Coarse-grained Mapping Research Articles

Fine-Grained Urban Flow Inference via Normalizing Flow (Student Abstract)

Fine-grained urban flow inference (FUFI) aims to infer the coarse-grained (CG) urban flow map to the corresponding fine-grained (FG) one, which plays an important role in efficient traffic monitoring and management in smart cities. In FUFI, the CG map can be obtained with only a small number of monitoring devices, greatly reducing the overhead of deploying devices and the costs of maintenance, labor, and electricity. Existing FUFI methods are mainly based on techniques from image super-resolution (SR) models, which cannot fully consider the influence of external factors and face the ill-posed problem in SR tasks. In this paper, we propose UFI-Flow, a novel approach for addressing the FUFI problem by learning the conditional distributions of CG and FG map pairs. Given the CG map and the latent variables, the corresponding FG map is inferred by invertible transformations. In addition, an augmented distribution fusion mechanism is further proposed to constrain the urban flow distribution within the influence of external factors. We provide a new large-scale real-world FUFI dataset and show that UFI-Flow significantly outperforms the strong baselines.

Guided-Pix2Pix+: End-to-end spatial and color refinement network for image dehazing

Inevitable hazy contamination degrades the visibility of images, and the resulting haze removal is one of the essential prerequisites for image processing and computer vision tasks. We proposed an end-to-end dehazing network, referred to as Guided-Pix2Pix, to estimate spatially refined transmission maps and dehaze via the physical scattering equation. The remaining enhancement of color contrast, ill-posed adversarial training, and redundant backbone, however, should be thoroughly investigated, as required in the prospect of Guided-Pix2Pix. In this paper, we inherit the end-to-end structure of Guided-Pix2Pix and accordingly propose Guided-Pix2Pix+ as an update, which concatenates transmission estimation and refinement, physical scattering equation-based dehazing, together with color refinement, to achieve haze removal in a one-stage way. Specifically, we make use of the pretrained instance of EfficientNetB0 to estimate coarse-grained transmission maps and concatenate a guided filter layer to perform spatial refinement for the incoming transmission maps. Restored by the physical scattering equation, color refinement of dehazed proposals is finally performed via the standardization and clipping of pixel intensities. All the operations are differentiable, making it possible to achieve end-to-end, tight training. Furthermore, adversarial and perceptual losses are employed to regulate the performance of our model, giving rise to structurally similar but photo-realistic dehazed proposals. Extensive experiments confirm that our Guided-Pix2Pix+ yields dehazed proposals with fine-grained spatial refinement and relatively effective color contrast, compared to our previous Guided-Pix2Pix, the baseline, and advanced dehazing methods. The source code is currently available at https://github.com/92xianshen/guided-pix2pixplus.

Quantified overdamped limit for kinetic Vlasov–Fokker–Planck equations with singular interaction forces

We establish a quantified overdamped limit for kinetic Vlasov–Fokker–Planck equations with nonlocal interaction forces. We provide explicit bounds on the error between solutions of that kinetic equation and the limiting equation, which is known under the names of aggregation-diffusion equation or McKean–Vlasov equation. Introducing an intermediate system via a coarse-graining map, we quantitatively estimate the error between the spatial densities of the Vlasov–Fokker–Planck equation and the intermediate system in the Wasserstein distance of order 2. We then derive an evolution-variational-like inequality for Wasserstein gradient flows which allows us to quantify the error between the intermediate system and the corresponding limiting equation. Our strategy only requires weak integrability of the interaction potentials, thus in particular it includes the quantified overdamped limit of the kinetic Vlasov–Poisson–Fokker–Planck system to the aggregation-diffusion equation with either repulsive electrostatic or attractive gravitational interactions.

Coarse-Grained Density Functional Theory Predictions via Deep Kernel Learning

Scalable electronic predictions are critical for soft materials design. Recently, the Electronic Coarse-Graining (ECG) method was introduced to renormalize all-atom quantum chemical (QC) predictions to coarse-grained (CG) resolutions using deep neural networks (DNNs). While DNNs can learn complex representations that prove challenging for kernel-based methods, they are susceptible to overfitting and the overconfidence of uncertainty estimations. Here, we develop ECG within a GPU-accelerated Deep Kernel Learning (DKL) framework to enable CG QC predictions using range-separated hybrid density functional theory (DFT), obtaining a 107 speedup relative to naive all-atom QC. By treating the predicted electronic properties as random Gaussian Processes, DKL incorporates CG mapping degeneracy by learning the distribution of electronic energies as a function of CG configuration. DKL-ECG accurately reproduces molecular orbital energies from range-separated DFT while facilitating efficient training via active learning using the uncertainties provided by DKL. We show that while active learning algorithms enable efficient sampling of a more diverse configurational space relative to random sampling, all explored query methods exhibit comparable performance for the examined system. We attribute this result to the significant overlap of the feature space and output property distributions across multiple temperatures.

Automated Coarse-Grained Mapping Algorithm for the Martini Force Field and Benchmarks for Membrane-Water Partitioning.

With a view to high-throughput simulations, we present an automated system for mapping and parameterizing organic molecules for use with the coarse-grained Martini force field. The method scales to larger molecules and a broader chemical space than existing schemes. The core of the mapping process is a graph-based analysis of the molecule’s bonding network, which has the advantages of being fast, general, and preserving symmetry. The parameterization process pays special attention to coarse-grained beads in aromatic rings. It also includes a method for building efficient and stable frameworks of constraints for molecules with structural rigidity. The performance of the method is tested on a diverse set of 87 neutral organic molecules and the ability of the resulting models to capture octanol–water and membrane–water partition coefficients. In the latter case, we introduce an adaptive method for extracting partition coefficients from free-energy profiles to take into account the interfacial region of the membrane. We also use the models to probe the response of membrane–water partitioning to the cholesterol content of the membrane.

Granular internal dynamics in a silo discharged with a conveyor belt

The dynamics of granular media within a silo in which the grain velocities are controlled by a conveyor belt has been experimentally investigated. To this end, the building of coarse-grained field maps of different magnitudes has allowed a deep analysis of the flow properties as a function of two parameters: the orifice size and the belt velocity. First, the internal dynamics of the particles within the silo has been fully characterized by the solid fraction, the velocity of the particles and the kinetic stress. Then, the analysis of the vertical profiles of the same magnitude (plus the acceleration) has allowed connection of the internal dynamics with the flow rate. In particular, we show that the gamma parameter – which accounts for the integration of the normalized acceleration along the vertical direction – can successfully discriminate the kind of flow established within the silo (from the quasistatic regime to the free discharge) depending on the outlet size and belt velocity.

Conservative Potentials for a Lattice-Mapped Coarse-Grained Scheme.

The conservative potential, arising from a coarse-grain (CG) mapping scheme for nonbonded atomistic particles, is studied. This is a bottom-up approach from first-principles that maps atomistic particles to fluid element-like subcells whose centers lie on a regular, cubic lattice. Unlike standard CG mapping schemes, the current one uses dynamic labeling which on-the-fly changes the CG labels of the particles. The subcells can also be different sizes and shapes, in principle. Equilibrium atomistic molecular dynamics trajectories for different Lennard-Jones fluids are calculated and converted to CG ones, from which CG probability distribution functions are calculated. Correlation studies show position and mass CG variables are uncoupled in a given subcell, as are different vector components of position. Furthermore, the strongest coupling occurs with neighboring cells in specific directions, and the resulting distribution is well described by a multivariate Gaussian. This implies the CG potential has a generalized quadratic form, whose derivative can be determined analytically. A microscopic rationalization is provided for the signs and relative magnitudes of different correlation coefficients, and in some cases, a connection is made with bulk properties of the fluid. We argue the generalized quadratic form should be robust to changes in the particulars of the CG scheme, as well as the nature of the atomistic intermolecular potential. Only a few potential parameters need to be calculated from the underlying atomistic system. This is significant because it indicates the transferability of this form to other, more complex systems. This transferability will be tested in future work, where mapping schemes with fuzzy boundaries will be considered.

Quantum-to-classical transition via quantum cellular automata

A quantum cellular automaton (QCA) is an abstract model consisting of an array of finite-dimensional quantum systems that evolves in discrete time by local unitary operations. Here we propose a simple coarse-graining map, where the spatial structure of the QCA is merged into effective ones. Starting with a QCA that simulates the Dirac equation, we apply this coarse-graining map recursively until we get its effective dynamics in the semiclassical limit, which can be described by a classical cellular automaton. We show that the emergent-effective result of the former microscopic discrete model converges to the diffusion equation and to a classical transport equation under a specific initial condition. Therefore, QCA is a good model to validate the quantum-to-classical transition.

Macro-to-micro quantum mapping and the emergence of nonlinearity

As a universal theory of physics, quantum mechanics must assign states to every level of description of a system -- from a full microscopic description, all the way up to an effective macroscopic characterization -- and also to describe the interconnections among them. Assuming that we only have a coarse-grained access to a physical system, here we show how to assign to it a microscopic description that abides by all macroscopic constraints. In order to do that, we employ general coarse-graining maps, allowing our approach to be used even when the split between system and environment is not obvious. As a by-product, we show how effective nonlinear dynamics can emerge from the linear quantum evolution, and we readily apply it to a state discrimination task.

Coarse-Grained Force Fields from the Perspective of Statistical Mechanics: Better Understanding of the Origins of a MARTINI Hangover

The popular MARTINI coarse-grained model is used as a test case to analyze the adherence of top-down coarse-grained molecular dynamics models (i.e., models primarily parametrized to match experimental results) to the known features of statistical mechanics for the underlying all-atom representations. Specifically, the temperature dependence of various pair distribution functions, and hence their underlying potentials of mean force via the reversible work theorem, are compared between MARTINI 2.0, Dry MARTINI, and all-atom simulations mapped onto equivalent coarse-grained sites for certain lipid bilayers. It is found that the MARTINI models do not completely capture the lipid structure seen in atomistic simulations as projected onto the coarse-grained mappings and that issues of accuracy and temperature transferability arise due to an incorrect enthalpy–entropy decomposition of these potentials of mean force. The potential of mean force for the association of two amphipathic helices in a lipid bilayer is also calculated, and especially at shorter ranges, the MARTINI and all-atom projection results differ substantially. The former is much less repulsive and hence will lead to a higher probability of MARTINI helix association in the MARTINI bilayer than occurs in the actual all-atom case. Additionally, the bilayer height fluctuation spectra are calculated for the MARTINI model, and compared to the all-atom results, it is found that the magnitude of thermally averaged amplitudes at intermediate length scales are quite different, pointing to a number of possible consequences for realistic modeling of membrane processes. Taken as a whole, the results presented here show disagreement in the enthalpic and entropic driving forces driving lateral structure in lipid bilayers as well as quantitative differences in association of embedded amphipathic helices, which can help direct future efforts to parametrize CG models with better agreement to the all-atom systems they aspire to represent.

Super Resolution Deduction: Inferring Fine-Grained Capacity for Urban Signal Station Deployment

With the boosting of mobile devices, wireless sensor networks, and the internet of things, abundant multi-modal data, such as GPS signal, sensor data, are produced intentionally or unintentionally, which can represent the people's active patterns, vehicle's routes, and city's flows to develop a smart city. These multi-modal data are usually transmitted and received by signal stations deployed in the city. However, reasonably choosing the signal stations' locations is still an open issue for enhancing people's life quality in the smart city. To this end, we propose the Super Resolution Deduction (SRD) model for solving the signal station selection problem. SRD first initializes the city map as a coarse-grained heat map representing the capacity of the signal stations. Then an image-based super-resolution deduction model is proposed to obtain a fine-grained signal station capacity for deploying. To be specific, we employ Dense Block to capture the spatio-temporal correlations, C-Attention to selectively enhance useful feature maps, and S-Distribution to impose structural constraints. By sharing the GPS data load with the new deployment of signal stations, we ensure the smart city's efficiency and effectiveness. Extensive experimental results on real-world dataset Changchun City demonstrate that our proposed model achieves the superior performance among the state-of-the-art baselines.

Boundary Tracking of Continuous Objects Based on Binary Tree Structured SVM for Industrial Wireless Sensor Networks

Due to the flammability, explosiveness and toxicity of continuous objects (e.g., chemical gas, oil spill, radioactive waste) in the petrochemical and nuclear industries, boundary tracking of continuous objects is a critical issue for industrial wireless sensor networks (IWSNs). In this article, we propose a continuous object boundary tracking algorithm for IWSNs – which fully exploits the collective intelligence and machine learning capability within the sensor nodes. The proposed algorithm first determines an upper bound of the event region covered by the continuous objects. A binary tree-based partition is performed within the event region, obtaining a coarse-grained boundary area mapping. To study the irregularity of continuous objects in detail, the boundary tracking problem is then transformed into a binary classification problem; a <i>hierarchical soft margin support vector machine</i> training strategy is designed to address the binary classification problem in a distributed fashion. Simulation results demonstrate that the proposed algorithm shows a reduction in the number of nodes required for boundary tracking by at least 50 percent. Without additional fault-tolerant mechanisms, the proposed algorithm is inherently robust to false sensor readings, even for high ratios of faulty nodes ( <inline-formula><tex-math notation="LaTeX">$\approx 9\%$</tex-math></inline-formula> ).

Hybrid particle-field molecular dynamics under constant pressure.

Hybrid particle-field methods are computationally efficient approaches for modeling soft matter systems. So far, applications of these methodologies have been limited to constant volume conditions. Here, we reformulate particle-field interactions to represent systems coupled to constant external pressure. First, we show that the commonly used particle-field energy functional can be modified to model and parameterize the isotropic contributions to the pressure tensor without interfering with the microscopic forces on the particles. Second, we employ a square gradient particle-field interaction term to model non-isotropic contributions to the pressure tensor, such as in surface tension phenomena. This formulation is implemented within the hybrid particle-field molecular dynamics approach and is tested on a series of model systems. Simulations of a homogeneous water box demonstrate that it is possible to parameterize the equation of state to reproduce any target density for a given external pressure. Moreover, the same parameterization is transferable to systems of similar coarse-grained mapping resolution. Finally, we evaluate the feasibility of the proposed approach on coarse-grained models of phospholipids, finding that the term between water and the lipid hydrocarbon tails is alone sufficient to reproduce the experimental area per lipid in constant-pressure simulations and to produce a qualitatively correct lateral pressure profile.

Continuum Limits of Homogeneous Binary Trees and the Thompson Group.

Tree tensor network descriptions of critical quantum spin chains are empirically known to reproduce correlation functions matching conformal field theory (CFT) predictions in the continuum limit. It is natural to seek a more complete correspondence, additionally incorporating dynamics. On the CFT side, this is determined by a representation of the diffeomorphism group of the circle. In a remarkable series of papers, Jones outlined a research program where the Thompson group T takes the role of the latter in the discrete setting, and representations of T are constructed from certain elements of a subfactor planar algebra. He also showed that, for a particular example of such a construction, this approach only yields-in the continuum limit-a representation which is highly discontinuous and hence unphysical. Here we show that the same issue arises generically when considering tree tensor networks: the set of coarse-graining maps yielding discontinuous representations has full measure in the set of all isometries. This extends Jones's no-go example to typical elements of the so-called tensor planar algebra. We also identify an easily verified necessary condition for a continuous limit to exist. This singles out a particular class of tree tensor networks. Our considerations apply to recent approaches for introducing dynamics in holographic codes.

Is preservation of symmetry necessary for coarse-graining?

There is a need for theory on how to group atoms in a molecule to define a coarse-grained (CG) mapping. This article investigates the importance of preserving symmetry of the underlying molecular graph of a given molecule when choosing a CG mapping. 26 CG models of seven alkanes with three different CG techniques were examined. We unexpectedly find preserving symmetry has no consistent effect on CG model accuracy regardless of CG method or comparison metric.

Graph neural network based coarse-grained mapping prediction.

The selection of coarse-grained (CG) mapping operators is a critical step for CG molecular dynamics (MD) simulation. It is still an open question about what is optimal for this choice and there is a need for theory. The current state-of-the art method is mapping operators manually selected by experts. In this work, we demonstrate an automated approach by viewing this problem as supervised learning where we seek to reproduce the mapping operators produced by experts. We present a graph neural network based CG mapping predictor called Deep Supervised Graph Partitioning Model (DSGPM) that treats mapping operators as a graph segmentation problem. DSGPM is trained on a novel dataset, Human-annotated Mappings (HAM), consisting of 1180 molecules with expert annotated mapping operators. HAM can be used to facilitate further research in this area. Our model uses a novel metric learning objective to produce high-quality atomic features that are used in spectral clustering. The results show that the DSGPM outperforms state-of-the-art methods in the field of graph segmentation. Finally, we find that predicted CG mapping operators indeed result in good CG MD models when used in simulation.

Dual responsive PMEEECL-PAE block copolymers: a computational self-assembly and doxorubicin uptake study.

The self-assembly behaviour of dual-responsive block copolymers and their ability to solubilize the anticancer drug doxorubicin (DOX) has been investigated using all-atom molecular dynamics (MD) simulations, MARTINI coarse-grained (CG) force field simulation and Scheutjens–Fleer self-consistent field (SCF) computations. These diblock copolymers, composed of poly{γ-2-[2-(2-methoxyethoxy)ethoxy]ethoxy-ε-caprolactone} (PMEEECL) and poly(β-amino ester) (PAE) are dual-responsive: the PMEEECL block is thermoresponsive (becomes insoluble above a certain temperature), while the PAE block is pH-responsive (becomes soluble below a certain pH). Three MEEECL20–AEM compositions with M = 5, 10, and 15, have been studied. All-atom MD simulations have been performed to calculate the coil-to-globule transition temperature (Tcg) of these copolymers and finding appropriate CG mapping for both PMEEECL–PAE and DOX. The output of the MARTINI CG simulations is in agreement with SCF predictions. The results show that DOX is solubilized with high efficiency (75–80%) at different concentrations inside the PMEEECL–PAE micelles, although, interestingly, the loading efficiency is reduced by increasing the drug concentration. The non-bonded interaction energy and the RDF between DOX and water beads confirm this result. Finally, MD simulations and SCF computations reveal that the responsive behaviour of PMEEECL–PAE self-assembled structures take place at temperature and pH ranges appropriate for drug delivery.

Evidence of information limitations in coarse-grained models.

Developing accurate coarse-grained (CG) models is critical for addressing long time and length scale phenomena with molecular simulations. Here, we distinguish and quantify two sources of error that are relevant to CG models in order to guide further methods development: "representability" errors, which result from the finite basis associated with the chosen functional form of the CG model and mapping operator, and "information" errors, which result from the limited kind and quantity of data supplied to the CG parameterization algorithm. We have performed a systematic investigation of these errors by generating all possible CG models of three liquids (butane, 1-butanol, and 1,3-propanediol) that conserve a set of chemically motivated locality and topology relationships. In turn, standard algorithms (iterative Boltzmann inversion, IBI, and multiscale coarse-graining, MSCG) were used to parameterize the models and the CG predictions were compared with atomistic results. For off-target properties, we observe a strong correlation between the accuracy and the resolution of the CG model, which suggests that the approximations represented by MSCG and IBI deteriorate with decreasing resolution. Conversely, on-target properties exhibit an extremely weak resolution dependence that suggests a limited role of representability errors in model accuracy. Taken together, these results suggest that simple CG models are capable of utilizing more information than is provided by standard parameterization algorithms, and that model accuracy can be improved by algorithm development rather than resorting to more complicated CG models.

Structured mesh-oriented framework design and optimization for a coarse-grained parallel CFD solver based on hybrid MPI/OpenMP programming

Despite the shortcomings of the MPI/OpenMP hybrid parallel model that is extensively employed in massively parallel CFD solvers, this paper creates a set of MPI/OpenMP coarse-grained hybrid communication mapping rules for a structured mesh and establishes a mapping relationship among the geometric topology, the boundary communication topology, the topology of processes and threads groups, and the communication buffer. Based on the key technologies of the nonblocking asynchronous message communication and fine-grained mutex synchronization with a double-buffer mechanism for shared memory communication, an MPI/OpenMP coarse-grained hybrid parallel CFD solver framework for a structured mesh is designed. The experimental results show that the framework has high parallel performance and excellent scalability.

Study and Simulation of Nanoparticle Translocation Through Cell Membrane

In this research, nanoparticle translocation through cell membrane has been studied and simulated. To this end, gold nanoparticles have been selected as the main carrier of the drug and have been functionalized with some selected ligands. The partial charges of the ligands have been calculated using quantum mechanics based on HF technique with 6-31Gd basis set. To achieve the realistic shape of a drug, the number and arrangement of ligands loaded on the gold nanoparticle have been optimized. After determining the properties such as diffusion coefficient and validating the results with experimental data, a MARTINI coarse-grained mapping of the drugs is created. The coarse-grained model of the drug has been placed in a cellular microenvironment containing a solvent. The cytoplasmic membranes investigated in this study consists of more than 60 types phospholipids similar to animal cell membranes. After minimizing the system and performing procedures for controlling temperature and pressure, ions are added to the system and a potential difference is applied to the membrane and translocation of the drug is studied.