arXiv subjects
Publications and source records attributed to Tao Wang.
We obtain explicit criteria for both exponential ergodicity and strong ergodicity for one-dimensional time-changed symmetric stable processes with $α\in(1,2)$. Explicit lower bounds for ergodic convergence rates are given.
Transformers, which are popular for language modeling, have been explored for solving vision tasks recently, e.g., the Vision Transformer (ViT) for image classification. The ViT model splits each image into a sequence of tokens with fixed length and then applies multiple Transformer layers to model their global relation for classification. However, ViT achieves inferior performance to CNNs when trained from scratch on a midsize dataset like ImageNet. We find it is because: 1) the simple tokenization of input images fails to model the important local structure such as edges and lines among neighboring pixels, leading to low training sample efficiency; 2) the redundant attention backbone design of ViT leads to limited feature richness for fixed computation budgets and limited training samples. To overcome such limitations, we propose a new Tokens-To-Token Vision Transformer (T2T-ViT), which incorporates 1) a layer-wise Tokens-to-Token (T2T) transformation to progressively structurize the image to tokens by recursively aggregating neighboring Tokens into one Token (Tokens-to-Token), such that local structure represented by surrounding tokens can be modeled and tokens length can be reduced; 2) an efficient backbone with a deep-narrow structure for vision transformer motivated by CNN architecture design after empirical study. Notably, T2T-ViT reduces the parameter count and MACs of vanilla ViT by half, while achieving more than 3.0\% improvement when trained from scratch on ImageNet. It also outperforms ResNets and achieves comparable performance with MobileNets by directly training on ImageNet. For example, T2T-ViT with comparable size to ResNet50 (21.5M parameters) can achieve 83.3\% top1 accuracy in image resolution 384$\times$384 on ImageNet. (Code: https://github.com/yitu-opensource/T2T-ViT)
Nitrogen heteroatom doping into a triangulene molecule allows tuning its magnetic state. However, the synthesis of the nitrogen-doped triangulene (aza-triangulene) has been challenging. Herein, we report the successful synthesis of aza-triangulene on the Au(111) and Ag(111) surfaces, along with their characterizations by scanning tunneling microscopy and spectroscopy in combination with density functional theory (DFT) calculations. Aza-triangulenes were obtained by reducing ketone-substituted precursors. Exposure to atomic hydrogen followed by thermal annealing and, when necessary, manipulations with the scanning probe, afforded the target product. We demonstrate that on Au(111) aza-triangulene donates an electron to the substrate and exhibits an open-shell triplet ground state. This is derived from the different Kondo resonances of the final aza-triangulene product and a series of intermediates on Au(111). Experimentally mapped molecular orbitals match perfectly with DFT calculated counterparts for a positively charged aza-triangulene. In contrast, aza-triangulene on Ag(111) receives an extra electron from the substrate and displays a closed-shell character. Our study reveals the electronic properties of aza-triangulene on different metal surfaces and offers an efficient approach for the fabrication of new hydrocarbon structures, including reactive open-shell molecules.
We present Multi-view Pose transformer (MvP) for estimating multi-person 3D poses from multi-view images. Instead of estimating 3D joint locations from costly volumetric representation or reconstructing the per-person 3D pose from multiple detected 2D poses as in previous methods, MvP directly regresses the multi-person 3D poses in a clean and efficient way, without relying on intermediate tasks. Specifically, MvP represents skeleton joints as learnable query embeddings and let them progressively attend to and reason over the multi-view information from the input images to directly regress the actual 3D joint locations. To improve the accuracy of such a simple pipeline, MvP presents a hierarchical scheme to concisely represent query embeddings of multi-person skeleton joints and introduces an input-dependent query adaptation approach. Further, MvP designs a novel geometrically guided attention mechanism, called projective attention, to more precisely fuse the cross-view information for each joint. MvP also introduces a RayConv operation to integrate the view-dependent camera geometry into the feature representations for augmenting the projective attention. We show experimentally that our MvP model outperforms the state-of-the-art methods on several benchmarks while being much more efficient. Notably, it achieves 92.3% AP25 on the challenging Panoptic dataset, improving upon the previous best approach [36] by 9.8%. MvP is general and also extendable to recovering human mesh represented by the SMPL model, thus useful for modeling multi-person body shapes. Code and models are available at https://github.com/sail-sg/mvp.
Ultraviolet-C light-emitting diodes (UVC-LEDs) have great application in pathogen inactivation under various kinds of situations, especially in the fight against the COVID-19. Unfortunately, its epitaxial wafers are so far limited to 2-inch size, which greatly increases the cost of massive production. In this work, we report the 4-inch crack-free high-power UVC-LED wafer. This achievement relies on a proposed strain-tailored strategy, where a three-dimensional to two-dimensional (3D-2D) transition layer is introduced during the homo-epitaxy of AlN on high temperature annealed (HTA)-AlN template, which successfully drives the original compressive strain into tensile one and thus solves the challenge of realizing high quality Al$_{0.6}$Ga$_{0.4}$N layer with a flat surface. This smooth Al$_{0.6}$Ga$_{0.4}$N layer is nearly pseudomorphically grown on the strain-tailored HTA-AlN template, leading to 4-inch UVC-LED wafers with outstanding performances. Our strategy succeeds in compromising the bottlenecked contradictory in producing large-sized UVC-LED wafer on pronounced crystalline AlN template: The compressive strain in HTA-AlN allows for crack-free 4-inch wafer, but at the same time leads to a deterioration of the AlGaN morphology and crystal quality. The launch of 4-inch wafers makes the chip fabrication process of UVC-LEDs matches the mature blue one, and will definitely speed up the universal of UVC-LED in daily life.
Variational formulas for the Laplace transform of the exit time from an open set of a Hunt process generated by a regular lower bounded semi-Dirichlet form are established. While for symmetric Markov processes, variational formulas are derived for the exponential moments of the exit time. As applications, we provide some comparison theorems and quantitative relations of the exponential moments and Poincaré inequalities.
While many forms of financial support are currently available, there are still many complaints about inadequate financing from software maintainers. In May 2019, GitHub, the world's most active social coding platform, launched the Sponsor mechanism as a step toward more deeply integrating open source development and financial support. This paper collects data on 8,028 maintainers, 13,555 sponsors, and 22,515 sponsorships and conducts a comprehensive analysis. We explore the relationship between the Sponsor mechanism and developers along four dimensions using a combination of qualitative and quantitative analysis, examining why developers participate, how the mechanism affects developer activity, who obtains more sponsorships, and what mechanism flaws developers have encountered in the process of using it. We find a long-tail effect in the act of sponsorship, with most maintainers' expectations remaining unmet, and sponsorship has only a short-term, slightly positive impact on development activity but is not sustainable. While sponsors participate in this mechanism mainly as a means of thanking the developers of OSS that they use, in practice, the social status of developers is the primary influence on the number of sponsorships. We find that both the Sponsor mechanism and open source donations have certain shortcomings and need further improvements to attract more participants.
Tor, an onion-routing anonymity network, has been shown to be vulnerable to Website Fingerprinting (WF), which de-anonymizes web browsing by analyzing the unique characteristics of the encrypted network traffic. Although many defenses have been proposed, few have been implemented and tested in the real world; others were only simulated. Due to its synthetic nature, simulation may fail to capture the real performance of these defenses. To figure out how these defenses perform in the real world, we propose WFDefProxy, a general platform for WF defense implementation on Tor using pluggable transports. We create the first full implementation of three WF defenses: FRONT, Tamaraw and Random-WT. We evaluate each defense in both simulation and implementation to compare their results, and we find that simulation correctly captures the strength of each defense against attacks. In addition, we confirm that Random-WT is not effective in both simulation and implementation, reducing the strongest attacker's accuracy by only 7%. We also found a minor difference in overhead between simulation and implementation. We analyze how this may be due to assumptions made in simulation regarding packet delays and queuing, or the soft stop condition we implemented in WFDefProxy to detect the end of a page load. The implementation of FRONT cost about 23% more data overhead than simulation, while the implementation of Tamaraw cost about 28% - 45% less data overhead. In addition, the implementation of Tamaraw incurred only 21% time overhead, compared to 51% - 242% estimated by simulation in previous work.
In recent years, powered by the learned discriminative representation via graph neural network (GNN) models, deep graph matching methods have made great progresses in the task of matching semantic features. However, these methods usually rely on heuristically generated graph patterns, which may introduce unreliable relationships to hurt the matching performance. In this paper, we propose a joint \emph{graph learning and matching} network, named GLAM, to explore reliable graph structures for boosting graph matching. GLAM adopts a pure attention-based framework for both graph learning and graph matching. Specifically, it employs two types of attention mechanisms, self-attention and cross-attention for the task. The self-attention discovers the relationships between features and to further update feature representations over the learnt structures; and the cross-attention computes cross-graph correlations between the two feature sets to be matched for feature reconstruction. Moreover, the final matching solution is directly derived from the output of the cross-attention layer, without employing a specific matching decision module. The proposed method is evaluated on three popular visual matching benchmarks (Pascal VOC, Willow Object and SPair-71k), and it outperforms previous state-of-the-art graph matching methods by significant margins on all benchmarks. Furthermore, the graph patterns learnt by our model are validated to be able to remarkably enhance previous deep graph matching methods by replacing their handcrafted graph structures with the learnt ones.
Despite decades of efforts, robot navigation in a real scenario with volatility, uncertainty, complexity, and ambiguity (VUCA for short), remains a challenging topic. Inspired by the central nervous system (CNS), we propose a hierarchical multi-expert learning framework for autonomous navigation in a VUCA environment. With a heuristic exploration mechanism considering target location, path cost, and safety level, the upper layer performs simultaneous map exploration and route-planning to avoid trapping in a blind alley, similar to the cerebrum in the CNS. Using a local adaptive model fusing multiple discrepant strategies, the lower layer pursuits a balance between collision-avoidance and go-straight strategies, acting as the cerebellum in the CNS. We conduct simulation and real-world experiments on multiple platforms, including legged and wheeled robots. Experimental results demonstrate our algorithm outperforms the existing methods in terms of task achievement, time efficiency, and security.
As cross-chain technologies make the interactions among different blockchains (hereinafter "chains") possible, multi-chains consensus is becoming more and more important in blockchain networks. However, more attention has been paid to the single-chain consensus schemes. The multi-chains consensus with trusted miners participation has been not considered, thus offering opportunities for malicious users to launch Diverse Miners Behaviors (DMB) attacks on different chains. DMB attackers can be friendly in the consensus process of some chains called mask-chains to enhance trust value, while on other chains called kill-chains they engage in destructive behaviors of network. In this paper, we propose a multi-chains consensus scheme named as Proof-of-DiscTrust (PoDT) to defend against DMB attacks. Distinctive trust idea (DiscTrust) is introduced to evaluate the trust value of each user concerning different chains. A dynamic behaviors prediction scheme is designed to strengthen DiscTrust to prevent intensive DMB attackers who maintain high trust by alternately creating true or false blocks on kill-chains. On this basis, a trusted miners selection algorithm for multi-chains can be achieved at a round of block creation. Experimental results show that PoDT is secure against DMB attacks and more effective than traditional consensus schemes in multi-chains environments.
Quantum synchronizable codes (QSCs) are special quantum error-correcting codes which can be used to correct the effects of quantum noise on qubits and misalignment in block synchronization. In this paper, a new class of quantum synchronizable codes of length $2^n$ are constructed by using the cyclotomic cosets, whose synchronization capabilities always reach the upper bound $2^n$.
The interference between a sequence of Landau-Zener (LZ) transitions can produce Rabi oscillations(LZROs). This phenomenon is a kind of time-domain Landau-Zener-Stuckelberg-Majorana (LZSM) interference. However, experimental demonstrations of this LZSM interference induced Rabi oscillation are extremely hard due to the short coherence time of the driven quantum system. Here, we study theoretically the time-domain LZSM interference between the clock transition in one-dimensional (1D) Sr-87 optical lattice clock (OLC) system. With the help of both the adiabatic-impulse model and Floquet numerical simulation method, the LZROs with special step-like structure are clearly found both in fast- and slow-passage limit in the real experiment parameter regions. In addition, the dephasing effect caused by the system temperature can be suppressed with destructive interference in the slow-passage limit. Finally, we discuss the possible Bloch-Siegert shift while the pulse time is away from the half-integer and integer periods. Our work provides a clear roadmap to observe the LZROs on the OLC platform.
Visible-infrared cross-modality person re-identification is a challenging ReID task, which aims to retrieve and match the same identity's images between the heterogeneous visible and infrared modalities. Thus, the core of this task is to bridge the huge gap between these two modalities. The existing convolutional neural network-based methods mainly face the problem of insufficient perception of modalities' information, and can not learn good discriminative modality-invariant embeddings for identities, which limits their performance. To solve these problems, we propose a cross-modality transformer-based method (CMTR) for the visible-infrared person re-identification task, which can explicitly mine the information of each modality and generate better discriminative features based on it. Specifically, to capture modalities' characteristics, we design the novel modality embeddings, which are fused with token embeddings to encode modalities' information. Furthermore, to enhance representation of modality embeddings and adjust matching embeddings' distribution, we propose a modality-aware enhancement loss based on the learned modalities' information, reducing intra-class distance and enlarging inter-class distance. To our knowledge, this is the first work of applying transformer network to the cross-modality re-identification task. We implement extensive experiments on the public SYSU-MM01 and RegDB datasets, and our proposed CMTR model's performance significantly surpasses existing outstanding CNN-based methods.
In this paper, we classify graphs with nonnegative Lin-Lu-Yau-Ollivier Ricci curvature, maximum degree at most 3 and diameter at least 6.
Entity retrieval, which aims at disambiguating mentions to canonical entities from massive KBs, is essential for many tasks in natural language processing. Recent progress in entity retrieval shows that the dual-encoder structure is a powerful and efficient framework to nominate candidates if entities are only identified by descriptions. However, they ignore the property that meanings of entity mentions diverge in different contexts and are related to various portions of descriptions, which are treated equally in previous works. In this work, we propose Multi-View Entity Representations (MuVER), a novel approach for entity retrieval that constructs multi-view representations for entity descriptions and approximates the optimal view for mentions via a heuristic searching method. Our method achieves the state-of-the-art performance on ZESHEL and improves the quality of candidates on three standard Entity Linking datasets
We obtain the lower bounds for ergodic convergence rates, including spectral gaps and convergence rates in strong ergodicity for time-changed symmetric Lévy processes by using harmonic function and reversible measure. As direct applications, explicit sufficient conditions for exponential and strong ergodicity are given. Some examples are also presented.
Bismuth oxyselenide (Bi$_2$O$_2$Se) attracts great interest as a potential n-type complement to p-type thermoelectric oxides in practical applications. Previous investigations were generally focused on polycrystals. Here, we performed a study on the thermoelectric properties of Bi$_2$O$_2$Se single crystals. Our samples exhibit electron mobility as high as 250 cm$^2.$V$^{-1}$.s$^{-1}$ and thermal conductivity as low as $2$ W.m$^{-1}$.K$^{-1}$ near room temperature. The maximized figure of merit is yielded to be 0.188 at 390 K, higher than that of polycrystals. Consequently, a rough estimation of the phonon mean free path ($\ell_\textrm{ph}$) from the kinetic model amounts to 12 $Å$ at 390 K and follows a $T^{-1}$ behavior. An extrapolation of $\ell_\textrm{ph}$ to higher temperatures indicates that this system approaches the Ioffe-Regel limit at about 1100 K. In light of the phonon dispersions, we argue that the ultralow $\ell_\textrm{ph}$ is attributed to intense anharmonic phonon-phonon scattering, including Umklapp process and acoustic to optical phonon scattering. Our results suggest that single crystals provide a further improvement of thermoelectric performance of Bi$_2$O$_2$Se.