formation have been greatly empowered by extensive theoretical studies and simulations, however, the inverse problem is less well understood. It remains unclear how accurately one can use images of pattern formation to learn the functional forms of the nonlinear and nonlocal constitutive relations in the governing equation. We use PDE-constrained optimization to infer the governing dynamics and constitutive relations and use Bayesian inference and linearization to quantify their uncertainties in different systems, operating conditions, and imaging conditions. We discuss the conditions to reduce the uncertainty of the inferred functions and the correlation between them, such as state-dependent free energy and reaction kinetics (or diffusivity). We present the inversion algorithm and illustrate its robustness and uncertainties under limited spatiotemporal resolution, unknown boundary conditions, blurry initial conditions, and other non-ideal situations. Under certain situations, prior physical knowledge can be included to constrain the result. Phase-field, reaction-diffusion, and phase-field-crystal models are used as model systems. The approach developed here can find applications in inferring unknown physical properties of complex pattern-forming systems and in guiding their experimental design.

physics.comp-ph↗
arXiv2020

Correlated Ion Transport and the Gel Phase in Room Temperature Ionic Liquids

Here we present a theory of ion aggregation and gelation of room temperature ionic liquids (RTILs). Based on it, we investigate the effect of ion aggregation on correlated ion transport - ionic conductivity and transference numbers - obtaining closed-form expressions for these quantities.The theory depends on the maximum number of associations a cation and anion can form, and the strength of their association. To validate the presented theory, we perform molecular dynamics simulations on several RTILs, and a range of temperatures for one RTIL. The simulations indicate the formation of large clusters, even percolating through the system under certain circumstances, thus forming a gel, with the theory accurately describing the obtained cluster distributions in all cases. We discuss the possibility of observing a gel phase in neat RTILs, which has hitherto not been discussed in any detail.

physics.chem-ph↗
arXiv2019

On Fixed Points of a Map Defined by Alain Connes

In this paper we study certain analogues of the map defined by Alain Connes, which follows an idea of Atiyah in trying to simplify the proof of Feit-Thompson theorem. It turns out that the the non-zero fixed points of the map can be characterized explicitly as an abelian group, and we can calculate some examples. It turns out that most fixed points are not one dimensional representations for these examples, and much work need to be done.

math.QA↗
arXiv2019

Linear stability analysis of transient electrodeposition in charged porous media: suppression of dendritic growth by surface conduction

We study the linear stability of transient electrodeposition in a charged random porous medium, whose pore surface charges can be of any sign, flanked by a pair of planar metal electrodes. Discretization of the linear stability problem results in a generalized eigenvalue problem for the dispersion relation that is solved numerically, which agrees well with the analytical approximation obtained from a boundary layer analysis valid at high wavenumbers. Under galvanostatic conditions in which an overlimiting current is applied, in the classical case of zero surface charges, the electric field at the cathode diverges at Sand's time due to electrolyte depletion. The same phenomenon happens for positive charges but earlier than Sand's time. However, negative charges allow the system to sustain an overlimiting current via surface conduction past Sand's time, keeping the electric field bounded. Therefore, at Sand's time, negative charges greatly reduce surface instabilities and suppress dendritic growth, while zero and positive charges magnify them. We compare theoretical predictions for overall surface stabilization with published experimental data for copper electrodeposition in cellulose nitrate membranes and demonstrate good agreement between theory and experiment. We also apply the stability analysis to how crystal grain size varies with duty cycle during pulse electroplating.

physics.chem-ph↗
arXiv2019

Population dynamics of driven autocatalytic reactive mixtures

Motivated by the theory of reaction kinetics based on nonequilibrium thermodynamics and the linear stability of driven reaction-diffusion, we apply the Fokker-Planck equation to describe the population dynamics of an ensemble of reactive particles in contact with a chemical reservoir. We illustrate the effect of autocatalysis on the population dynamics by comparing systems with identical thermodynamics yet different reaction kinetics. The dynamic phase behavior of the system may be entirely different from what its thermodynamics may suggest. By defining phase separation for a particle ensemble to be when the probability distribution is bimodal, we find that thermodynamic phase separation may be suppressed by autoinhibitory reactions, while autocatalysis enhances phase separation and in some cases induce the ensemble that consists of thermodynamically single-phase systems to segregate into two distinct populations, which we term fictitious phase separation. Asymmetric reaction kinetics also results in qualitatively different population dynamics upon reversing the reaction direction. In the limit of negligible fluctuations, we use method of characteristics and linearization to study the evolution of the standard deviation of concentration as well as the condition for phase separation, in good agreement with the full numerical solution. Applications are discussed to Li-ion batteries and {\it in situ} x-ray diffraction.

physics.chem-ph↗
arXiv2018

Efficient Top K Temporal Spatial Keyword Search

Massive amount of data that are geo-tagged and associated with text information are being generated at an unprecedented scale in many emerging applications such as location based services and social networks. Due to their importance, a large body of work has focused on efficiently computing various spatial keyword queries. In this paper,we study the top-$k$ temporal spatial keyword query which considers three important constraints during the search including time, spatial proximity and textual relevance. A novel index structure, namely SSG-tree, to efficiently insert/delete spatio-temporal web objects with high rates. Base on SSG-tree an efficient algorithm is developed to support top-k temporal spatial keyword query. We show via extensive experimentation with real spatial databases that our method has increased performance over alternate techniques

cs.DB↗
arXiv2017

Hybrid Electrothermal Simulation of a Three-Dimensional Fin-Shaped Field-Effect Transistor Based on GaN Nanowires

In recent years, three-dimensional GaN-based transistors have been intensively studied for their dramatically improved output power, better gate controllability, and shorter channels for speedup and miniaturization. However, thermal analysis of such devices is often oversimplified using the conventional Fourier's law and bulk material properties in thermal simulations. In this aspect, accurate temperature predictions can be achieved by coupled phonon and electron Monte Carlo simulations that track the movement and scattering of individual phonons and electrons. However, the heavy computational load often restricts such simulations to nanoscale devices, while a real chip is of millimeter to centimeter sizes. This issue can be addressed by a hybrid simulation technique that employs the Fourier's law for regions away from the hot spot. Using this technique, accurate electrothermal simulations are carried out on a nanowire-based GaN transistor to reveal the temperature rise in such devices.

cond-mat.mtrl-sci↗
arXiv2017

Thermoelectric Studies of Nanoporous Thin Films with Adjusted Pore-Edge Charges

In recent years, nanoporous thin films have been widely studied for thermoelectric applications. High thermoelectric performance is reported for nanoporous Si films, which is attributed to the dramatically reduced lattice thermal conductivity and bulk-like electrical properties. Porous materials can also be used in gas sensing applications by engineering the surface-trapped charges on pore edges. In this work, an analytical model is developed to explore the relationship between the thermoelectric properties and pore-edge charges in a periodic two-dimensional nanoporous material. The presented model can be widely used to analyze the measured electrical properties of general nanoporous thin films and two-dimensional materials.

cond-mat.mtrl-sci↗