Clustered active-subspace based local Gaussian Process emulator for high-dimensional and complex computer models

Quantifying uncertainties in physical or engineering systems often requires a large number of simulations of the underlying computer models that are computationally intensive. Emulators or surrogate models are often used to accelerate the computation in such problems, and in this regard the Gaussian Process (GP) emulator is a popular choice for its ability to quantify the approximation error in the emulator itself. However, a major limitation of the GP emulator is that it can not handle problems of very high dimensions, which is often addressed with dimension reduction techniques. In this work we hope to address an issue that the models of interest are so complex that they admit different low dimensional structures in different parameter regimes. Building upon the active subspace method for dimension reduction, we propose a clustered active subspace method which identifies the local low-dimensional structures as well as the parameter regimes they are in (represented as clusters), and then construct low dimensional and local GP emulators within the clusters. Specifically we design a clustering method based on the gradient information to identify these clusters, and a local GP construction procedure to construct the GP emulator within a local cluster. With numerical examples, we demonstrate that the proposed method is effective when the underlying models are of complex low-dimensional structures

Characterization, synthesis, and optimization of quantum circuits over multiple-control Z\textit{Z}-rotation gates: A systematic study

We conduct a systematic study of quantum circuits composed of multiple-control $Z$-rotation (MCZR) gates as primitives, since they are widely-used components in quantum algorithms and also have attracted much experimental interest in recent years. Herein, we establish a circuit-polynomial correspondence to characterize the functionality of quantum circuits over the MCZR gate set with continuous parameters. An analytic method for exactly synthesizing such quantum circuit to implement any given diagonal unitary matrix with an optimal gate count is proposed, which also enables the circuit depth optimal for specific cases with pairs of complementary gates. Furthermore, we present a gate-exchange strategy together with a flexible iterative algorithm for effectively optimizing the depth of any MCZR circuit, which can also be applied to quantum circuits over any other commuting gate set. Besides the theoretical analysis, the practical performances of our circuit synthesis and optimization techniques are further evaluated by numerical experiments on two typical examples in quantum computing, including diagonal Hermitian operators and Quantum Approximate Optimization Algorithm (QAOA) circuits with tens of qubits, which can demonstrate a reduction in circuit depth by 33.40\% and 15.55\% on average over relevant prior works, respectively. Therefore, our methods and results provide a pathway for implementing quantum circuits and algorithms on recently developed devices.Comment: Comments are welcom

A multi-objective differential evolutionary algorithm for optimal sustainable pavement maintenance plan at the network level

Sustainable highway pavement maintenance is important for achieving sustainability in the transportation sector. Because the three aspects included in sustainability metrics (environment, economy, and society) often contradict each other, maximising the sustainability performance of highway pavements is difficult, especially at the network level. This study developed a novel multi-objective heuristic algorithm to formulate sustainable highway pavement network maintenance plans considering carbon emissions (CE), life cycle agency cost (LCAC), and pavement long-term performance (LTP). The proposed algorithm is a new variant of multi-objective differential evolution (MODE) that incorporates self-adaptive parameter control and hybrid mutation strategies embedded in its framework (MOSHDE). Three state-of-the-art multi-objective heuristics, namely, the non-dominated sorting genetic algorithm II(NSGA-II), classic MODE, and multi-objective particle swarm optimisation (MOPSO), as well as the proposed MOSHDE, were applied to an existing highway pavement network in China for performance evaluation. Compared with other heuristic algorithms, the proposed self-adaptive parameter control strategy enables the automatic adjustment of the control parameters, avoiding the time-consuming process of selecting them and enhancing the robustness and applicability of differential evolution. The hybrid mutation strategy uses both exploration and exploitation operators for the mutation operations, thus leveraging both global and local searches. The results of the numerical experiment demonstrate that MOSHDE outperforms the other tested heuristics in terms of efficiency and quality and diversity of the obtained approximate Pareto set. The optimal solutions obtained by the proposed method correspond to a proactive maintenance policy, as opposed to the reactive maintenance policy commonly adopted in current practice. In addition, these solutions are more cost-effective and environmentally friendly and can provide better pavement performance to highway users over the project life cycle. Therefore, the proposed MOSHDE may help practitioners in the transportation sector make their highway infrastructure more sustainable

An Automatic Generation Method of Finite Element Model Based on BIM and Ontology

For the mechanical analysis work in the structural design phase, data conversion and information transfer between BIM model and finite element model have become the main factors limiting its efficiency and quality, with the development of BIM (building information modeling) technology application in the whole life cycle. The combined application of BIM and ontology technology has promoted the automation of compliance checking, cost management, green building evaluation, and many other fields. Based on OpenBIM, this study combines IFC (Industry Foundation Classes) and the ontology system and proposes an automatic generation method for converting BIM to the finite element model. Firstly, the elements contained in the finite element model are generalized and the information set requirement, to be extracted or inferred from BIM for the generation of the finite element model, is obtained accordingly. Secondly, the information extraction technical route is constructed to satisfy the acquisition of the information set, including three main aspects, i.e., IFC-based material information, spatial information, and other basic information; ontology-based finite element cell selection method; and APDL statement generation methods based on JAVA, C#, etc. Finally, a complete technical route and a software architecture, designed for converting BIM to the finite element model, are derived. To assess the feasibility of the method, a simple structure is tested in this paper, and the result indicates that the automatic decision-making reasoning mechanism of constructing element type and meshing method can be explored by ontology and IFC. This study contributes to the body of knowledge by providing an efficient method for automatic generation of the BIM structure model and a reference for future applications using BIM in structural analysis

Self-Supervised Continual Graph Learning in Adaptive Riemannian Spaces

Continual graph learning routinely finds its role in a variety of real-world applications where the graph data with different tasks come sequentially. Despite the success of prior works, it still faces great challenges. On the one hand, existing methods work with the zero-curvature Euclidean space, and largely ignore the fact that curvature varies over the coming graph sequence. On the other hand, continual learners in the literature rely on abundant labels, but labeling graph in practice is particularly hard especially for the continuously emerging graphs on-the-fly. To address the aforementioned challenges, we propose to explore a challenging yet practical problem, the self-supervised continual graph learning in adaptive Riemannian spaces. In this paper, we propose a novel self-supervised Riemannian Graph Continual Learner (RieGrace). In RieGrace, we first design an Adaptive Riemannian GCN (AdaRGCN), a unified GCN coupled with a neural curvature adapter, so that Riemannian space is shaped by the learnt curvature adaptive to each graph. Then, we present a Label-free Lorentz Distillation approach, in which we create teacher-student AdaRGCN for the graph sequence. The student successively performs intra-distillation from itself and inter-distillation from the teacher so as to consolidate knowledge without catastrophic forgetting. In particular, we propose a theoretically grounded Generalized Lorentz Projection for the contrastive distillation in Riemannian space. Extensive experiments on the benchmark datasets show the superiority of RieGrace, and additionally, we investigate on how curvature changes over the graph sequence.Comment: Accepted by AAAI 2023 (Main Track), 9 pages, 4 figure

Drug Target Prediction Based on the Herbs Components: The Study on the Multitargets Pharmacological Mechanism of Qishenkeli Acting on the Coronary Heart Disease

In this paper, we present a case study of Qishenkeli (QSKL) to research TCM's underlying molecular mechanism, based on drug target prediction and analyses of TCM chemical components and following experimental validation. First, after determining the compositive compounds of QSKL, we use drugCIPHER-CS to predict their potential drug targets. These potential targets are significantly enriched with known cardiovascular disease-related drug targets. Then we find these potential drug targets are significantly enriched in the biological processes of neuroactive ligand-receptor interaction, aminoacyl-tRNA biosynthesis, calcium signaling pathway, glycine, serine and threonine metabolism, and renin-angiotensin system (RAAS), and so on. Then, animal model of coronary heart disease (CHD) induced by left anterior descending coronary artery ligation is applied to validate predicted pathway. RAAS pathway is selected as an example, and the results show that QSKL has effect on both rennin and angiotensin II receptor (AT1R), which eventually down regulates the angiotensin II (AngII). Bioinformatics combing with experiment verification can provide a credible and objective method to understand the complicated multitargets mechanism for Chinese herbal formula

An Automatic Generation Method of Finite Element Model Based on BIM and Ontology

For the mechanical analysis work in the structural design phase, data conversion and information transfer between BIM model and finite element model have become the main factors limiting its efficiency and quality, with the development of BIM (building information modeling) technology application in the whole life cycle. The combined application of BIM and ontology technology has promoted the automation of compliance checking, cost management, green building evaluation, and many other fields. Based on OpenBIM, this study combines IFC (Industry Foundation Classes) and the ontology system and proposes an automatic generation method for converting BIM to the finite element model. Firstly, the elements contained in the finite element model are generalized and the information set requirement, to be extracted or inferred from BIM for the generation of the finite element model, is obtained accordingly. Secondly, the information extraction technical route is constructed to satisfy the acquisition of the information set, including three main aspects, i.e., IFC-based material information, spatial information, and other basic information; ontology-based finite element cell selection method; and APDL statement generation methods based on JAVA, C#, etc. Finally, a complete technical route and a software architecture, designed for converting BIM to the finite element model, are derived. To assess the feasibility of the method, a simple structure is tested in this paper, and the result indicates that the automatic decision-making reasoning mechanism of constructing element type and meshing method can be explored by ontology and IFC. This study contributes to the body of knowledge by providing an efficient method for automatic generation of the BIM structure model and a reference for future applications using BIM in structural analysis

The goose genome sequence leads to insights into the evolution of waterfowl and susceptibility to fatty liver

BACKGROUND: Geese were domesticated over 6,000 years ago, making them one of the first domesticated poultry. Geese are capable of rapid growth, disease resistance, and high liver lipid storage capacity, and can be easily fed coarse fodder. Here, we sequence and analyze the whole-genome sequence of an economically important goose breed in China and compare it with that of terrestrial bird species. RESULTS: A draft sequence of the whole-goose genome was obtained by shotgun sequencing, and 16,150 protein-coding genes were predicted. Comparative genomics indicate that significant differences occur between the goose genome and that of other terrestrial bird species, particularly regarding major histocompatibility complex, Myxovirus resistance, Retinoic acid-inducible gene I, and other genes related to disease resistance in geese. In addition, analysis of transcriptome data further reveals a potential molecular mechanism involved in the susceptibility of geese to fatty liver disease and its associated symptoms, including high levels of unsaturated fatty acids and low levels of cholesterol. The results of this study show that deletion of the goose lep gene might be the result of positive selection, thus allowing the liver to adopt energy storage mechanisms for long-distance migration. CONCLUSIONS: This is the first report describing the complete goose genome sequence and contributes to genomic resources available for studying aquatic birds. The findings in this study are useful not only for genetic breeding programs, but also for studying lipid metabolism disorders. ELECTRONIC SUPPLEMENTARY MATERIAL: The online version of this article (doi:10.1186/s13059-015-0652-y) contains supplementary material, which is available to authorized users