arXiv subjects

Lili Wang

Publications and source records attributed to Lili Wang.

At least 73 records · Page 4Linked to original sources

arXiv2023

Graph-Level Embedding for Time-Evolving Graphs

Graph representation learning (also known as network embedding) has been extensively researched with varying levels of granularity, ranging from nodes to graphs. While most prior work in this area focuses on node-level representation, limited research has been conducted on graph-level embedding, particularly for dynamic or temporal networks. However, learning low-dimensional graph-level representations for dynamic networks is critical for various downstream graph retrieval tasks such as temporal graph similarity ranking, temporal graph isomorphism, and anomaly detection. In this paper, we present a novel method for temporal graph-level embedding that addresses this gap. Our approach involves constructing a multilayer graph and using a modified random walk with temporal backtracking to generate temporal contexts for the graph's nodes. We then train a "document-level" language model on these contexts to generate graph-level embeddings. We evaluate our proposed model on five publicly available datasets for the task of temporal graph similarity ranking, and our model outperforms baseline methods. Our experimental results demonstrate the effectiveness of our method in generating graph-level embeddings for dynamic networks.

cs.LG↗

arXiv2023

A Simulation-free Group Sequential Design with Max-combo Tests in the Presence of Non-proportional Hazards

Non-proportional hazards (NPH) have been observed recently in many immuno-oncology clinical trials. Weighted log-rank tests (WLRT) with suitably chosen weights can be used to improve the power of detecting the difference of the two survival curves in the presence of NPH. However, it is not easy to choose a proper WLRT in practice when both robustness and efficiency are considered. A versatile maxcombo test was proposed to achieve the balance of robustness and efficiency and has received increasing attentions in both methodology development and application. However, survival trials often warrant interim analyses due to its high cost and long duration. The integration and application of maxcombo tests in interim analyses often require extensive simulation studies. In this paper, we propose a simulation-free approach for group sequential design with maxcombo test in survival trials. The simulation results support that the proposed approaches successfully control both the type I error rate and offer great accuracy and flexibility in estimating sample sizes, at the expense of light computation burden. Notably, our methods display a strong robustness towards various model misspecifications, and have been implemented in an R package for free access online.

stat.ME↗

arXiv2022

Label Guidance based Object Locating in Virtual Reality

Object locating in virtual reality (VR) has been widely used in many VR applications, such as virtual assembly, virtual repair, virtual remote coaching. However, when there are a large number of objects in the virtual environment(VE), the user cannot locate the target object efficiently and comfortably. In this paper, we propose a label guidance based object locating method for locating the target object efficiently in VR. Firstly, we introduce the label guidance based object locating pipeline to improve the efficiency of the object locating. It arranges the labels of all objects on the same screen, lets the user select the target labels first, and then uses the flying labels to guide the user to the target object. Then we summarize five principles for constructing the label layout for object locating and propose a two-level hierarchical sorted and orientated label layout based on the five principles for the user to select the candidate labels efficiently and comfortably. After that, we propose the view and gaze based label guidance method for guiding the user to locate the target object based on the selected candidate labels.It generates specific flying trajectories for candidate labels, updates the flying speed of candidate labels, keeps valid candidate labels , and removes the invalid candidate labels in real time during object locating with the guidance of the candidate labels. Compared with the traditional method, the user study results show that our method significantly improves efficiency and reduces task load for object locating.

cs.HC↗

arXiv2022

Distributed Feedback Control of Multi-Channel Linear Systems

In this paper it is established that any jointly controllable, jointly observable, multi-channel, discrete or continuous time linear system with a strongly connected neighbor (communication) graph can be exponentially stabilized with any pre-specified convergence rate using a time-invariant distributed linear control. As an illustration of how this finding can be used to deal with certain distributed tracking problems, a solution is given to a distributed set-point control problem for a continuous-time, multi-channel linear system in which each and every agent with access to the system is able to independently adjust its scalar-valued, controlled output to any desired set-point value. To better understand the constraints on controller design, the distributed control problem is recast as a classical decentralized control problem. Armed with the tools of decentralized control, including the notion of a "fixed spectrum", it is possible to show quite surprisingly that if the only information each agent is allowed to share with its neighbors is its measured output, then distributed stabilization in some cases is impossible. Using well-known decentralized control concepts, lower bounds are derived on the dimensions of the shared sub-states of local controllers which, if satisfied, guarantee that there will be no fixed closed-loop system eigenvalues to contend with. The decentralized control perspective also enables one to assert that without imposing a partitioning constraint, the closed-loop spectrum of any jointly controllable, jointly observable multi-channel linear system with a strongly connected neighbor graph, can be freely assigned with distributed feedback control. It is also explained why in the face of finite delays, exponential stabilization at any prescribed convergence rate can still be achieved with distributed control, at least for discrete-time multi-channel linear systems.

eess.SY↗

arXiv2022

Foveated Rendering: a State-of-the-Art Survey

Recently, virtual reality (VR) technology has been widely used in medical, military, manufacturing, entertainment, and other fields. These applications must simulate different complex material surfaces, various dynamic objects, and complex physical phenomena, increasing the complexity of VR scenes. Current computing devices cannot efficiently render these complex scenes in real time, and delayed rendering makes the content observed by the user inconsistent with the user's interaction, causing discomfort. Foveated rendering is a promising technique that can accelerate rendering. It takes advantage of human eyes' inherent features and renders different regions with different qualities without sacrificing perceived visual quality. Foveated rendering research has a history of 31 years and is mainly focused on solving the following three problems. The first is to apply perceptual models of the human visual system into foveated rendering. The second is to render the image with different qualities according to foveation principles. The third is to integrate foveated rendering into existing rendering paradigms to improve rendering performance. In this survey, we review foveated rendering research from 1990 to 2021. We first revisit the visual perceptual models related to foveated rendering. Subsequently, we propose a new foveated rendering taxonomy and then classify and review the research on this basis. Finally, we discuss potential opportunities and open questions in the foveated rendering field. We anticipate that this survey will provide new researchers with a high-level overview of the state of the art in this field, furnish experts with up-to-date information and offer ideas alongside a framework to VR display software and hardware designers and engineers.

cs.GR↗

arXiv2022

A Hybrid Observer for Estimating the State of a Distributed Linear System

A hybrid observer is described for estimating the state of a system of the form dot x=Ax, y_i=C_ix, i=1,...,m. The system's state x is simultaneously estimated by m agents assuming agent i senses y_i and receives appropriately defined data from its neighbors. Neighbor relations are characterized by a time-varying directed graph N(t). Agent i updates its estimate x_i of x at event times t_{i1},t_{i2} ... using a local continuous-time linear observer and a local parameter estimator which iterates q times during each event time interval [t_{i(s-1)},t_{is}), s>=1, to obtain an estimate of x(t_{is}). Subject to the assumptions that N(t) is strongly connected, and the system is jointly observable, it is possible to design parameters so that x_i converges to x with a pre-assigned rate. This result holds when agents communicate asynchronously with the assumption that N(t) changes slowly. Exponential convergence is also assured if the event time sequence of the agents are slightly different, although only if the system being observed is exponentially stable; this limitation however, is a robustness issue shared by all open loop state estimators with small modeling errors. The result also holds facing abrupt changes in the number of vertices and arcs in the inter-agent communication graph upon which the algorithm depends.

cs.DC↗

arXiv2022

Asymptotic properties of steady plane solutions of the Navier-Stokes equations in the exterior of a half-space

Motivated by Gilbarg-Weinberger's early work on asymptotic properties of steady plane solutions of the Navier-Stokes equations on a neighborhood of infinity \cite{GW1978} , we investigate asymptotic properties of steady plane solutions of this system on a half-neighborhood of infinity with finite Dirichlet integral and Navier-slip boundary condition, and obtain that the velocity of the solution grows more slowly than $\sqrt{\log r}$, while the pressure converges to $0$ along each ray passing through the origin.

math.AP↗

arXiv2022

Evolution of the electronic structure of ultrathin MnBi2Te4 Films

Ultrathin films of intrinsic magnetic topological insulator MnBi2Te4 exhibit fascinating quantum properties such as quantum anomalous Hall effect and axion insulator state. In this work, we systematically investigate the evolution of the electronic structure of MnBi2Te4 thin films. With increasing film thickness, the electronic structure changes from an insulator-type with a large energy gap to one with in-gap topological surface states, which is, however, still drastically different from the bulk material. By surface doping of alkali-metal atoms, a Rashba split band gradually emerges and hybridizes with topological surface states, which not only reconciles the puzzling difference between the electronic structures of the bulk and thin film MnBi2Te4 but also provides an interesting platform to establish Rashba ferromagnet that is attractive for (quantum) anomalous Hall effect. Our results provide important insights into the understanding and engineering of the intriguing quantum properties of MnBi2Te4 thin films.

cond-mat.mtrl-sci↗

arXiv2022

Identifying $s$-wave pairing symmetry in single-layer FeSe from topologically trivial edge states

Determining the pairing symmetry of single-layer FeSe on SrTiO$_3$ is the key to understanding the enhanced pairing mechanism; furthermore, it guides exploring new superconductors with high transition temperatures ($T_\mathrm{c}$). Despite significant efforts, it remains controversial whether the symmetry is the sign-preserving $s$- or the sign-changed $s_{\pm}$-wave. Here, we investigate the pairing symmetry of single-layer FeSe from a topological point of view. Using low-temperature scanning tunneling microscopy/spectroscopy, we have systematically characterized the superconducting states at isolated edges and corners of single-layer FeSe. The tunneling spectra collected at edges and corners exhibit full gap and substantial dip, respectively, demonstrating the absence of topologically non-trivial edge/corner modes. According to the theoretical calculation, these spectroscopic features are strong evidence for the sign-preserving $s$-wave pairing.

cond-mat.supr-con↗

arXiv2022

Predicted high-temperature superconductivity in rare earth hydride ErH2 at moderate pressure

Hydrides offer an opportunity to study high-temperature (Tc) superconductivity at experimentally achievable pressures. However, they remained extremely high. Using density functional theory calculations, herein we demonstrated that a newly rare earth hydride, namely bulk ErH2, could be superconducting with a Tc around 80 K at 14.5 GPa. To date, the drived pressure is the lowest reported value for compressed hydrides. Besides superconductivity, Fermi Surface nesting and Kondo effect were manifested at this pressure. Intriguingly, due to Kondo destruction, superconductivity was prone to exist at 15 GPa. Under the rest of applied pressures, we also revealed a gap of band structure at 20 GPa on the background of normal metallic states. At 20 GPa, this compressed system could act as a host of superconductor being judged from a sharp jump of spontaneous magnetic susceptibility with an evanescent spin density of state at Fermi level along with the competition between spin density wave and superconductivity. Finally, electron pairing glue for ErH2 at these three typical pressures was attributed to the antiferromagnetic spin fluctuation.

cond-mat.supr-con↗

arXiv2022

EnCBP: A New Benchmark Dataset for Finer-Grained Cultural Background Prediction in English

While cultural backgrounds have been shown to affect linguistic expressions, existing natural language processing (NLP) research on culture modeling is overly coarse-grained and does not examine cultural differences among speakers of the same language. To address this problem and augment NLP models with cultural background features, we collect, annotate, manually validate, and benchmark EnCBP, a finer-grained news-based cultural background prediction dataset in English. Through language modeling (LM) evaluations and manual analyses, we confirm that there are noticeable differences in linguistic expressions among five English-speaking countries and across four states in the US. Additionally, our evaluations on nine syntactic (CoNLL-2003), semantic (PAWS-Wiki, QNLI, STS-B, and RTE), and psycholinguistic tasks (SST-5, SST-2, Emotion, and Go-Emotions) show that, while introducing cultural background information does not benefit the Go-Emotions task due to text domain conflicts, it noticeably improves deep learning (DL) model performance on other tasks. Our findings strongly support the importance of cultural background modeling to a wide variety of NLP tasks and demonstrate the applicability of EnCBP in culture-related research.

cs.CL↗

arXiv2022

Processing Induced Distinct Charge Carrier Dynamics of Bulky Organic Halide Treated Perovskites

State-of-the-art metal halide perovskite-based photovoltaics often employ organic ammonium salts, AX, as a surface passivator, where A is a large organic cation and X is a halide. These surface treatments passivate the perovskite by forming layered perovskites (e.g., A2PbX4) or by AX itself serving as a surface passivation agent on the perovskite photoactive film. It remains unclear whether layered perovskites or AX is the ideal passivator due to an incomplete understanding of the interfacial impact and resulting photoexcited carrier dynamics of AX treatment. In the present study, we use TRPL measurements to selectively probe the different interfaces of glass/perovskite/AX to demonstrate the vastly distinct interfacial photoexcited state dynamics with the presence of A2PbX4 or AX. Coupling the TRPL results with X-ray diffraction and nanoscale microscopy measurements, we find that the presence of AX not only passivates the traps at the surface and the grain boundaries, but also induces an α/δ-FAPbI3 phase mixing that alters the carrier dynamics near the glass/perovskite interface and enhances the photoluminescence quantum yield. In contrast, the passivation with A2PbI4 is mostly localized to the surface and grain boundaries near the top surface where the availability of PbI2 directly determines the formation of A2PbI4. Such distinct mechanisms significantly impact the corresponding solar cell performance, and we find AX passivation that has not been converted to a layered perovskite allows for a much larger processing window (e.g., larger allowed variance of AX concentration which is critical for improving the eventual manufacturing yield) and more reproducible condition to realize device performance improvements, while A2PbI4 as a passivator yields a much narrower processing window. We expect these results to enable a more rational route for developing AX for perovskite.

cond-mat.mtrl-sci↗

arXiv2022

Selective Trapping of Hexagonally Warped Topological Surface States in a Triangular Quantum Corral

The surface of a three-dimensional topological insulator (TI) hosts two-dimensional massless Dirac fermions (DFs), the gapless and spin-helical nature of which yields many exotic phenomena, such as the immunity of topological surface states (TSS) to back-scattering. This leads to their high transmission through surface defects or potential barriers. Quantum corrals, previously elaborated on metal surfaces, can act as nanometer-sized electronic resonators to trap Schrödinger electrons by quantum confinement. It is thus intriguing, concerning their peculiar nature, to put the Dirac electrons of TSS to the test in similar circumstances. Here, we report the behaviors of TSS in a triangular quantum corral (TQC) fabricated by epitaxially growing Bi bilayer nanostructures on the surfaces of Bi2Te3 films. Unlike a circular corral, the TQC is supposed to be totally transparent for DFs. By mapping the electronic structure of TSS inside TQCs through a low-temperature scanning tunneling microscope in the real space, both the trapping and de-trapping behaviors of the TSS electrons are observed. The selection rules are found to be governed by the geometry and spin texture of the constant energy contour of TSS upon the strong hexagonal warping in Bi2Te3. Careful analysis of the quantum interference patterns of quasi-bound states yields the corresponding wave vectors of trapped TSS, through which two trapping mechanisms favoring momenta in different directions are uncovered. Our work indicates the extended nature of TSS and elucidates the selection rules of the trapping of TSS in the presence of a complicated surface state structure, giving insights into the effective engineering of DFs in TIs.

cond-mat.mes-hall↗

arXiv2021

Transformer with Peak Suppression and Knowledge Guidance for Fine-grained Image Recognition

Fine-grained image recognition is challenging because discriminative clues are usually fragmented, whether from a single image or multiple images. Despite their significant improvements, most existing methods still focus on the most discriminative parts from a single image, ignoring informative details in other regions and lacking consideration of clues from other associated images. In this paper, we analyze the difficulties of fine-grained image recognition from a new perspective and propose a transformer architecture with the peak suppression module and knowledge guidance module, which respects the diversification of discriminative features in a single image and the aggregation of discriminative clues among multiple images. Specifically, the peak suppression module first utilizes a linear projection to convert the input image into sequential tokens. It then blocks the token based on the attention response generated by the transformer encoder. This module penalizes the attention to the most discriminative parts in the feature learning process, therefore, enhancing the information exploitation of the neglected regions. The knowledge guidance module compares the image-based representation generated from the peak suppression module with the learnable knowledge embedding set to obtain the knowledge response coefficients. Afterwards, it formalizes the knowledge learning as a classification problem using response coefficients as the classification scores. Knowledge embeddings and image-based representations are updated during training so that the knowledge embedding includes discriminative clues for different images. Finally, we incorporate the acquired knowledge embeddings into the image-based representations as comprehensive representations, leading to significantly higher performance. Extensive evaluations on the six popular datasets demonstrate the advantage of the proposed method.

cs.MM↗

arXiv2021

Integral Ricci curvature and the mass gap of Dirichlet Laplacians on domains

We obtain a fundamental gap estimate for classes of bounded domains with quantitative control on the boundary in a complete manifold with integral bounds on the negative part of the Ricci curvature. This extends the result of \cite{Oden-Sung-Wang99} to $L^p$-Ricci curvature assumptions, $p>n/2$. To achieve our result, it is shown that the domains under consideration are John domains, what enables us to obtain an estimate on the first nonzero Neumann eigenvalue, which is of independent interest.

math.DG↗

arXiv2021

Embedding Node Structural Role Identity Using Stress Majorization

Nodes in networks may have one or more functions that determine their role in the system. As opposed to local proximity, which captures the local context of nodes, the role identity captures the functional "role" that nodes play in a network, such as being the center of a group, or the bridge between two groups. This means that nodes far apart in a network can have similar structural role identities. Several recent works have explored methods for embedding the roles of nodes in networks. However, these methods all rely on either approximating or indirect modeling of structural equivalence. In this paper, we present a novel and flexible framework using stress majorization, to transform the high-dimensional role identities in networks directly (without approximation or indirect modeling) to a low-dimensional embedding space. Our method is also flexible, in that it does not rely on specific structural similarity definitions. We evaluated our method on the tasks of node classification, clustering, and visualization, using three real-world and five synthetic networks. Our experiments show that our framework achieves superior results than existing methods in learning node role representations.

cs.SI↗

arXiv2021

Graph Embedding via Diffusion-Wavelets-Based Node Feature Distribution Characterization

Recent years have seen a rise in the development of representational learning methods for graph data. Most of these methods, however, focus on node-level representation learning at various scales (e.g., microscopic, mesoscopic, and macroscopic node embedding). In comparison, methods for representation learning on whole graphs are currently relatively sparse. In this paper, we propose a novel unsupervised whole graph embedding method. Our method uses spectral graph wavelets to capture topological similarities on each k-hop sub-graph between nodes and uses them to learn embeddings for the whole graph. We evaluate our method against 12 well-known baselines on 4 real-world datasets and show that our method achieves the best performance across all experiments, outperforming the current state-of-the-art by a considerable margin.

cs.LG↗

arXiv2021

GradTS: A Gradient-Based Automatic Auxiliary Task Selection Method Based on Transformer Networks

A key problem in multi-task learning (MTL) research is how to select high-quality auxiliary tasks automatically. This paper presents GradTS, an automatic auxiliary task selection method based on gradient calculation in Transformer-based models. Compared to AUTOSEM, a strong baseline method, GradTS improves the performance of MT-DNN with a bert-base-cased backend model, from 0.33% to 17.93% on 8 natural language understanding (NLU) tasks in the GLUE benchmarks. GradTS is also time-saving since (1) its gradient calculations are based on single-task experiments and (2) the gradients are re-used without additional experiments when the candidate task set changes. On the 8 GLUE classification tasks, for example, GradTS costs on average 21.32% less time than AUTOSEM with comparable GPU consumption. Further, we show the robustness of GradTS across various task settings and model selections, e.g. mixed objectives among candidate tasks. The efficiency and efficacy of GradTS in these case studies illustrate its general applicability in MTL research without requiring manual task filtering or costly parameter tuning.

cs.LG↗