arXiv subjects

Yufeng Zhang

Publications and source records attributed to Yufeng Zhang.

At least 91 records · Page 5Linked to original sources

arXiv2021

Substrate effect on thermal conductivity of monolayer WS2: Experimental measurement and theoretical analysis

Monolayer WS2 has been a competitive candidate in electrical and optoelectronic devices due to its superior optoelectronic properties. To tackle the challenge of thermal management caused by the decreased size and concentrated heat in modern ICs, it is of great significance to accurately characterize the thermal conductivity of the monolayer WS2, especially with substrate supported. In this work, the dual-wavelength flash Raman method is used to experimentally measure the thermal conductivity of the suspended and the Si/SiO2 substrate supported monolayer WS2 at a temperature range of 200 K - 400 K. The room-temperature thermal conductivity of suspended and supported WS2 are 28.45 W/mK and 15.39 W/mK, respectively, with a ~50% reduction due to substrate effect. To systematically study the underlying mechanism behind the striking reduction, we employed the Raman spatial mapping analysis combined with the molecular dynamics simulation. The analysis of Raman spectra showed the increase of doping level, reduction of phonon lifetime and suppression of out-of-plane vibration mode due to substrate effect. In addition, the phonon transmission coefficient was mutually verified with Raman spectra analysis and further revealed that the substrate effect significantly enhances the phonon scattering at the interface and mainly suppresses the acoustic phonon, thus leading to the reduction of thermal conductivity. The thermal conductivity of other suspended and supported monolayer TMDCs (e.g. MoS2, MoSe2 and WSe2) were also listed for comparison. Our researches can be extended to understand the substrate effect of other 2D TMDCs and provide guidance for future TMDCs-based electrical and optoelectronic devices.

cond-mat.mes-hall↗

arXiv2021

Provably Efficient Generative Adversarial Imitation Learning for Online and Offline Setting with Linear Function Approximation

In generative adversarial imitation learning (GAIL), the agent aims to learn a policy from an expert demonstration so that its performance cannot be discriminated from the expert policy on a certain predefined reward set. In this paper, we study GAIL in both online and offline settings with linear function approximation, where both the transition and reward function are linear in the feature maps. Besides the expert demonstration, in the online setting the agent can interact with the environment, while in the offline setting the agent only accesses an additional dataset collected by a prior. For online GAIL, we propose an optimistic generative adversarial policy optimization algorithm (OGAP) and prove that OGAP achieves $\widetilde{\mathcal{O}}(H^2 d^{3/2}K^{1/2}+KH^{3/2}dN_1^{-1/2})$ regret. Here $N_1$ represents the number of trajectories of the expert demonstration, $d$ is the feature dimension, and $K$ is the number of episodes. For offline GAIL, we propose a pessimistic generative adversarial policy optimization algorithm (PGAP). For an arbitrary additional dataset, we obtain the optimality gap of PGAP, achieving the minimax lower bound in the utilization of the additional dataset. Assuming sufficient coverage on the additional dataset, we show that PGAP achieves $\widetilde{\mathcal{O}}(H^{2}dK^{-1/2} +H^2d^{3/2}N_2^{-1/2}+H^{3/2}dN_1^{-1/2} \ )$ optimality gap. Here $N_2$ represents the number of trajectories of the additional dataset with sufficient coverage.

cs.LG↗

arXiv2021

Deep Active Learning for Text Classification with Diverse Interpretations

Recently, Deep Neural Networks (DNNs) have made remarkable progress for text classification, which, however, still require a large number of labeled data. To train high-performing models with the minimal annotation cost, active learning is proposed to select and label the most informative samples, yet it is still challenging to measure informativeness of samples used in DNNs. In this paper, inspired by piece-wise linear interpretability of DNNs, we propose a novel Active Learning with DivErse iNterpretations (ALDEN) approach. With local interpretations in DNNs, ALDEN identifies linearly separable regions of samples. Then, it selects samples according to their diversity of local interpretations and queries their labels. To tackle the text classification problem, we choose the word with the most diverse interpretations to represent the whole sentence. Extensive experiments demonstrate that ALDEN consistently outperforms several state-of-the-art deep active learning methods.

cs.CL↗

arXiv2021

Nearly room temperature ferromagnetism in pressure-induced correlated metallic state of van der Waals insulator CrGeTe$_3$

A complex interplay of different energy scales involving Coulomb repulsion, spin-orbit coupling and Hund's coupling energy in two-dimensional (2D) van der Waals (vdW) material produces novel emerging physical state. For instance, ferromagnetism in vdW charge transfer insulator CrGeTe$_3$, that provides a promising platform to simultaneously manipulate the magnetic and electrical properties for potential device implementation using few layers thick materials. Here, we show a continuous tuning of magnetic and electrical properties of CrGeTe$_3$ single crystal using pressure. With application of pressure, CrGeTe$_3$ transforms from a FM insulator with Curie temperature, $T_{\rm{C}} \sim $ 66 K at ambient condition to a correlated 2D Fermi metal with $T_{\rm{C}}$ exceeding $\sim$ 250 K. Notably, absence of an accompanying structural distortion across the insulator-metal transition (IMT) suggests that the pressure induced modification of electronic ground states are driven by electronic correlation furnishing a rare example of bandwidth-controlled IMT in a vdW material.

cond-mat.str-el↗

arXiv2021

Electronic transport properties and hydrostatic pressure effect of FeSe$_{0.67}$Te$_{0.33}$ single crystals free of phase separation

FeSe$_{1-x}$Te$_{x}$ superconductors manifest some intriguing electronic properties depending on the value of $x$. In FeSe single crystal, the nematic phase and Dirac band structure have been observed, while topological surface superconductivity with the Majorana bound state was found in the crystal of $x \sim 0.55$. Therefore, the electronic properties of single crystals with $0 < x \leq 0.5$ are crucial for probing the evolution of those intriguing properties as well as their relations. However, this study is still left blank due to the lack of single crystals because of phase separation. Here, we report the synthesis, magnetization, electronic transport properties, and hydrostatic pressure effect of FeSe$_{0.67}$Te$_{0.33}$ single crystals free of phase separation. A structural (nematic) transition is visible at $T_{s} = 39$ K, below which the resistivity exhibits a Fermi-liquid behavior. Analysis of upper critical fields suggests that spin-paramagnetic effect should be taken into account for both $H \parallel c$ axis and $H \parallel ab$ plane. A crossover from the low-$H$ quadratic to the high-$H$ quasi-linear behavior is observed in the magnetoresistance, signifying the possible existence of Dirac-cone state. Besides, the strong temperature dependence of Hall coefficient, violation of (modified) Kohler's rule, and two-band model analysis indicate the multiband effects in FeSe$_{0.67}$Te$_{0.33}$ single crystals. Hydrostatic pressure measurements reveal that $T_{s}$ is quickly suppressed with pressure while $T_{c}$ is monotonically increased up to 2.31 GPa, indicating the competition between nematicity and superconductivity. No signature of magnetic order that has been detected in FeSe$_{1-x}$S$_{x}$ is observed. Our findings fill up the blank of the knowledge on the basic properties of FeSe$_{1-x}$Te$_{x}$ system with low-Te concentrations.

cond-mat.supr-con↗

arXiv2021

Hydrothermal synthesis and complete phase diagram of FeSe$_{1-x}$S$_{x}$ $(0 \leq x \leq 1)$ single crystals

We report the successful synthesis of FeSe$_{1-x}$S$_{x}$ single crystals with $x$ ranging from 0 to 1 via a hydrothermal method. A complete phase diagram of FeSe$_{1-x}$S$_{x}$ has been obtained based on resistivity and magnetization measurements. The nematicity is suppressed with increasing $x$, and a small superconducting dome appears within the nematic phase. Outside the nematic phase, the superconductivity is continuously suppressed and reaches a minimum $T_c$ at $x$ = 0.45; beyond this point, $T_c$ slowly increases until $x$ = 1. Intriguingly, an anomalous resistivity upturn with a characteristic temperature $T^*$ in the intermediate region of $0.31 \leq x \leq 0.71$ is observed. $T^{*}$ shows a dome-like behavior with a maximum value at $x$ = 0.45, which is opposite the evolution of $T_c$, indicating competition between $T^*$ and superconductivity. The origin of $T^*$ is discussed in detail. Furthermore, the normal state resistivity evolves from non-Fermi-liquid to Fermi-liquid behavior with S doping at low temperatures, accompanied by a reduction in electronic correlations. Our study addresses the lack of single crystals in the high-S doping region and provides a complete phase diagram, which will promote the study of relations among nematicity, superconductivity, and magnetism.

cond-mat.supr-con↗

arXiv2021

Prevalent Behavior of Smooth Strongly Monotone Discrete-Time Dynamical Systems

For C1-smooth strongly monotone discrete-time dynamical systems, it is shown that ``convergence to linearly stable cycles" is a prevalent asymptotic behavior in the measuretheoretic sense. The results are then applied to classes of time-periodic parabolic equations and give new results on prevalence of convergence to periodic solutions. In particular, for equations with Neumann boundary conditions on convex domains, we show the prevalence of the set of initial conditions corresponding to the solutions that converge to spatiallyhomogeneous periodic solutions. While, for equations on radially symmetric domains, we obtain the prevalence of the set of initial values corresponding to solutions that are asymptotic to radially symmetric periodic solutions.

math.DS↗

arXiv2021

A Graph-based Relevance Matching Model for Ad-hoc Retrieval

To retrieve more relevant, appropriate and useful documents given a query, finding clues about that query through the text is crucial. Recent deep learning models regard the task as a term-level matching problem, which seeks exact or similar query patterns in the document. However, we argue that they are inherently based on local interactions and do not generalise to ubiquitous, non-consecutive contextual relationships. In this work, we propose a novel relevance matching model based on graph neural networks to leverage the document-level word relationships for ad-hoc retrieval. In addition to the local interactions, we explicitly incorporate all contexts of a term through the graph-of-word text format. Matching patterns can be revealed accordingly to provide a more accurate relevance score. Our approach significantly outperforms strong baselines on two ad-hoc benchmarks. We also experimentally compare our model with BERT and show our advantages on long documents.

cs.IR↗

arXiv2020

Phonon Magic Angle in Two-Dimensional Puckered Homostructures

The emergence of twistronics provides an unprecedented platform to modulate the band structure, resulting in exotic electronic phenomena ranging from ferromagnetism to superconductivity. However, such concept on phonon engineering is still lacking. Here, we extend the 'twistnonics' to 2D puckered materials with a 'phonon magic angle' discovered by molecular dynamics simulation. The phonon magic angle, with the TP-1 and TP-2 direction overlapped, remains a high level or even enhances phonon transport capability due to van der Waals confinement. This novel phenomenon originates from the confined vdW interaction and ordered atomic vibration caused by the perfect lattice arrangement that the atoms of the top layer can be stuck to the spaces of the bottom layer. Moreover, it is found that both the in-plane and out-of-plane thermal transport properties can be effectively regulated by applying the twist. Through the phononic and electronic analysis, the deterioration of phonon transport capability for other twist angles are attributed to the suppression of acoustic phonon modes, reduction of phonon lifetimes and mismatched lattice vibration between layers. Our findings shed light on the twistnonics of low-dimensional asymmetrical materials and can be further extended to electronic and photonic devices.

cond-mat.mes-hall↗

arXiv2020

Generative Adversarial Imitation Learning with Neural Networks: Global Optimality and Convergence Rate

Generative adversarial imitation learning (GAIL) demonstrates tremendous success in practice, especially when combined with neural networks. Different from reinforcement learning, GAIL learns both policy and reward function from expert (human) demonstration. Despite its empirical success, it remains unclear whether GAIL with neural networks converges to the globally optimal solution. The major difficulty comes from the nonconvex-nonconcave minimax optimization structure. To bridge the gap between practice and theory, we analyze a gradient-based algorithm with alternating updates and establish its sublinear convergence to the globally optimal solution. To the best of our knowledge, our analysis establishes the global optimality and convergence rate of GAIL with neural networks for the first time.

cs.LG↗

arXiv2020

Every Document Owns Its Structure: Inductive Text Classification via Graph Neural Networks

Text classification is fundamental in natural language processing (NLP), and Graph Neural Networks (GNN) are recently applied in this task. However, the existing graph-based works can neither capture the contextual word relationships within each document nor fulfil the inductive learning of new words. In this work, to overcome such problems, we propose TextING for inductive text classification via GNN. We first build individual graphs for each document and then use GNN to learn the fine-grained word representations based on their local structures, which can also effectively produce embeddings for unseen words in the new document. Finally, the word nodes are aggregated as the document embedding. Extensive experiments on four benchmark datasets show that our method outperforms state-of-the-art text classification methods.

cs.CL↗

arXiv2019

Learning to Order Sub-questions for Complex Question Answering

Answering complex questions involving multiple entities and relations is a challenging task. Logically, the answer to a complex question should be derived by decomposing the complex question into multiple simple sub-questions and then answering those sub-questions. Existing work has followed this strategy but has not attempted to optimize the order of how those sub-questions are answered. As a result, the sub-questions are answered in an arbitrary order, leading to larger search space and a higher risk of missing an answer. In this paper, we propose a novel reinforcement learning(RL) approach to answering complex questions that can learn a policy to dynamically decide which sub-question should be answered at each stage of reasoning. We lever-age the expected value-variance criterion to enable the learned policy to balance between the risk and utility of answering a sub-question. Experiment results show that the RL approach can substantially improve the optimality of ordering the sub-questions, leading to improved accuracy of question answering. The proposed method for learning to order sub-questions is general and can thus be potentially combined with many existing ideas for answering complex questions to enhance their performance.

cs.CL↗

arXiv2019

UniVSE: Robust Visual Semantic Embeddings via Structured Semantic Representations

We propose Unified Visual-Semantic Embeddings (UniVSE) for learning a joint space of visual and textual concepts. The space unifies the concepts at different levels, including objects, attributes, relations, and full scenes. A contrastive learning approach is proposed for the fine-grained alignment from only image-caption pairs. Moreover, we present an effective approach for enforcing the coverage of semantic components that appear in the sentence. We demonstrate the robustness of Unified VSE in defending text-domain adversarial attacks on cross-modal retrieval tasks. Such robustness also empowers the use of visual cues to resolve word dependencies in novel sentences.

cs.CV↗

arXiv2015

Blow-up phenomena for an integrable two-component Camassa-Holm system with cubic nonlinearity and peakon solutions

This paper is devoted to an integrable two-component Camassa-Holm system with cubic nonlinearity, which includes the cubic Camassa-Holm equation (also called the Fokas-Olver-Rosenau-Qiao equation) as a special case. The one peaked solitons (peakons) and two peakon solutions are described in an explicit formula. Then, the local well-posedness for the Cauchy problem of the system is studied. Moreover, we target at the precise blow-up scenario for strong solutions to the system, and establish a new blow-up result with respect to the initial data.

math.AP↗

arXiv2014

MPISE: Symbolic Execution of MPI Programs

Message Passing Interfaces (MPI) plays an important role in parallel computing. Many parallel applications are implemented as MPI programs. The existing methods of bug detection for MPI programs have the shortage of providing both input and non-determinism coverage, leading to missed bugs. In this paper, we employ symbolic execution to ensure the input coverage, and propose an on-the-fly schedule algorithm to reduce the interleaving explorations for non-determinism coverage, while ensuring the soundness and completeness. We have implemented our approach as a tool, called MPISE, which can automatically detect the deadlock and runtime bugs in MPI programs. The results of the experiments on benchmark programs and real world MPI programs indicate that MPISE finds bugs effectively and efficiently. In addition, our tool also provides diagnostic information and replay mechanism to help understanding bugs.

cs.DC↗

arXiv2012

Electronic structure and optical properties of Graphene Monoxide

The electronic and optical properties of graphene monoxide, a new type of semiconductor materials, are first theoretically studied based on density functional theory. Electronic calculations show that the band gap is 0.952 eV which indicate that graphene monoxide is a direct band gap semiconductor. The density of states of graphene monoxide and the partial density of states for C and O are given to understand the electronic structure. In addition, we calculate the optical properties of graphene monoxide including the complex dielectric function, absorption coefficient, the complex refractive index, loss-function, reflectivity and conductivity. These results provide a physical basis for the potential applications in optoelectronic devices.

cond-mat.mtrl-sci↗

arXiv2012

Speculative Symbolic Execution

Symbolic execution is an effective path oriented and constraint based program analysis technique. Recently, there is a significant development in the research and application of symbolic execution. However, symbolic execution still suffers from the scalability problem in practice, especially when applied to large-scale or very complex programs. In this paper, we propose a new fashion of symbolic execution, named Speculative Symbolic Execution (SSE), to speed up symbolic execution by reducing the invocation times of constraint solver. In SSE, when encountering a branch statement, the search procedure may speculatively explore the branch without regard to the feasibility. Constraint solver is invoked only when the speculated branches are accumulated to a specified number. In addition, we present a key optimization technique that enhances SSE greatly. We have implemented SSE and the optimization technique on Symbolic Pathfinder (SPF). Experimental results on six programs show that, our method can reduce the invocation times of constraint solver by 21% to 49% (with an average of 30%), and save the search time from 23.6% to 43.6% (with an average of 30%).

cs.SE↗

arXiv2006

Semi-direct sums of Lie algebras and discrete integrable couplings

A relation between semi-direct sums of Lie algebras and integrable couplings of lattice equations is established, and a practicable way to construct integrable couplings is further proposed. An application of the resulting general theory to the generalized Toda spectral problem yields two classes of integrable couplings for the generalized Toda hierarchy of lattice equations. The construction of integrable couplings using semi-direct sums of Lie algebras provides a good source of information on complete classification of integrable lattice equations.

nlin.SI↗