arXiv subjects

Sheng Li

Publications and source records attributed to Sheng Li.

At least 235 records · Page 13Linked to original sources

arXiv2021

Cooperative control of perpendicular magnetic anisotropy via crystal structure and orientation in single-crystal flexible SrRuO3 membranes

Flexible magnetic materials with robust and controllable perpendicular magnetic anisotropy (PMA) are highly desirable for developing flexible high-performance spintronic devices. However, it is still challenge to fabricate PMA films through current techniques of direct deposition on polymers. Here, we report a facile method for synthesizing single-crystal freestanding SrRuO3 (SRO) membranes with controlled crystal structure and orientation using water-soluble Ca3-xSrxAl2O6 sacrificial layers. Through cooperative effect of crystal structure and orientation engineering, flexible SrRuO3 membranes reveal highly tunable magnetic anisotropy from in-plane to our-of-plane with a remarkable PMA energy of 7.34*106 erg/cm3. Based on the first-principles calculations, it reveals that the underlying mechanism of PMA modulation is intimately correlated with structure-controlled Ru 4d-orbital occupation, as well as the spin-orbital matrix element differences, dependent on the crystal orientation. In addition, there are no obvious changes of the magnetism after 10,000 bending cycles, indicating an excellent magnetism reliability in the prepared films. This work provides a feasible approach to prepare the flexible oxide films with strong and controllable PMA.

cond-mat.str-el↗

arXiv2021

Automated Graph Learning via Population Based Self-Tuning GCN

Owing to the remarkable capability of extracting effective graph embeddings, graph convolutional network (GCN) and its variants have been successfully applied to a broad range of tasks, such as node classification, link prediction, and graph classification. Traditional GCN models suffer from the issues of overfitting and oversmoothing, while some recent techniques like DropEdge could alleviate these issues and thus enable the development of deep GCN. However, training GCN models is non-trivial, as it is sensitive to the choice of hyperparameters such as dropout rate and learning weight decay, especially for deep GCN models. In this paper, we aim to automate the training of GCN models through hyperparameter optimization. To be specific, we propose a self-tuning GCN approach with an alternate training algorithm, and further extend our approach by incorporating the population based training scheme. Experimental results on three benchmark datasets demonstrate the effectiveness of our approaches on optimizing multi-layer GCN, compared with several representative baselines.

cs.LG↗

arXiv2021

Multi-Agent Cooperative Bidding Games for Multi-Objective Optimization in e-Commercial Sponsored Search

Bid optimization for online advertising from single advertiser's perspective has been thoroughly investigated in both academic research and industrial practice. However, existing work typically assume competitors do not change their bids, i.e., the wining price is fixed, leading to poor performance of the derived solution. Although a few studies use multi-agent reinforcement learning to set up a cooperative game, they still suffer the following drawbacks: (1) They fail to avoid collusion solutions where all the advertisers involved in an auction collude to bid an extremely low price on purpose. (2) Previous works cannot well handle the underlying complex bidding environment, leading to poor model convergence. This problem could be amplified when handling multiple objectives of advertisers which are practical demands but not considered by previous work. In this paper, we propose a novel multi-objective cooperative bid optimization formulation called Multi-Agent Cooperative bidding Games (MACG). MACG sets up a carefully designed multi-objective optimization framework where different objectives of advertisers are incorporated. A global objective to maximize the overall profit of all advertisements is added in order to encourage better cooperation and also to protect self-bidding advertisers. To avoid collusion, we also introduce an extra platform revenue constraint. We analyze the optimal functional form of the bidding formula theoretically and design a policy network accordingly to generate auction-level bids. Then we design an efficient multi-agent evolutionary strategy for model optimization. Offline experiments and online A/B tests conducted on the Taobao platform indicate both single advertiser's objective and global profit have been significantly improved compared to state-of-art methods.

cs.AI↗

arXiv2021

Safe reopening of university campuses is possible with COVID-19 vaccination

We construct an agent-based SEIR model to simulate COVID-19 spread at a 16000-student mostly non-residential urban university during the Fall 2021 Semester. We find that mRNA vaccine coverage above 80% makes it possible to safely reopen to in-person instruction. If vaccine coverage is 100%, then our model indicates that facemask use is not necessary. Our simulations with vaccine coverage below 70% exhibit a right-skew for total infections over the semester, which suggests that high levels of infection are not exceedingly rare with campus social connections the main transmission route. Less effective vaccines or incidence of new variants may require additional intervention such as screening testing to reopen safely.

q-bio.PE↗

arXiv2021

Matching in Selective and Balanced Representation Space for Treatment Effects Estimation

The dramatically growing availability of observational data is being witnessed in various domains of science and technology, which facilitates the study of causal inference. However, estimating treatment effects from observational data is faced with two major challenges, missing counterfactual outcomes and treatment selection bias. Matching methods are among the most widely used and fundamental approaches to estimating treatment effects, but existing matching methods have poor performance when facing data with high dimensional and complicated variables. We propose a feature selection representation matching (FSRM) method based on deep representation learning and matching, which maps the original covariate space into a selective, nonlinear, and balanced representation space, and then conducts matching in the learned representation space. FSRM adopts deep feature selection to minimize the influence of irrelevant variables for estimating treatment effects and incorporates a regularizer based on the Wasserstein distance to learn balanced representations. We evaluate the performance of our FSRM method on three datasets, and the results demonstrate superiority over the state-of-the-art methods.

stat.ML↗

arXiv2021

Graph Infomax Adversarial Learning for Treatment Effect Estimation with Networked Observational Data

Treatment effect estimation from observational data is a critical research topic across many domains. The foremost challenge in treatment effect estimation is how to capture hidden confounders. Recently, the growing availability of networked observational data offers a new opportunity to deal with the issue of hidden confounders. Unlike networked data in traditional graph learning tasks, such as node classification and link detection, the networked data under the causal inference problem has its particularity, i.e., imbalanced network structure. In this paper, we propose a Graph Infomax Adversarial Learning (GIAL) model for treatment effect estimation, which makes full use of the network structure to capture more information by recognizing the imbalance in network structure. We evaluate the performance of our GIAL model on two benchmark datasets, and the results demonstrate superiority over the state-of-the-art methods.

cs.LG↗

arXiv2021

Room-Temperature Topological Phase Transition in Quasi-One-Dimensional Material Bi$_4$I$_4$

Quasi-one-dimensional (1D) materials provide a superior platform for characterizing and tuning topological phases for two reasons: i) existence for multiple cleavable surfaces that enables better experimental identification of topological classification, and ii) stronger response to perturbations such as strain for tuning topological phases compared to higher dimensional crystal structures. In this paper, we present experimental evidence for a room-temperature topological phase transition in the quasi-1D material Bi$_4$I$_4$, mediated via a first order structural transition between two distinct stacking orders of the weakly-coupled chains. Using high resolution angle-resolved photoemission spectroscopy on the two natural cleavable surfaces, we identify the high temperature $β$ phase to be the first weak topological insulator with gapless Dirac cones on the (100) surface and no Dirac crossing on the (001) surface, while in the low temperature $α$ phase, the topological surface state on the (100) surface opens a gap, consistent with a recent theoretical prediction of a higher-order topological insulator beyond the scope of the established topological materials databases that hosts gapless hinge states. Our results not only identify a rare topological phase transition between first-order and second-order topological insulators but also establish a novel quasi-1D material platform for exploring unprecedented physics.

cond-mat.mtrl-sci↗

arXiv2021

Designation of Intra-layer and Intercalated High Entropy Quasi-2D Compounds

Here, we designed two promising schemes to realize the high-entropy structure in a series of quasi-two-dimensional compounds, transition metal dichalcogenides (TMDCs). In the intra-layer high-entropy plan, (HEM)X2 compounds with high-entropy structure in the MX2 slabs were obtained, here HEM means high-entropy metals, such as TiZrNbMoTa. And superconductivity with a Tc~7.4 K was found in a Mo-rich HEMX2. On the other hand, in the intercalation plan, we intercalated HEM-atoms (FeCoCrNiMn) into the gap between the sandwiched-MX2 slabs resulting in a series of (HEM)xMX2 compounds, x in the range of 0~0.5, in which HEM is mainly composed of 3d transition metal elements, such as FeCoCrNiMn. As the introduction of multi-component magnetic atoms, ferromagnetic spin-glass states with strong 2D characteristics ensued. Tuning the x content, three kinds of two in the high-entropy intercalated layer were observed including the 1*1 triangular lattice and two kinds of superlattices \sqrt3*\sqrt3 and \sqrt3*2 in x=0.333 and x>0.5, respectively. Meanwhile, the spin frustration in the two-dimensional high-entropy magnetic plane will be enhanced with the development of \sqrt3*\sqrt3 and will be reduced significantly when changing into the \sqrt3*2 phase. The high-entropy TMDCs and versatile two-dimensional high-entropy structures found by us possess great potentials to find new physics in low-dimensional high-entropy structures and future applications.

cond-mat.mtrl-sci↗

arXiv2021

Edge: Enriching Knowledge Graph Embeddings with External Text

Knowledge graphs suffer from sparsity which degrades the quality of representations generated by various methods. While there is an abundance of textual information throughout the web and many existing knowledge bases, aligning information across these diverse data sources remains a challenge in the literature. Previous work has partially addressed this issue by enriching knowledge graph entities based on "hard" co-occurrence of words present in the entities of the knowledge graphs and external text, while we achieve "soft" augmentation by proposing a knowledge graph enrichment and embedding framework named Edge. Given an original knowledge graph, we first generate a rich but noisy augmented graph using external texts in semantic and structural level. To distill the relevant knowledge and suppress the introduced noise, we design a graph alignment term in a shared embedding space between the original graph and augmented graph. To enhance the embedding learning on the augmented graph, we further regularize the locality relationship of target entity based on negative sampling. Experimental results on four benchmark datasets demonstrate the robustness and effectiveness of Edge in link prediction and node classification.

cs.CL↗

arXiv2021

Prediction of three-phase relative permeabilities of Berea sandstone using lattice Boltzmann method

Three-phase flows through a pore network of Berea sandstone are studied numerically under critical interfacial tension condition. Results show that the relative permeability of each fluid increases as its own saturation increases. The specific interfacial length between wetting and non-wetting fluids monotonously decreases with increasing the saturation of intermediate-wetting fluid, while the other two specific interfacial lengths exhibit a non-monotonous variation. As the wetting (non-wetting) fluid becomes less wetting (non-wetting), the relative permeability of wetting fluid monotonously increases, while the other two relative permeabilities show a non-monotonous trend. Due to the presence of spreading layer, the specific interfacial length between wetting and non-wetting fluids always stabilizes at a low level. As the viscosity ratio of wetting (non-wetting) to intermediate-wetting fluids increases, the relative permeability of wetting (non-wetting) fluid increases. With the viscosity ratio deviating from unity, the phase interfaces become increasingly unstable, leading to an increased specific interfacial length.

physics.flu-dyn↗

arXiv2021

New polymorphic phase and rich phase diagram in the PdSe2-xTex system

We report a combined experimental and theoretical study of the PdSe2-xTex system. With increasing Te fraction, structural evolutions, first from an orthorhombic phase (space group Pbca) to a monoclinic phase (space group C2/c) and then to a trigonal phase (space group P-3m1), are observed accompanied with clearly distinct electrical transport behavior. The monoclinic phase (C2/c) is a completely new polymorphic phase and is discovered within a narrow range of Te composition (0.3 \leq x \leq 0.8). This phase has a different packing sequence from all known transition metal dichalcogenides to date. Electronic calculations and detailed transport analysis of the new polymorphic PdSe1.3Te0.7 phase are presented. In the trigonal phase region, superconductivity with enhanced critical temperature is also observed within a narrow range of Te content (1.0 \leq x \leq 1.2). The rich phase diagram, new polymorphic structure as well as abnormally enhanced superconductivity could further stimulate more interest to explore new types of polymorphs and investigate their transport and electronic properties in the transition metal dichalcogenides family that are of significant interest.

cond-mat.supr-con↗

arXiv2021

Enhanced Superconductivity in the Se-substituted 1T-PdTe$_2$

Two-dimensional transition metal dichalcogenide PdTe$_2$ recently attracts much attention due to its phase coexistence of type-II Dirac semimetal and type-I superconductivity. Here we report a 67 % enhancement of superconducting transition temperature in the 1T-PdSeTe in comparison to that of PdTe2 through partial substitution of Te atoms by Se. The superconductivity has been unambiguously confirmed by the magnetization, resistivity and specific heat measurements. 1T-PdSeTe shows type-II superconductivity with large anisotropy and non-bulk superconductivity nature with volume fraction ~ 20 % estimated from magnetic and heat capacity measurements. 1T-PdSeTe expands the family of superconducting transition metal dichalcogenides and thus provides additional insights for understanding superconductivity and topological physics in the 1T-PdTe$_2$ system

cond-mat.supr-con↗

arXiv2021

Learning Emergent Discrete Message Communication for Cooperative Reinforcement Learning

Communication is a important factor that enables agents work cooperatively in multi-agent reinforcement learning (MARL). Most previous work uses continuous message communication whose high representational capacity comes at the expense of interpretability. Allowing agents to learn their own discrete message communication protocol emerged from a variety of domains can increase the interpretability for human designers and other agents.This paper proposes a method to generate discrete messages analogous to human languages, and achieve communication by a broadcast-and-listen mechanism based on self-attention. We show that discrete message communication has performance comparable to continuous message communication but with much a much smaller vocabulary size.Furthermore, we propose an approach that allows humans to interactively send discrete messages to agents.

cs.LG↗

arXiv2021

Searching for Fast Model Families on Datacenter Accelerators

Neural Architecture Search (NAS), together with model scaling, has shown remarkable progress in designing high accuracy and fast convolutional architecture families. However, as neither NAS nor model scaling considers sufficient hardware architecture details, they do not take full advantage of the emerging datacenter (DC) accelerators. In this paper, we search for fast and accurate CNN model families for efficient inference on DC accelerators. We first analyze DC accelerators and find that existing CNNs suffer from insufficient operational intensity, parallelism, and execution efficiency. These insights let us create a DC-accelerator-optimized search space, with space-to-depth, space-to-batch, hybrid fused convolution structures with vanilla and depthwise convolutions, and block-wise activation functions. On top of our DC accelerator optimized neural architecture search space, we further propose a latency-aware compound scaling (LACS), the first multi-objective compound scaling method optimizing both accuracy and latency. Our LACS discovers that network depth should grow much faster than image size and network width, which is quite different from previous compound scaling results. With the new search space and LACS, our search and scaling on datacenter accelerators results in a new model series named EfficientNet-X. EfficientNet-X is up to more than 2X faster than EfficientNet (a model series with state-of-the-art trade-off on FLOPs and accuracy) on TPUv3 and GPUv100, with comparable accuracy. EfficientNet-X is also up to 7X faster than recent RegNet and ResNeSt on TPUv3 and GPUv100.

cs.CV↗

arXiv2021

Lattice Boltzmann simulation of three-phase flows with moving contact lines on curved surfaces

A numerical method for simulating three-phase flows with moving contact lines on arbitrarily complex surfaces is developed in the framework of lattice Boltzmann method. In this method, the immiscible three-phase flow is modeled through a multiple-relaxation-time color-gradient model, which not only allows for a full range of interfacial tensions but also produces stable outcomes for a wide range of viscosity ratios. A characteristic line model is introduced to implement the wetting boundary condition, which is not only easy to implement but also able to handle arbitrarily complex boundaries with prescribed contact angles. The developed method is first validated by the simulation of a Janus droplet resting on a flat surface, a perfect Janus droplet deposited on a cylinder, and the capillary intrusion of ternary fluids for various viscosity ratios. It is then used to study a compound droplet subject to a uniform incoming flow passing through a multi-pillar structure, where three different values of surface wettability are considered. The simulated results show that the surface wettability has significant impact on the droplet dynamic behavior and final fluid distribution.

physics.flu-dyn↗

arXiv2021

Deep Implicit Coordination Graphs for Multi-agent Reinforcement Learning

Multi-agent reinforcement learning (MARL) requires coordination to efficiently solve certain tasks. Fully centralized control is often infeasible in such domains due to the size of joint action spaces. Coordination graph based formalization allows reasoning about the joint action based on the structure of interactions. However, they often require domain expertise in their design. This paper introduces the deep implicit coordination graph (DICG) architecture for such scenarios. DICG consists of a module for inferring the dynamic coordination graph structure which is then used by a graph neural network based module to learn to implicitly reason about the joint actions or values. DICG allows learning the tradeoff between full centralization and decentralization via standard actor-critic methods to significantly improve coordination for domains with large number of agents. We apply DICG to both centralized-training-centralized-execution and centralized-training-decentralized-execution regimes. We demonstrate that DICG solves the relative overgeneralization pathology in predatory-prey tasks as well as outperforms various MARL baselines on the challenging StarCraft II Multi-agent Challenge (SMAC) and traffic junction environments.

cs.LG↗

arXiv2020

Collaborative Attention Mechanism for Multi-View Action Recognition

Multi-view action recognition (MVAR) leverages complementary temporal information from different views to improve the learning performance. Obtaining informative view-specific representation plays an essential role in MVAR. Attention has been widely adopted as an effective strategy for discovering discriminative cues underlying temporal data. However, most existing MVAR methods only utilize attention to extract representation for each view individually, ignoring the potential to dig latent patterns based on mutual-support information in attention space. To this end, we propose a collaborative attention mechanism (CAM) for solving the MVAR problem in this paper. The proposed CAM detects the attention differences among multi-view, and adaptively integrates frame-level information to benefit each other. Specifically, we extend the long short-term memory (LSTM) to a Mutual-Aid RNN (MAR) to achieve the multi-view collaboration process. CAM takes advantages of view-specific attention pattern to guide another view and discover potential information which is hard to be explored by itself. It paves a novel way to leverage attention information and enhances the multi-view representation learning. Extensive experiments on four action datasets illustrate the proposed CAM achieves better results for each view and also boosts multi-view performance.

cs.CV↗

arXiv2020

Correlative Channel-Aware Fusion for Multi-View Time Series Classification

Multi-view time series classification (MVTSC) aims to improve the performance by fusing the distinctive temporal information from multiple views. Existing methods mainly focus on fusing multi-view information at an early stage, e.g., by learning a common feature subspace among multiple views. However, these early fusion methods may not fully exploit the unique temporal patterns of each view in complicated time series. Moreover, the label correlations of multiple views, which are critical to boost-ing, are usually under-explored for the MVTSC problem. To address the aforementioned issues, we propose a Correlative Channel-Aware Fusion (C2AF) network. First, C2AF extracts comprehensive and robust temporal patterns by a two-stream structured encoder for each view, and captures the intra-view and inter-view label correlations with a graph-based correlation matrix. Second, a channel-aware learnable fusion mechanism is implemented through convolutional neural networks to further explore the global correlative patterns. These two steps are trained end-to-end in the proposed C2AF network. Extensive experimental results on three real-world datasets demonstrate the superiority of our approach over the state-of-the-art methods. A detailed ablation study is also provided to show the effectiveness of each model component.

cs.LG↗