arXiv subjects

Yi Du

Publications and source records attributed to Yi Du.

At least 73 records · Page 4Linked to original sources

arXiv2023

Hierarchical Interdisciplinary Topic Detection Model for Research Proposal Classification

The peer merit review of research proposals has been the major mechanism for deciding grant awards. However, research proposals have become increasingly interdisciplinary. It has been a longstanding challenge to assign interdisciplinary proposals to appropriate reviewers, so proposals are fairly evaluated. One of the critical steps in reviewer assignment is to generate accurate interdisciplinary topic labels for proposal-reviewer matching. Existing systems mainly collect topic labels manually generated by principal investigators. However, such human-reported labels can be non-accurate, incomplete, labor intensive, and time costly. What role can AI play in developing a fair and precise proposal reviewer assignment system? In this study, we collaborate with the National Science Foundation of China to address the task of automated interdisciplinary topic path detection. For this purpose, we develop a deep Hierarchical Interdisciplinary Research Proposal Classification Network (HIRPCN). Specifically, we first propose a hierarchical transformer to extract the textual semantic information of proposals. We then design an interdisciplinary graph and leverage GNNs for learning representations of each discipline in order to extract interdisciplinary knowledge. After extracting the semantic and interdisciplinary knowledge, we design a level-wise prediction component to fuse the two types of knowledge representations and detect interdisciplinary topic paths for each proposal. We conduct extensive experiments and expert evaluations on three real-world datasets to demonstrate the effectiveness of our proposed model.

cs.IR↗

arXiv2023

High-dimensional Automated Radiation Therapy Treatment Planning via Bayesian Optimization

Radiation therapy treatment planning can be viewed as an iterative hyperparameter tuning process to balance conflicting clinical goals. In this work, we investigated the performance of modern Bayesian Optimization (BO) methods on automated treatment planning problems in high-dimensional settings. 20 locally advanced rectal cancer patients treated with intensity-modulated radiation therapy (IMRT) were retrospectively selected as test cases. We implemented an automated treatment planning framework that adjusts dose objectives and weights simultaneously, and tested the performance of two BO methods on the treatment planning task: one standard BO method (GPEI) and one BO method dedicated to high-dimensional problems (SAAS-BO). A random tuning method was also included as the baseline. We compared and analyzed the three automated methods' plan quality and planning efficiency and the different search patterns of the two BO methods. For the target structures, the SAAS-BO plans achieved comparable hot spot control ($p=0.43$) and homogeneity ($p=0.96$) with the clinical plans, significantly better than the GPEI and random plans ($p<0.05$). Both SAAS-BO and GPEI plans significantly outperformed the clinical plans in conformity and dose spillage ($p<0.05$). Compared with clinical plans, the treatment plans generated by the three automated methods all made reductions in evaluated dosimetric indices for the femoral head and the bladder. The analysis of the underlying predictive models has shown that both BO procedures have identified similar important planning parameters. This work implemented a BO-based hyperparameter tuning framework for automated treatment planning. Both tested BO methods were able to produce high-quality treatment plans and the model analysis also confirmed the intrinsic low dimensionality of the tested treatment planning problems.

physics.med-ph↗

arXiv2023

NEEDED: Introducing Hierarchical Transformer to Eye Diseases Diagnosis

With the development of natural language processing techniques(NLP), automatic diagnosis of eye diseases using ophthalmology electronic medical records (OEMR) has become possible. It aims to evaluate the condition of both eyes of a patient respectively, and we formulate it as a particular multi-label classification task in this paper. Although there are a few related studies in other diseases, automatic diagnosis of eye diseases exhibits unique characteristics. First, descriptions of both eyes are mixed up in OEMR documents, with both free text and templated asymptomatic descriptions, resulting in sparsity and clutter of information. Second, OEMR documents contain multiple parts of descriptions and have long document lengths. Third, it is critical to provide explainability to the disease diagnosis model. To overcome those challenges, we present an effective automatic eye disease diagnosis framework, NEEDED. In this framework, a preprocessing module is integrated to improve the density and quality of information. Then, we design a hierarchical transformer structure for learning the contextualized representations of each sentence in the OEMR document. For the diagnosis part, we propose an attention-based predictor that enables traceable diagnosis by obtaining disease-specific information. Experiments on the real dataset and comparison with several baseline models show the advantage and explainability of our framework.

cs.CL↗

arXiv2022

Strain-induced pseudo-magnetic field in $α-{\cal T}_3$ lattice

We investigate the effects of a nonuniform uniaxial strain and a triaxial strain on the $α-{\cal T}_3$ lattice. The analytical expressions of the pseudo-Landau levels (pLLs) are derived based on low-energy Hamiltonians, and are verified by tight-binding calculations. We find the pseudo-magnetic field leads to the oscillating density of states, and the first pLL is sublattice polarized, which is distributed on only two of the three sets of sublattices. For the nonuniform uniaxial strain, we show that pLLs become dispersive due to the renormalization of the Fermi velocity, and a valley polarized current emerges. Our results generalize the study of pseudo-magnetic field to the $α-{\cal T}_3$ lattice, which will not only deepen the understanding of the intriguing effects of mechanical strains, but also provide theoretical foundation for possible experimental studies of the effects of strain on the $α-{\cal T}_3$ lattice.

cond-mat.mes-hall↗

arXiv2022

Who Should Review Your Proposal? Interdisciplinary Topic Path Detection for Research Proposals

The peer merit review of research proposals has been the major mechanism to decide grant awards. Nowadays, research proposals have become increasingly interdisciplinary. It has been a longstanding challenge to assign proposals to appropriate reviewers. One of the critical steps in reviewer assignment is to generate accurate interdisciplinary topic labels for proposals. Existing systems mainly collect topic labels manually reported by discipline investigators. However, such human-reported labels can be non-accurate and incomplete. What role can AI play in developing a fair and precise proposal review system? In this evidential study, we collaborate with the National Science Foundation of China to address the task of automated interdisciplinary topic path detection. For this purpose, we develop a deep Hierarchical Interdisciplinary Research Proposal Classification Network (HIRPCN). We first propose a hierarchical transformer to extract the textual semantic information of proposals. We then design an interdisciplinary graph and leverage GNNs to learn representations of each discipline in order to extract interdisciplinary knowledge. After extracting the semantic and interdisciplinary knowledge, we design a level-wise prediction component to fuse the two types of knowledge representations and detect interdisciplinary topic paths for each proposal. We conduct extensive experiments and expert evaluations on three real-world datasets to demonstrate the effectiveness of our proposed model.

cs.CL↗

arXiv2022

RPT: Toward Transferable Model on Heterogeneous Researcher Data via Pre-Training

With the growth of the academic engines, the mining and analysis acquisition of massive researcher data, such as collaborator recommendation and researcher retrieval, has become indispensable. It can improve the quality of services and intelligence of academic engines. Most of the existing studies for researcher data mining focus on a single task for a particular application scenario and learning a task-specific model, which is usually unable to transfer to out-of-scope tasks. The pre-training technology provides a generalized and sharing model to capture valuable information from enormous unlabeled data. The model can accomplish multiple downstream tasks via a few fine-tuning steps. In this paper, we propose a multi-task self-supervised learning-based researcher data pre-training model named RPT. Specifically, we divide the researchers' data into semantic document sets and community graph. We design the hierarchical Transformer and the local community encoder to capture information from the two categories of data, respectively. Then, we propose three self-supervised learning objectives to train the whole model. Finally, we also propose two transfer modes of RPT for fine-tuning in different scenarios. We conduct extensive experiments to evaluate RPT, results on three downstream tasks verify the effectiveness of pre-training for researcher data mining.

cs.IR↗

arXiv2022

Two-dimensional ZIF-L nanosheets as high performance non-enzymatic glucose sensor

An effective biosensor based on two-dimensional (2D) Co-ZIF-L nanosheets for sensitive electrochemical non-enzymatic glucose detection is developed, which exhibits high electrocalalytic activities towards glucose due to the ordered porous structure as well as ultrahigh specific surface area. The fabricated Co-ZIF-L nanosheets electrodes present an outstanding performance with higher sensitivity of 769.5 *10$^{-6}$ A mM$^{-1}$ cm$^{-2}$ and lower detect limit of 90.4 nM, while the constructed 3D ZIF-67 nanoparticles electrodes show a weaker sensitivity of 697.4 *10$^{-6}$ A mM$^{-1}$ cm$^{-2}$ and a limited detection range from 2 *10$^{-6}$ M to 414 *10$^{-6}$ M. Furthermore, the Co-ZIF-L based non-enzymatic glucose biosensors possess an acceptable selectivity, long-term stability as well as reproducibility. This work may offer a new approach to develop 2D ZIF nanosheets as a potential candidate in electrochemical biosensors.

physics.app-ph↗

arXiv2021

Facet Dependent Topological Phase Transition in Bi4Br4

The realization of the coexistence of various topologically nontrivial surface states in one material is expected to lay a foundation for new electric applications with selective robust spin current. Here we apply the magnetoconductivity characteristic and angle-resolved photoemission spectroscopy (ARPES) to visualize the surface-selected electronic features evolution of quasi-one-dimensional material Bi4Br4. The transport measurements indicate the quantum interference correction to conductivity possesses symbolic spin rotational characteristic correlated to the value of Berry phase with the effects of weak localization and weak antilocalization for (001) and (100) surfaces, respectively. The ARPES spectra provide the experimental evidence for quasi-one-dimensional massless Dirac surface state at the side (100) surface and anisotropic massive Dirac surface state at the top (001) surface, respectively, which is highly coincide with the angle-dependent scaling behavior of magnetoconductivity. Our results reveal the facet dependent topological phases in quasi-one-dimensional Bi4Br4, stimulating the further investigations of this dual topology classes and the applications of the feasible technologies of topological spintronics.

cond-mat.mtrl-sci↗

arXiv2021

Epitaxial growth of bilayer Bi(110) on two-dimensional ferromagnetic Fe3GeTe2

Heterostructures of two-dimensional (2D) layered materials with selective compositions play an important role in creating novel functionalities. Effective interface coupling between 2D ferromagnet and electronic materials would enable the generation of exotic physical phenomena caused by intrinsic symmetry breaking and proximity effect at interfaces. Here, epitaxial growth of bilayer Bi(110) on 2D ferromagnetic Fe3GeTe2 (FGT) with large magnetic anisotropy has been reported. Bilayer Bi(110) islands are found to extend along fixed lattice directions of FGT. The six preferred orientations could be divided into two groups of three-fold symmetry axes with the difference approximately to 26°. Moreover, dI/dV measurements confirm the existence of interface coupling between bilayer Bi(110) and FGT. A variation of the energy gap at the edges of bilayer Bi(110) is also observed which is modulated by the interface coupling strengths associated with its buckled atomic structure. This system provides a good platform for further study of the exotic electronic properties of epitaxial Bi(110) on 2D ferromagnetic substrate and promotes potential applications in the field of spin devices.

cond-mat.mtrl-sci↗

arXiv2021

Large-Gap Quantum Spin Hall State and Temperature-Induced Lifshitz Transition in Bi4Br4

Searching for new quantum spin Hall insulators with large fully opened energy gap to overcome the thermal disturbance at room temperature has attracted tremendous attention due to the one-dimensional (1D) spin-momentum locked topological edge states serving as dissipationless channels for the practical applications in low consumption electronics and high performance spintronics. Here, we report the investigation of topological nature of monolayer Bi4Br4 by the techniques of scanning tunneling microscopy and angle-resolved photoemission spectroscopy (ARPES). The topological non-triviality of 1D edge state integrals within the large bulk energy gap (~ 0.2 eV) is revealed by the first-principle calculations. The ARPES measurements at different temperature show a temperature-induced Lifshitz transition, corresponding to the resistivity anomaly caused by the shift of chemical potential. The connection between the emergency of superconductivity and the Lifshitz transition is discussed.

cond-mat.mtrl-sci↗

arXiv2021

Expert Knowledge-Guided Length-Variant Hierarchical Label Generation for Proposal Classification

To advance the development of science and technology, research proposals are submitted to open-court competitive programs developed by government agencies (e.g., NSF). Proposal classification is one of the most important tasks to achieve effective and fair review assignments. Proposal classification aims to classify a proposal into a length-variant sequence of labels. In this paper, we formulate the proposal classification problem into a hierarchical multi-label classification task. Although there are certain prior studies, proposal classification exhibit unique features: 1) the classification result of a proposal is in a hierarchical discipline structure with different levels of granularity; 2) proposals contain multiple types of documents; 3) domain experts can empirically provide partial labels that can be leveraged to improve task performances. In this paper, we focus on developing a new deep proposal classification framework to jointly model the three features. In particular, to sequentially generate labels, we leverage previously-generated labels to predict the label of next level; to integrate partial labels from experts, we use the embedding of these empirical partial labels to initialize the state of neural networks. Our model can automatically identify the best length of label sequence to stop next label prediction. Finally, we present extensive results to demonstrate that our method can jointly model partial labels, textual information, and semantic dependencies in label sequences, and, thus, achieve advanced performances.

cs.LG↗

arXiv2021

Data Augmentation for Graph Convolutional Network on Semi-Supervised Classification

Data augmentation aims to generate new and synthetic features from the original data, which can identify a better representation of data and improve the performance and generalizability of downstream tasks. However, data augmentation for graph-based models remains a challenging problem, as graph data is more complex than traditional data, which consists of two features with different properties: graph topology and node attributes. In this paper, we study the problem of graph data augmentation for Graph Convolutional Network (GCN) in the context of improving the node embeddings for semi-supervised node classification. Specifically, we conduct cosine similarity based cross operation on the original features to create new graph features, including new node attributes and new graph topologies, and we combine them as new pairwise inputs for specific GCNs. Then, we propose an attentional integrating model to weighted sum the hidden node embeddings encoded by these GCNs into the final node embeddings. We also conduct a disparity constraint on these hidden node embeddings when training to ensure that non-redundant information is captured from different features. Experimental results on five real-world datasets show that our method improves the classification accuracy with a clear margin (+2.5% - +84.2%) than the original GCN model.

cs.LG↗

arXiv2021

Superconductivity in Layered van der Waals Hydrogenated Germanene at High Pressure

Structural and superconducting transitions of layered van der Waals (vdW) hydrogenated germanene (GeH) were observed under high-pressure compression and decompression processes. GeH possesses a superconducting transition at critical temperature (Tc) of 5.41 K at 8.39 GPa. A crystalline to amorphous transition occurs at 16.80 GPa while superconductivity remains. An abnormally increased Tc up to 6.1 K has been observed in the decompression process while the GeH remained amorphous. Thorough in-situ high-pressure synchrotron X-ray diffraction and in-situ high-pressure Raman spectroscopy with the density functional theory simulations suggest that the superconductivity of GeH should be attributed to the increased density of states at the Fermi level as well as the enhanced electron-phonon coupling effect under high pressure. The decompression-driven superconductivity enhancement arises from pressure-induced phonon softening related to an in-plane Ge-Ge phonon mode. As an amorphous metal hydride superconductor, GeH provides a platform to study amorphous hydride superconductivity in layered vdW materials.

cond-mat.supr-con↗

arXiv2021

TWIST-GAN: Towards Wavelet Transform and Transferred GAN for Spatio-Temporal Single Image Super Resolution

Single Image Super-resolution (SISR) produces high-resolution images with fine spatial resolutions from aremotely sensed image with low spatial resolution. Recently, deep learning and generative adversarial networks(GANs) have made breakthroughs for the challenging task of single image super-resolution (SISR). However, thegenerated image still suffers from undesirable artifacts such as, the absence of texture-feature representationand high-frequency information. We propose a frequency domain-based spatio-temporal remote sensingsingle image super-resolution technique to reconstruct the HR image combined with generative adversarialnetworks (GANs) on various frequency bands (TWIST-GAN). We have introduced a new method incorporatingWavelet Transform (WT) characteristics and transferred generative adversarial network. The LR image hasbeen split into various frequency bands by using the WT, whereas, the transfer generative adversarial networkpredicts high-frequency components via a proposed architecture. Finally, the inverse transfer of waveletsproduces a reconstructed image with super-resolution. The model is first trained on an external DIV2 Kdataset and validated with the UC Merceed Landsat remote sensing dataset and Set14 with each image sizeof 256x256. Following that, transferred GANs are used to process spatio-temporal remote sensing images inorder to minimize computation cost differences and improve texture information. The findings are comparedqualitatively and qualitatively with the current state-of-art approaches. In addition, we saved about 43% of theGPU memory during training and accelerated the execution of our simplified version by eliminating batchnormalization layers.

eess.IV↗

arXiv2021

Resolving the Intrinsic Bandgap and Edge Effect of BiI3 Film Epitaxially Grown on Graphene

Two-dimensional materials with layered structures, appropriate bandgap, and high carrier mobilities have gathered tremendous interests due to their potential applications in optoelectronic and photovoltaic devices. Here, we report the growth of BiI3 thin film with controllable atomic thickness on graphene-terminated 6H-SiC(0001) substrate by molecular beam epitaxy (MBE) method. The growth kinetic processes and crystalline properties of the BiI3 film are studied by scanning tunneling microscopy (STM). The scanning tunneling spectroscopy (STS) reveals a bandgap of 2.8 eV for monolayer BiI3 with a weak dependence on film thickness for few-layer BiI3, which greatly exceeds the previous reported values identified by macroscopic optical measurements. This discrepancy originates from the edge effect of BiI3 that renders the bandgap downshift to 1.5 - 1.6 eV, as identified by the STS curves and the further confirmed by density functional theory (DFT) calculations. Our work provides a method to fabricate high-quality monolayer BiI3 film and resolves its intrinsic bandgap as well as the edge effect on reduction of bandgap, benefitting not only to fundamental researches but also to nanoelectronic and optoelectronic applications.

cond-mat.mtrl-sci↗

arXiv2021

Epitaxial Growth of Quasi-One-Dimensional Bismuth-Halide Chains with Topological Non-Trivial Edge States

Quantum spin Hall insulators have one-dimensional (1D) spin-momentum locked topological edge states (ES) inside the bulk band gap, which can serve as dissipationless channels for the practical applications in low consumption electronics and high performance spintronics. However, the clean and atomically sharp ES serving as ideal 1D conducting channels are still lack. Here, we report the formation of the quasi-1D Bi4I4 nanoribbons on the surface of Bi(111) with the support of the graphene-terminated 6H-SiC(0001) and the direct observations of the topological ES at the step edge by scanning tunneling microscopy and spectroscopic-imaging results. The ES reside surround the edge of Bi4I4 nanoribbons and exhibits remarkable robustness against non time reversal symmetry perturbations. The theoretical simulations verify the topological non-trivial character of 1D ES, which is retained after considering the presence of the underlying Bi(111). Our study supports the existence of topological ES in Bi4I4 nanoribbons, paving the way to engineer the novel topological features by using the nanoribbons as the 1D building block.

cond-mat.mtrl-sci↗

arXiv2021

LightCAKE: A Lightweight Framework for Context-Aware Knowledge Graph Embedding

Knowledge graph embedding (KGE) models learn to project symbolic entities and relations into a continuous vector space based on the observed triplets. However, existing KGE models cannot make a proper trade-off between the graph context and the model complexity, which makes them still far from satisfactory. In this paper, we propose a lightweight framework named LightCAKE for context-aware KGE. LightCAKE explicitly models the graph context without introducing redundant trainable parameters, and uses an iterative aggregation strategy to integrate the context information into the entity/relation embeddings. As a generic framework, it can be used with many simple KGE models to achieve excellent results. Finally, extensive experiments on public benchmarks demonstrate the efficiency and effectiveness of our framework.

cs.AI↗

arXiv2020

Context-Enhanced Entity and Relation Embedding for Knowledge Graph Completion

Most researches for knowledge graph completion learn representations of entities and relations to predict missing links in incomplete knowledge graphs. However, these methods fail to take full advantage of both the contextual information of entity and relation. Here, we extract contexts of entities and relations from the triplets which they compose. We propose a model named AggrE, which conducts efficient aggregations respectively on entity context and relation context in multi-hops, and learns context-enhanced entity and relation embeddings for knowledge graph completion. The experiment results show that AggrE is competitive to existing models.

cs.CL↗