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Wei Yao

Publications and source records attributed to Wei Yao.

At least 91 records · Page 5Linked to original sources
arXiv2023

SoK: A Taxonomy for Critical Analysis of Consensus Mechanisms in Consortium Blockchain

Consensus algorithms are central to blockchain technology and an emerging research area. In this paper, we begin with an overview of the different types and architectures of blockchain networks. Then, with a focus on consortium blockchains, we survey, classify, and assess their principal consensus mechanisms. Furthermore, as consensus mechanisms determine network reliability, enhance performance efficiency, and ensure system security, we conduct a critical analysis of the strengths and weaknesses of consensus algorithms using a taxonomy of three different criteria: reliability, performance, and security. We conclude with insights into current and future research challenges and opportunities in this domain.

cs.DS↗
arXiv2023

Temperature-driven confinements of surface electrons and adatoms in a weakly interacting 2D organic porous network

Two-dimensional organic porous networks (2DOPNs) have opened new vistas for tailoring the physicochemical characteristics of metallic surfaces. These typically chemically bound nanoporous structures act as periodical quantum wells leading to the 2D confinements of surface electron gases, adatoms and molecular guests. Here we propose a new type of porous network with weakly interacting 2,4,6-triphenyl-1,3,5-triazine (TPT) molecules on a Cu(111) surface, in which a temperature-driven (T-driven) phase transition can reversibly alter the supramolecular structures from a close-packed (CP-TPT) phase to a porous-network (PN-TPT) phase. Crucially, only the low-temperature PN-TPT exhibits subnano-scale cavities that can confine the surface state electrons and metal adatoms. The confined surface electrons undergo a significant electronic band renormalization. To activate the spin degree of freedom, the T-driven PN-TPT structure can additionally trap Co atoms within the cavities, forming highly ordered quantum dots. Our theoretical simulation reveals a complex spin carrier transfer from the confined Co cluster to the neighbouring TPT molecules via the underlying substrate. Our results demonstrate that weakly interacting 2DOPN offers a unique quantum switch capable of steering and controlling electrons and spin at surfaces via tailored quantum confinements.

cond-mat.mtrl-sci↗
arXiv2023

Averaged Method of Multipliers for Bi-Level Optimization without Lower-Level Strong Convexity

Gradient methods have become mainstream techniques for Bi-Level Optimization (BLO) in learning fields. The validity of existing works heavily rely on either a restrictive Lower-Level Strong Convexity (LLSC) condition or on solving a series of approximation subproblems with high accuracy or both. In this work, by averaging the upper and lower level objectives, we propose a single loop Bi-level Averaged Method of Multipliers (sl-BAMM) for BLO that is simple yet efficient for large-scale BLO and gets rid of the limited LLSC restriction. We further provide non-asymptotic convergence analysis of sl-BAMM towards KKT stationary points, and the comparative advantage of our analysis lies in the absence of strong gradient boundedness assumption, which is always required by others. Thus our theory safely captures a wider variety of applications in deep learning, especially where the upper-level objective is quadratic w.r.t. the lower-level variable. Experimental results demonstrate the superiority of our method.

math.OC↗
arXiv2023

Calm local optimality for nonconvex-nonconcave minimax problems

Nonconvex-nonconcave minimax problems have found numerous applications in various fields including machine learning. However, questions remain about what is a good surrogate for local minimax optimum and how to characterize the minimax optimality. Recently Jin, Netrapalli, and Jordan (ICML 2020) introduced a concept of local minimax point and derived optimality conditions for the smooth and unconstrained case. In this paper, we introduce the concept of calm local minimax point, which is a local minimax point with a calm radius function. With the extra calmness property we obtain first and second-order sufficient and necessary optimality conditions for a very general class of nonsmooth nonconvex-nonconcave minimax problem. Moreover we show that the calm local minimax optimality and the local minimax optimality coincide under a weak sufficient optimality condition for the maximization problem. This equivalence allows us to derive stronger optimality conditions under weaker assumptions for local minimax optimality.

math.OC↗
arXiv2023

Self-supervised Multi-task Learning Framework for Safety and Health-Oriented Connected Driving Environment Perception using Onboard Camera

Cutting-edge connected vehicle (CV) technologies have drawn much attention in recent years. The real-time traffic data captured by a CV can be shared with other CVs and data centers so as to open new possibilities for solving diverse transportation problems. However, imagery captured by onboard cameras in a connected environment, are not sufficiently investigated, especially for safety and health-oriented visual perception. In this paper, a bidirectional process of image synthesis and decomposition (BPISD) approach is proposed, and thus a novel self-supervised multi-task learning framework, to simultaneously estimate depth map, atmospheric visibility, airlight, and PM2.5 mass concentration, in which depth map and visibility are considered highly associated with traffic safety, while airlight and PM2.5 mass concentration are directly correlated with human health. Both the training and testing phases of the proposed system solely require a single image as input. Due to the innovative training pipeline, the depth estimation network can manage various levels of visibility conditions and overcome inherent problems in current image-synthesis-based depth estimation, thereby generating high-quality depth maps even in low-visibility situations and further benefiting accurate estimations of visibility, airlight, and PM2.5 mass concentration. Extensive experiments on the synthesized data from the KITTI and real-world data collected in Beijing demonstrate that the proposed method can (1) achieve performance competitive in depth estimation as compared with state-of-the-art methods when taking clear images as input; (2) predict vivid depth map for images contaminated by various levels of haze; and (3) accurately estimate visibility, airlight, and PM2.5 mass concentrations. Beneficial applications can be developed based on the presented work to improve traffic safety, air quality, and public health.

cs.CV↗
arXiv2023

Urban GeoBIM construction by integrating semantic LiDAR point clouds with as-designed BIM models

Developments in three-dimensional real worlds promote the integration of geoinformation and building information models (BIM) known as GeoBIM in urban construction. Light detection and ranging (LiDAR) integrated with global navigation satellite systems can provide geo-referenced spatial information. However, constructing detailed urban GeoBIM poses challenges in terms of LiDAR data quality. BIM models designed from software are rich in geometrical information but often lack accurate geo-referenced locations. In this paper, we propose a complementary strategy that integrates LiDAR point clouds with as-designed BIM models for reconstructing urban scenes. A state-of-the-art deep learning framework and graph theory are first combined for LiDAR point cloud segmentation. A coarse-to-fine matching program is then developed to integrate object point clouds with corresponding BIM models. Results show the overall segmentation accuracy of LiDAR datasets reaches up to 90%, and average positioning accuracies of BIM models are 0.023 m for pole-like objects and 0.156 m for buildings, demonstrating the effectiveness of the method in segmentation and matching processes. This work offers a practical solution for rapid and accurate urban GeoBIM construction.

cs.CV↗
arXiv2023

Revealing the two-dimensional electronic structure and anisotropic superconductivity in a natural van der Waals superlattice (PbSe)$_{1.14}$NbSe$_2$

Van der Waals superlattices are important for tailoring the electronic structures and properties of layered materials. Here we report the superconducting properties and electronic structure of a natural van der Waals superlattice (PbSe)$_{1.14}$NbSe$_2$. Anisotropic superconductivity with a transition temperature $T_c$ = 5.6 $\pm$ 0.1 K, which is higher than monolayer NbSe$_2$, is revealed by transport measurements on high-quality samples. Angle-resolved photoemission spectroscopy (ARPES) measurements reveal the two-dimensional electronic structure and a charge transfer of 0.43 electrons per NbSe$_2$ unit cell from the blocking PbSe layer. In addition, polarization-dependent ARPES measurements reveal a significant circular dichroism with opposite contrast at K and K' valleys, suggesting a significant spin-orbital coupling and distinct orbital angular momentum. Our work suggests natural van der Waals superlattice as an effective pathway for achieving intriguing properties distinct from both the bulk and monolayer samples.

cond-mat.supr-con↗
arXiv2023

Combined approach with second-order optimality conditions for bilevel programming problems

In this paper, we propose a combined approach with second-order optimality conditions of the lower level problem to study constraint qualifications and optimality conditions for bilevel programming problems. The new method is inspired by the combined approach developed by Ye and Zhu in 2010, where the authors combined the classical first-order and the value function approaches to derive new necessary optimality conditions. In our approach, we add a second-order optimality condition to the combined program as a new constraint. We show that when all known approaches fail, adding the second-order optimality condition as a constraint makes the corresponding partial calmness condition and the resulting necessary optimality condition easier to hold. We also give some discussions on advantages and disadvantages of the combined approaches with the first-order and the second-order information.

math.OC↗
arXiv2022

AuE-IPA: An AU Engagement Based Infant Pain Assessment Method

Recent studies have found that pain in infancy has a significant impact on infant development, including psychological problems, possible brain injury, and pain sensitivity in adulthood. However, due to the lack of specialists and the fact that infants are unable to express verbally their experience of pain, it is difficult to assess infant pain. Most existing infant pain assessment systems directly apply adult methods to infants ignoring the differences between infant expressions and adult expressions. Meanwhile, as the study of facial action coding system continues to advance, the use of action units (AUs) opens up new possibilities for expression recognition and pain assessment. In this paper, a novel AuE-IPA method is proposed for assessing infant pain by leveraging different engagement levels of AUs. First, different engagement levels of AUs in infant pain are revealed, by analyzing the class activation map of an end-to-end pain assessment model. The intensities of top-engaged AUs are then used in a regression model for achieving automatic infant pain assessment. The model proposed is trained and experimented on YouTube Immunization dataset, YouTube Blood Test dataset, and iCOPEVid dataset. The experimental results show that our AuE-IPA method is more applicable to infants and possesses stronger generalization ability than end-to-end assessment model and the classic PSPI metric.

cs.LG↗
arXiv2022

One Class One Click: Quasi Scene-level Weakly Supervised Point Cloud Semantic Segmentation with Active Learning

Reliance on vast annotations to achieve leading performance severely restricts the practicality of large-scale point cloud semantic segmentation. For the purpose of reducing data annotation costs, effective labeling schemes are developed and contribute to attaining competitive results under weak supervision strategy. Revisiting current weak label forms, we introduce One Class One Click (OCOC), a low cost yet informative quasi scene-level label, which encapsulates point-level and scene-level annotations. An active weakly supervised framework is proposed to leverage scarce labels by involving weak supervision from global and local perspectives. Contextual constraints are imposed by an auxiliary scene classification task, respectively based on global feature embedding and point-wise prediction aggregation, which restricts the model prediction merely to OCOC labels. Furthermore, we design a context-aware pseudo labeling strategy, which effectively supplement point-level supervisory signals. Finally, an active learning scheme with a uncertainty measure - temporal output discrepancy is integrated to examine informative samples and provides guidance on sub-clouds query, which is conducive to quickly attaining desirable OCOC annotations and reduces the labeling cost to an extremely low extent. Extensive experimental analysis using three LiDAR benchmarks collected from airborne, mobile and ground platforms demonstrates that our proposed method achieves very promising results though subject to scarce labels. It considerably outperforms genuine scene-level weakly supervised methods by up to 25\% in terms of average F1 score and achieves competitive results against full supervision schemes. On terrestrial LiDAR dataset - Semantics3D, using approximately 2\textpertenthousand{} of labels, our method achieves an average F1 score of 85.2\%, which increases by 11.58\% compared to the baseline model.

cs.CV↗
arXiv2022

VideoPipe 2022 Challenge: Real-World Video Understanding for Urban Pipe Inspection

Video understanding is an important problem in computer vision. Currently, the well-studied task in this research is human action recognition, where the clips are manually trimmed from the long videos, and a single class of human action is assumed for each clip. However, we may face more complicated scenarios in the industrial applications. For example, in the real-world urban pipe system, anomaly defects are fine-grained, multi-labeled, domain-relevant. To recognize them correctly, we need to understand the detailed video content. For this reason, we propose to advance research areas of video understanding, with a shift from traditional action recognition to industrial anomaly analysis. In particular, we introduce two high-quality video benchmarks, namely QV-Pipe and CCTV-Pipe, for anomaly inspection in the real-world urban pipe systems. Based on these new datasets, we will host two competitions including (1) Video Defect Classification on QV-Pipe and (2) Temporal Defect Localization on CCTV-Pipe. In this report, we describe the details of these benchmarks, the problem definitions of competition tracks, the evaluation metric, and the result summary. We expect that, this competition would bring new opportunities and challenges for video understanding in smart city and beyond. The details of our VideoPipe challenge can be found in https://videopipe.github.io.

cs.CV↗
arXiv2022

Towards Extremely Fast Bilevel Optimization with Self-governed Convergence Guarantees

Gradient methods have become mainstream techniques for Bi-Level Optimization (BLO) in learning and vision fields. The validity of existing works heavily relies on solving a series of approximation subproblems with extraordinarily high accuracy. Unfortunately, to achieve the approximation accuracy requires executing a large quantity of time-consuming iterations and computational burden is naturally caused. This paper is thus devoted to address this critical computational issue. In particular, we propose a single-level formulation to uniformly understand existing explicit and implicit Gradient-based BLOs (GBLOs). This together with our designed counter-example can clearly illustrate the fundamental numerical and theoretical issues of GBLOs and their naive accelerations. By introducing the dual multipliers as a new variable, we then establish Bilevel Alternating Gradient with Dual Correction (BAGDC), a general framework, which significantly accelerates different categories of existing methods by taking specific settings. A striking feature of our convergence result is that, compared to those original unaccelerated GBLO versions, the fast BAGDC admits a unified non-asymptotic convergence theory towards stationarity. A variety of numerical experiments have also been conducted to demonstrate the superiority of the proposed algorithmic framework.

math.OC↗
arXiv2022

Information fusion approach for biomass estimation in a plateau mountainous forest using a synergistic system comprising UAS-based digital camera and LiDAR

Forest land plays a vital role in global climate, ecosystems, farming and human living environments. Therefore, forest biomass estimation methods are necessary to monitor changes in the forest structure and function, which are key data in natural resources research. Although accurate forest biomass measurements are important in forest inventory and assessments, high-density measurements that involve airborne light detection and ranging (LiDAR) at a low flight height in large mountainous areas are highly expensive. The objective of this study was to quantify the aboveground biomass (AGB) of a plateau mountainous forest reserve using a system that synergistically combines an unmanned aircraft system (UAS)-based digital aerial camera and LiDAR to leverage their complementary advantages. In this study, we utilized digital aerial photogrammetry (DAP), which has the unique advantages of speed, high spatial resolution, and low cost, to compensate for the deficiency of forestry inventory using UAS-based LiDAR that requires terrain-following flight for high-resolution data acquisition. Combined with the sparse LiDAR points acquired by using a high-altitude and high-speed UAS for terrain extraction, dense normalized DAP point clouds can be obtained to produce an accurate and high-resolution canopy height model (CHM). Based on the CHM and spectral attributes obtained from multispectral images, we estimated and mapped the AGB of the region of interest with considerable cost efficiency. Our study supports the development of predictive models for large-scale wall-to-wall AGB mapping by leveraging the complementarity between DAP and LiDAR measurements. This work also reveals the potential of utilizing a UAS-based digital camera and LiDAR synergistically in a plateau mountainous forest area.

eess.IV↗
arXiv2022

Joint Learning of Frequency and Spatial Domains for Dense Predictions

Current artificial neural networks mainly conduct the learning process in the spatial domain but neglect the frequency domain learning. However, the learning course performed in the frequency domain can be more efficient than that in the spatial domain. In this paper, we fully explore frequency domain learning and propose a joint learning paradigm of frequency and spatial domains. This paradigm can take full advantage of the preponderances of frequency learning and spatial learning; specifically, frequency and spatial domain learning can effectively capture global and local information, respectively. Exhaustive experiments on two dense prediction tasks, i.e., self-supervised depth estimation and semantic segmentation, demonstrate that the proposed joint learning paradigm can 1) achieve performance competitive to those of state-of-the-art methods in both depth estimation and semantic segmentation tasks, even without pretraining; and 2) significantly reduce the number of parameters compared to other state-of-the-art methods, which provides more chance to develop real-world applications. We hope that the proposed method can encourage more research in cross-domain learning.

cs.CV↗
arXiv2022

Efficient divide-and-conquer registration of UAV and ground LiDAR point clouds through canopy shape context

Registration of unmanned aerial vehicle laser scanning (ULS) and ground light detection and ranging (LiDAR) point clouds in forests is critical to create a detailed representation of a forest structure and an accurate inversion of forest parameters. However, forest occlusion poses challenges for marker-based registration methods, and some marker-free automated registration methods have low efficiency due to the process of object (e.g., tree, crown) segmentation. Therefore, we use a divide-and-conquer strategy and propose an automated and efficient method to register ULS and ground LiDAR point clouds in forests. Registration involves coarse alignment and fine registration, where the coarse alignment of point clouds is divided into vertical and horizontal alignment. The vertical alignment is achieved by ground alignment, which is achieved by the transformation relationship between normal vectors of the ground point cloud and the horizontal plane, and the horizontal alignment is achieved by canopy projection image matching. During image matching, vegetation points are first distinguished by the ground filtering algorithm, and then, vegetation points are projected onto the horizontal plane to obtain two binary images. To match the two images, a matching strategy is used based on canopy shape context features, which are described by a two-point congruent set and canopy overlap. Finally, we implement coarse alignment of ULS and ground LiDAR datasets by combining the results of ground alignment and image matching and finish fine registration. Also, the effectiveness, accuracy, and efficiency of the proposed method are demonstrated by field measurements of forest plots. Experimental results show that the ULS and ground LiDAR data in different plots are registered, of which the horizontal alignment errors are less than 0.02 m, and the average runtime of the proposed method is less than 1 second.

cs.CV↗
arXiv2021

A hybrid convolutional neural network/active contour approach to segmenting dead trees in aerial imagery

The stability and ability of an ecosystem to withstand climate change is directly linked to its biodiversity. Dead trees are a key indicator of overall forest health, housing one-third of forest ecosystem biodiversity, and constitute 8%of the global carbon stocks. They are decomposed by several natural factors, e.g. climate, insects and fungi. Accurate detection and modeling of dead wood mass is paramount to understanding forest ecology, the carbon cycle and decomposers. We present a novel method to construct precise shape contours of dead trees from aerial photographs by combining established convolutional neural networks with a novel active contour model in an energy minimization framework. Our approach yields superior performance accuracy over state-of-the-art in terms of precision, recall, and intersection over union of detected dead trees. This improved performance is essential to meet emerging challenges caused by climate change (and other man-made perturbations to the systems), particularly to monitor and estimate carbon stock decay rates, monitor forest health and biodiversity, and the overall effects of dead wood on and from climate change.

cs.CV↗
arXiv2021

Generic property of the partial calmness condition for bilevel programming problems

The partial calmness for the bilevel programming problem (BLPP) is an important condition which ensures that a local optimal solution of BLPP is a local optimal solution of a partially penalized problem where the lower level optimality constraint is moved to the objective function and hence a weaker constraint qualification can be applied. In this paper we propose a sufficient condition in the form of a partial error bound condition which guarantees the partial calmness condition. We analyse the partial calmness for the combined program based on the Bouligand (B-) and the Fritz John (FJ) stationary conditions from a generic point of view. Our main result states that the partial error bound condition for the combined programs based on B and FJ conditions are generic for an important setting with applications in economics and hence the partial calmness for the combined program is not a particularly stringent assumption. Moreover we derive optimality conditions for the combined program for the generic case without any extra constraint qualifications and show the exact equivalence between our optimality condition and the one by Jongen and Shikhman given in implicit form. Our arguments are based on Jongen, Jonker and Twilt's generic (five type) classification of the so-called generalized critical points for one-dimensional parametric optimization problems and Jongen and Shikhman's generic local reductions of BLPPs.

math.OC↗
arXiv2021

Spatially-resolved electronic structure of stripe domains in IrTe$_2$ through electronic structure microscopy

Phase separation in the nanometer- to micrometer-scale is characteristic for correlated materials, for example, high temperature superconductors, colossal magnetoresistance manganites, Mott insulators, etc. Resolving the electronic structure with spatially-resolved information is critical for revealing the fundamental physics of such inhomogeneous systems yet this is challenging experimentally. Here by using nanometer- and micrometer-spot angle-resolved photoemission spectroscopies (NanoARPES and MicroARPES), we reveal the spatially-resolved electronic structure in the stripe phase of IrTe$_2$. Each separated domain shows two-fold symmetric electronic structure with the mirror axis aligned along 3 equivalent directions, and 6$\times$1 replicas are clearly identified. Moreover, such electronic structure inhomogeneity disappears across the stripe phase transition, suggesting that electronic phase with broken symmetry induced by the 6$\times$1 modulation is directly related to the stripe phase transition of IrTe$_2$. Our work demonstrates the capability of NanoARPES and MicroARPES in elucidating the fundamental physics of phase-separated materials.

cond-mat.mes-hall↗