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Liu, Hao

Publications and source records attributed to Liu, Hao.

At least 19 records
All sourcesNASA NTRSDOE OSTI
DOE OSTI2025

Integrating vehicle trajectory planning and arterial traffic management to facilitate eco-approach and departure deployment

Eco-approach and departure (EAD) enable continuous vehicle motion in urban signalized corridors. Since such a motion can extend to the EAD vehicles’ followers, it makes EAD a promising technology to benefit the traffic flow where automated vehicles and conventional vehicles coexist. Most existing EAD studies envision an ideal setting that neglects real-world operational conditions such as lane changes, multi-movement intersection configuration, partially automated fleet, and/or limited traffic state awareness. This study aims to fill the gap by designing an EAD algorithm considering real-world traffic operation constraints. The proposed algorithm uses a model predictive controller to minimize vehicle speed reduction and variation based on the real-time traffic signal control plan and measured queues at the intersection. The required inputs are readily available at many modern intersections. We observed that the proposed controller’s performance might degrade because of lane-changing maneuvers and lead-left turn traffic signals. These observations motivated our development of a lane change management strategy and a signal control implementation strategy to facilitate the EAD implementation. The lane change management strategies separate the EAD operations and lane-changing maneuvers in time and space. The signal control implementation strategy applies lag-left turn signals to enable EAD operation for both the through and left-turn vehicles. Compared to the non-EAD case, our EAD approach produces 2.5% to 7.8% energy savings while keeping similar intersection mobility. Notably, this approach brings about 2.5% to 3.6% energy savings in a 2% CAV case. This result demonstrates the feasibility of deploying EAD at low connected automated vehicle penetration rates.

Arterial corridor management↗
DOE OSTI2024

Generalization error guaranteed auto-encoder-based nonlinear model reduction for operator learning

Many physical processes in science and engineering are naturally represented by operators between infinite-dimensional function spaces. The problem of operator learning, in this context, seeks to extract these physical processes from empirical data, which is challenging due to the infinite or high dimensionality of data. An integral component in addressing this challenge is model reduction, which reduces both the data dimensionality and problem size. In this paper, we utilize low-dimensional nonlinear structures in model reduction by investigating Auto-Encoder-based Neural Network (AENet). AENet first learns the latent variables of the input data and then learns the transformation from these latent variables to corresponding output data. Our numerical experiments validate the ability of AENet to accurately learn the solution operator of nonlinear partial differential equations. Furthermore, we establish a mathematical and statistical estimation theory that analyzes the generalization error of AENet. Finally, our theoretical framework shows that the sample complexity of training AENet is intricately tied to the intrinsic dimension of the modeled process, while also demonstrating the robustness of AENet to noise.

Auto-encoder↗
DOE OSTI2024

Complex Phase Transitions of Fully Rigid Sphere–Rod Amphiphiles Induced by Solvent Polarity in Dilute Solutions

Here we report complex macrophase and microphase transitions of rigid amphiphiles with spherical Keggin molecular clusters as the solvophilic block and rod-like rigid oligofluorene (OF) as the solvophobic block in mixed solvents of water and polar organic solvent. By properly adjusting the solvent polarity, the amphiphiles are found to respond accordingly by self-assembling into multilayered incomplete onion-like structures (10–25 vol % THF), single-layered vesicular structures (60 vol % THF), and an unexpected macrophase separation in the middle (40–50 vol % THF), which is due to the anomalous trends in Keggin solubility as a result of the nature of TBA + counterions. The rigidity of the OF block prevents the amphiphile from assembling by following the rule of packing parameters; instead, interdigitation among different rods leads to the formation of the solvophobic domain to achieve self-assembly. The incomplete onion structures are controlled by the interdigitation of rigid rods for the number of layers and the electrostatic interaction among Keggin head groups for the interlayer distance. When the degree of interdigitation becomes lower, the self-assembly process shows a trend that can be explained by the traditional rule of packing parameter. This study demonstrates the formation of different self-assembled structures by rigid amphiphiles and their transitions induced by solvent composition. The self-assembly (microphase separation) of rigid amphiphiles in a dilute solution could indeed represent a broad area containing complicated, uncharted rules.

36 MATERIALS SCIENCE↗
DOE OSTI2024

Inverse design of a pyrochlore lattice of DNA origami through model-driven experiments

Sophisticated statistical mechanics approaches and human intuition have demonstrated the possibility of self-assembling complex lattices or finite-size constructs. However, attempts so far have mostly only been successful in silico and often fail in experiment because of unpredicted traps associated with kinetic slowing down (gelation, glass transition) and competing ordered structures. Theoretical predictions also face the difficulty of encoding the desired interparticle interaction potential with the experimentally available nano- and micrometer-sized particles. To overcome these issues, we combine SAT assembly (a patchy-particle interaction design algorithm based on constrained optimization) with coarse-grained simulations of DNA nanotechnology to experimentally realize trap-free self-assembly pathways. In this paper, we use this approach to assemble a pyrochlore three-dimensional lattice, coveted for its promise in the construction of optical metamaterials, and characterize it with small-angle x-ray scattering and scanning electron microscopy visualization.

77 NANOSCIENCE AND NANOTECHNOLOGY↗
DOE OSTI2024

Learning Functions Varying along a Central Subspace

Many functions of interest are in a high-dimensional space but exhibit low-dimensional structures. This paper studies regression of an s-Hölder function in $R^D$ which varies along a central subspace of dimension $d$ while $d \ll D$. A direct approximation of $f$ in $R^D$ with an accuracy $\varepsilon$ requires the number of samples in the order of $\varepsilon^{-(2s+D)/s}$. In this paper, we analyze the generalized contour regression (GCR) algorithm for the estimation of the central subspace and use piecewise polynomials for function approximation. GCR is among the best estimators for the central subspace, but its sample complexity is an open question. In this paper, we partially answer this questions by proving that if a variance quantity is exactly known, GCR leads to a mean squared estimation error of $O(n^{-1})$ for the central subspace. The estimation error of this variance quantity is also given in this paper. The mean squared regression error of $f$ is proved to be in the order of $(n/\log n)^{-\frac{2s}{2s+d}}$, where the exponent depends on the dimension of the central subspace instead of the ambient space . This result demonstrates that GCR is effective in learning the low-dimensional central subspace. We also propose a modified GCR with improved efficiency. Here, the convergence rate is validated through several numerical experiments.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2024

Deep Nonparametric Estimation of Operators between Infinite Dimensional Spaces

Learning operators between infinitely dimensional spaces is an important learning task arising in machine learning, imaging science, mathematical modeling and simulations, etc. This paper studies the nonparametric estimation of Lipschitz operators using deep neural networks. Non-asymptotic upper bounds are derived for the generalization error of the empirical risk minimizer over a properly chosen network class. Under the assumption that the target operator exhibits a low dimensional structure, our error bounds decay as the training sample size increases, with an attractive fast rate depending on the intrinsic dimension in our estimation. Our assumptions cover most scenarios in real applications and our results give rise to fast rates by exploiting low dimensional structures of data in operator estimation. We also investigate the influence of network structures (e.g., network width, depth, and sparsity) on the generalization error of the neural network estimator and propose a general suggestion on the choice of network structures to maximize the learning efficiency quantitatively.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2023

High dimensional binary classification under label shift: phase transition and regularization

Label Shift has been widely believed to be harmful to the generalization performance of machine learning models. Researchers have proposed many approaches to mitigate the impact of the label shift, e.g., balancing the training data. However, these methods often consider the underparametrized regime, where the sample size is much larger than the data dimension. The research under the overparametrized regime is very limited. Here, to bridge this gap, we propose a new asymptotic analysis of the Fisher Linear Discriminant classifier for binary classification with label shift. Specifically, we prove that there exists a phase transition phenomenon: Under certain overparametrized regime, the classifier trained using imbalanced data outperforms the counterpart with reduced balanced data. Moreover, we investigate the impact of regularization to the label shift: The aforementioned phase transition vanishes as the regularization becomes strong.

binary classification↗
DOE OSTI2023

Deep nonparametric estimation of intrinsic data structures by chart autoencoders: Generalization error and robustness

Autoencoders have demonstrated remarkable success in learning low-dimensional latent features of high-dimensional data across various applications. Assuming that data are sampled near a low-dimensional manifold, we employ chart autoencoders, which encode data into low-dimensional latent features on a collection of charts, preserving the topology and geometry of the data manifold. Our paper establishes statistical guarantees on the generalization error of chart autoencoders, and we demonstrate their denoising capabilities by considering n noisy training samples, along with their noise-free counterparts, on a d-dimensional manifold. By training autoencoders, we show that chart autoencoders can effectively denoise the input data with normal noise. We prove that, under proper network architectures, chart autoencoders achieve a squared generalization error in the order of n–$\frac{2}{d+2}$log 4 n, which depends on the intrinsic dimension of the manifold and only weakly depends on the ambient dimension and noise level. We further extend our theory on data with noise containing both normal and tangential components, where chart autoencoders still exhibit a denoising effect for the normal component. As a special case, our theory also applies to classical autoencoders, as long as the data manifold has a global parametrization. Furthermore, our results provide a solid theoretical foundation for the effectiveness of autoencoders, which is further validated through several numerical experiments.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2023

Group Projected subspace pursuit for IDENTification of variable coefficient differential equations (GP-IDENT)

We propose an effective and robust algorithm for identifying partial differential equations (PDEs) with space-time varying coefficients from the noisy observation of a single solution trajectory. Identifying unknown differential equations from noisy data is a difficult task, and it is even more challenging with space and time varying coefficients in the PDE. The proposed algorithm, GP-IDENT, has three ingredients: (i) we use B-spline bases to express the unknown space and time varying coefficients, (ii) we propose Group Projected Subspace Pursuit (GPSP) to find a sequence of candidate PDEs with various levels of complexity, and (iii) we propose a new criterion for model selection using the Reduction in Residual (RR) to choose an optimal one among a pool of candidates. The new GPSP considers group projected subspaces which is more robust than existing methods in distinguishing correlated group features. We test GP-IDENT on a variety of PDEs and PDE systems, and compare it with the state-of-the-art parametric PDE identification algorithms under different settings to illustrate its outstanding performance. Furthermore, our experiments show that GP-IDENT is effective in identifying the correct terms from a large dictionary, and our model selection scheme is robust to noise.

Data-driven method↗
DOE OSTI2023

Itinerant to relocalized transition of f electrons in the Kondo insulator CeRu 4 Sn 6

In this study, the three-dimensional electronic structure and the nature of Ce 4 f electrons of the Kondo insulator CeRu 4 Sn 6 are investigated by angle-resolved photoemission spectroscopy, utilizing tunable photon energies. Our results reveal (i) the three-dimensional k -space nature of the Fermi surface, (ii) the localized-to-itinerant transition of f electrons occurs at a much high temperature than the hybridization gap opening temperature, and (iii) the "relocalization" of itinerant f electrons below 25 K, which could be the precursor to the establishment of magnetic order.

72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗
DOE OSTI2023

Complex defect chemistry of hydrothermally-synthesized Nb-substituted β$'$-LiVOPO 4

Lithium vanadyl phosphate (LiVOPO 4 ) is a next-generation multielectron battery cathode that can intercalate up to two Li-ions per V-ion through the redox couples of V 4+ /V 3+ and V 5+ /V 4+ . However, its rate capacity is undermined by the sluggish Li-ion diffusion in the high-voltage region (4 V for V 5+ /V 4+ redox). Nb substitution was used to expand the crystal lattice to facilitate Li-ion diffusion. Here, in this work, Nb substitution was achieved via hydrothermal synthesis, which resulted in a new, lower symmetry β'-LiVOPO 4 phase with preferential Nb occupation of one of the two V sites. This phase presents complex defect chemistries, including cation vacancies and hydrogen interstitials, characterized by a combination of X-ray and neutron diffraction, elemental and thermogravimetric analyses, X-ray absorption spectroscopy, and magnetic susceptibility measurements. The Nb-substituted samples demonstrated improved capacity retention and rate capabilities in the high-voltage region, albeit an enlarged voltage hysteresis related to a partial V 4+ /V 3+ redox reaction, as evidenced by ex situ X-ray absorption spectroscopy and pair distribution function analysis. This work highlights the importance of understanding the complex defect chemistry and its consequence on electrochemistry in polyanionic intercalation compounds.

36 MATERIALS SCIENCE↗
DOE OSTI2023

Structural basis for botulinum neurotoxin E recognition of synaptic vesicle protein 2

Botulinum neurotoxin E (BoNT/E) is one of the major causes of human botulism and paradoxically also a promising therapeutic agent. Here we determined the co-crystal structures of the receptor-binding domain of BoNT/E (H C E) in complex with its neuronal receptor synaptic vesicle glycoprotein 2A (SV2A) and a nanobody that serves as a ganglioside surrogate. These structures reveal that the protein-protein interactions between H C E and SV2 provide the crucial location and specificity information for H C E to recognize SV2A and SV2B, but not the closely related SV2C. At the same time, H C E exploits a separated sialic acid-binding pocket to mediate recognition of an N-glycan of SV2. Structure-based mutagenesis and functional studies demonstrate that both the protein-protein and protein-glycan associations are essential for SV2A-mediated cell entry of BoNT/E and for its potent neurotoxicity. Our studies establish the structural basis to understand the receptor-specificity of BoNT/E and to engineer BoNT/E variants for new clinical applications.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2022

Metabolic activity and community structure of prokaryotes associated with particles in the twilight zone of the South China Sea

The twilight zone is an important depth of the ocean where particulate organic matter (POM) remineralization takes place, and prokaryotes contribute to more than 70% of the estimated remineralization. However, little is known about the microbial community and metabolic activity associated with different particles in the twilight zone. The composition and distribution of particle-attached prokaryotes in the twilight zone of the South China Sea (SCS) were investigated using high-throughput sequencing and quantitative PCR, together with the Biolog Ecoplate™ microplates culture to analyze the microbial metabolic activity. We found that α- and γ-Proteobacteria dominating at the lower and upper boundary of the twilight zone, respectively; Methanosarcinales and Halobacteriales of the Euyarchaeota occupied in the larger particles at the upper boundary. Similar microbial community existed between euphotic layer and the upper boundary. Higher amount of shared Operational Taxonomic Units (OTUs) in the larger particles along the water depths, might be due to the fast sinking and major contribution of carbon flux of the larger particles from the euphotic layer. In addition to polymers as the major carbon source, carbohydrates and amino acids were preferentially used by microbial community at the upper and lower boundary, respectively. This could potentially be attributed to the metabolic capabilities of attached microbial groups in different particles, and reflected the initial preference of the carbon source by the natural microbes in the twilight zone as well. The microbial structure and carbon metabolic profiles could be complemented with metatranscriptomic analysis in future studies to augment the understanding of the complex carbon cycling pathways in the twilight zone.

Liu, Hao↗
DOE OSTI2022

Two‐Dimensional Excitonic Networks Directed by DNA Templates as an Efficient Model Light‐Harvesting and Energy Transfer System

Abstract Photosynthetic organisms organize discrete light‐harvesting complexes into large‐scale networks to facilitate efficient light collection and utilization. Inspired by nature, herein, synthetic DNA templates were used to direct the formation of dye aggregates with a cyanine dye, K21, into discrete branched photonic complexes, and two‐dimensional (2D) excitonic networks. The DNA templates ranged from four‐arm DNA tiles, ≈10 nm in each arm, to 2D wireframe DNA origami nanostructures with different geometries and varying dimensions up to 100×100 nm. These DNA‐templated dye aggregates presented strongly coupled spectral features and delocalized exciton characteristics, enabling efficient photon collection and energy transfer. Compared to the discrete branched photonic systems templated on individual DNA tiles, the interconnected excitonic networks showed approximately a 2‐fold increase in energy transfer efficiency. This bottom‐up assembly strategy paves the way to create 2D excitonic systems with complex geometries and engineered energy pathways.

Zhou, Xu↗
DOE OSTI2022

Two–Dimensional Excitonic Networks Directed by DNA Templates as an Efficient Model Light–Harvesting and Energy Transfer System

Photosynthetic organisms organize discrete light-harvesting complexes into large-scale networks to facilitate efficient light collection and utilization. Inspired by nature, herein, synthetic DNA templates were used to direct the formation of dye aggregates with a cyanine dye, K21, into discrete branched photonic complexes, and two-dimensional (2D) excitonic networks. The DNA templates ranged from four-arm DNA tiles, ≈10 nm in each arm, to 2D wireframe DNA origami nanostructures with different geometries and varying dimensions up to 100×100 nm. These DNA-templated dye aggregates presented strongly coupled spectral features and delocalized exciton characteristics, enabling efficient photon collection and energy transfer. Compared to the discrete branched photonic systems templated on individual DNA tiles, the interconnected excitonic networks showed approximately a 2-fold increase in energy transfer efficiency. Lastly, this bottom-up assembly strategy paves the way to create 2D excitonic systems with complex geometries and engineered energy pathways.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗