arXiv subjects

Qi Wang

Publications and source records attributed to Qi Wang.

At least 505 records · Page 28Linked to original sources

arXiv2022

Deeply nonlinear excitation of self-normalised exchange spin waves

Spin waves are ideal candidates for wave-based computing, but the construction of magnetic circuits is blocked by a lack of an efficient mechanism to excite long-running exchange spin waves with normalised amplitudes. Here, we solve the challenge by exploiting the deeply nonlinear phenomena of forward-volume spin waves in 200 nm wide nanoscale waveguides and validate our concept with microfocused Brillouin light scattering spectroscopy. An unprecedented nonlinear frequency shift of >2 GHz is achieved, corresponding to a magnetisation precession angle of 55° and enabling the excitation of exchange spin waves with a wavelength of down to ten nanometres with an efficiency of >80%. The amplitude of the excited spin waves is constant and independent of the input microwave power due to the self-locking nonlinear shift, enabling robust adjustment of the spin wave amplitudes in future on-chip magnonic integrated circuits.

physics.app-ph↗

arXiv2022

Holistic Transformer: A Joint Neural Network for Trajectory Prediction and Decision-Making of Autonomous Vehicles

Trajectory prediction and behavioral decision-making are two important tasks for autonomous vehicles that require good understanding of the environmental context; behavioral decisions are better made by referring to the outputs of trajectory predictions. However, most current solutions perform these two tasks separately. Therefore, a joint neural network that combines multiple cues is proposed and named as the holistic transformer to predict trajectories and make behavioral decisions simultaneously. To better explore the intrinsic relationships between cues, the network uses existing knowledge and adopts three kinds of attention mechanisms: the sparse multi-head type for reducing noise impact, feature selection sparse type for optimally using partial prior knowledge, and multi-head with sigmoid activation type for optimally using posteriori knowledge. Compared with other trajectory prediction models, the proposed model has better comprehensive performance and good interpretability. Perceptual noise robustness experiments demonstrate that the proposed model has good noise robustness. Thus, simultaneous trajectory prediction and behavioral decision-making combining multiple cues can reduce computational costs and enhance semantic relationships between scenes and agents.

cs.LG↗

arXiv2022

Exploring Web3 From the View of Blockchain

Web3 is the most hyped concept from 2020 to date, greatly motivating the prosperity of the Internet of Value and Metaverse. However, no solid evidence stipulates the exact definition, criterion, or standard in the sense of such a buzzword. To fill the gap, we aim to clarify the term in this work. We narrow down the connotation of Web3 by separating it from high-level controversy argues and, instead, focusing on its protocol, architecture, and evaluation from the perspective of blockchain fields. Specifically, we have identified all potential architectural design types and evaluated each of them by employing the scenario-based architecture evaluation method. The evaluation shows that existing applications are neither secure nor adoptable as claimed. Meanwhile, we also discuss opportunities and challenges surrounding the Web3 space and answer several prevailing questions from communities. A primary result is that Web3 still relies on traditional internet infrastructure, not as independent as advocated. This report, as of June 2022, provides the first strict research on Web3 in the view of blockchain. We hope that this work would provide a guide for the development of future Web3 services.

cs.CR↗

arXiv2022

Sign-reversed anomalous Nernst effect in the ferromagnetic Weyl-semimetal Fe$_{3-x}$GeTe$_2$: the role of Fe vacancies

Anomalous Nernst effect, as a thermal partner of anomalous Hall effect, is particularly sensitive to the Berry curvature anomaly near the Fermi level, and has been used to probe the topological nature of quantum materials. In this work, we report the observation of both effects in the ferromagnetic Weyl-semimetal Fe$_{3-x}$GeTe$_2$ with tunable Fe vacancies. With decreasing Fe vacancies, the anomalous Hall conductivity evolves as a function of the longitudinal conductivity from the hopping region to the region where the intrinsic Berry curvature contribution dominates. Concomitant evolutions in the anomalous Nernst signal and the anomalous off-diagonal thermoelectric coefficient are observed below the Curie temperature, displaying a unique sign change caused by the Fe vacancies. Combining these results with first-principles calculations, we argue that the Fe-vacancy concentration plays a unique role in simultaneously tuning the chemical potential and ferromagnetism, which in turn controls the Berry curvature contribution in this family of ferromagnetic topological semimetals.

cond-mat.mtrl-sci↗

arXiv2022

On $τ$-tilting finite simply connected algebras

We show that $τ$-tilting finite simply connected algebras are representation-finite. Then, some related algebras are considered, including iterated tilted algebras, tubular algebras and so on. We also prove that the $τ$-tilting finiteness of non-sincere algebras can be reduced to that of sincere algebras. This motivates us to give a complete list of $τ$-tilting finite sincere simply connected algebras.

math.RT↗

arXiv2022

SS-GNN: A Simple-Structured Graph Neural Network for Affinity Prediction

Efficient and effective drug-target binding affinity (DTBA) prediction is a challenging task due to the limited computational resources in practical applications and is a crucial basis for drug screening. Inspired by the good representation ability of graph neural networks (GNNs), we propose a simple-structured GNN model named SS-GNN to accurately predict DTBA. By constructing a single undirected graph based on a distance threshold to represent protein-ligand interactions, the scale of the graph data is greatly reduced. Moreover, ignoring covalent bonds in the protein further reduces the computational cost of the model. The GNN-MLP module takes the latent feature extraction of atoms and edges in the graph as two mutually independent processes. We also develop an edge-based atom-pair feature aggregation method to represent complex interactions and a graph pooling-based method to predict the binding affinity of the complex. We achieve state-of-the-art prediction performance using a simple model (with only 0.6M parameters) without introducing complicated geometric feature descriptions. SS-GNN achieves Pearson's Rp=0.853 on the PDBbind v2016 core set, outperforming state-of-the-art GNN-based methods by 5.2%. Moreover, the simplified model structure and concise data processing procedure improve the prediction efficiency of the model. For a typical protein-ligand complex, affinity prediction takes only 0.2 ms. All codes are freely accessible at https://github.com/xianyuco/SS-GNN.

q-bio.BM↗

arXiv2022

Spin-polarized imaging of the antiferromagnetic structure and field-tunable bound states in kagome magnet FeSn

Kagome metals are as an exciting playground for the explorations of novel phenomena at the intersection of topology, electron correlations and magnetism. The family of FeSn-based kagome magnets in particular attracted a lot of attention for simplicity of the layered crystal structure and tunable topological electronic band structure. Despite a significant progress in understanding their bulk properties, surface electronic and magnetic structures are yet to be fully explored in many of these systems. In this work, we focus on a prototypical kagome metal FeSn. Using a combination of spin-averaged and spin-polarized scanning tunneling microscopy, we provide the first atomic-scale visualization of the layered antiferromagnetic structure at the surface of FeSn. In contrast to the field-tunable electronic structure of cousin material Fe3Sn2 that is a ferromagnet, we find that electronic density-of-states of FeSn is robust to the application of external magnetic field. Interestingly, despite the field-insensitive electronic band structure, FeSn exhibits bounds states tied to specific impurities with large effective moments that strongly couple to the magnetic field. Our experiments provide microscopic insights necessary for theoretical modeling of FeSn and serve as a spring board for spin-polarized measurements of topological magnets in general.

cond-mat.mtrl-sci↗

arXiv2022

Frontrunning Block Attack in PoA Clique: A Case Study

As a fundamental technology of decentralized finance (DeFi), blockchain's ability to maintain a distributed fair ledger is threatened by manipulation of block/transaction order. In this paper, we propose a frontrunning block attack against the Clique-based Proof of Authority (PoA) algorithms. Our attack can frontrun blocks from honest in-turn sealers by breaking the proper order of leader selection. By falsifying the priority parameters (both \textit{difficulty} and \textit{delay time}), a malicious out-of-turn sealer can always successfully occupy the leader position and produce advantageous blocks that may contain profitable transactions. As a typical instance, we apply our attack to a mature Clique-engined project, HPB (\$3,058,901, as of April 2022). Experimental results demonstrate the effectiveness and feasibility. Then, we further recommend fixes that make identity checks effective. Our investigation and suggestion have been submitted to its official team and got their approval. We believe this work can act as, at least, a warning case for Clique variants to avoid repeating these design mistakes.

cs.CR↗

arXiv2022

From Easy to Hard: Learning Language-guided Curriculum for Visual Question Answering on Remote Sensing Data

Visual question answering (VQA) for remote sensing scene has great potential in intelligent human-computer interaction system. Although VQA in computer vision has been widely researched, VQA for remote sensing data (RSVQA) is still in its infancy. There are two characteristics that need to be specially considered for the RSVQA task. 1) No object annotations are available in RSVQA datasets, which makes it difficult for models to exploit informative region representation; 2) There are questions with clearly different difficulty levels for each image in the RSVQA task. Directly training a model with questions in a random order may confuse the model and limit the performance. To address these two problems, in this paper, a multi-level visual feature learning method is proposed to jointly extract language-guided holistic and regional image features. Besides, a self-paced curriculum learning (SPCL)-based VQA model is developed to train networks with samples in an easy-to-hard way. To be more specific, a language-guided SPCL method with a soft weighting strategy is explored in this work. The proposed model is evaluated on three public datasets, and extensive experimental results show that the proposed RSVQA framework can achieve promising performance.

cs.CV↗

arXiv2022

Exploring Unfairness on Proof of Authority: Order Manipulation Attacks and Remedies

Proof of Authority (PoA) is a type of permissioned consensus algorithm with a fixed committee. PoA has been widely adopted by communities and industries due to its better performance and faster finality. In this paper, we explore the \textit{unfairness} issue existing in the current PoA implementations. We have investigated 2,500+ \textit{in the wild} projects and selected 10+ as our main focus (covering Ethereum, Binance smart chain, etc.). We have identified two types of order manipulation attacks to separately break the transaction-level (a.k.a. transaction ordering) and the block-level (sealer position ordering) fairness. Both of them merely rely on honest-but-\textit{profitable} sealer assumption without modifying original settings. We launch these attacks on the forked branches under an isolated environment and carefully evaluate the attacking scope towards different implementations. To date (as of Nov 2021), the potentially affected PoA market cap can reach up to $681,087$ million USD. Besides, we further dive into the source code of selected projects, and accordingly, propose our recommendation for the fix. To the best of knowledge, this work provides the first exploration of the \textit{unfairness} issue in PoA algorithms.

cs.CR↗

arXiv2022

Worst-Case Complexity of TRACE with Inexact Subproblem Solutions for Nonconvex Smooth Optimization

An algorithm for solving nonconvex smooth optimization problems is proposed, analyzed, and tested. The algorithm is an extension of the Trust Region Algorithm with Contractions and Expansions (TRACE) [Math. Prog. 162(1):132, 2017]. In particular, the extension allows the algorithm to use inexact solutions of the arising subproblems, which is an important feature for solving large-scale problems. Inexactness is allowed in a manner such that the optimal iteration complexity of ${\cal O}(ε^{-3/2})$ for attaining an $ε$-approximate first-order stationary point is maintained while the worst-case complexity in terms of Hessian-vector products may be significantly improved as compared to the original TRACE. Numerical experiments show the benefits of allowing inexact subproblem solutions and that the algorithm compares favorably to a state-of-the-art technique.

math.OC↗

arXiv2022

How Do Smart Contracts Benefit Security Protocols?

Smart contracts have recently been adopted by many security protocols. However, existing studies lack satisfactory theoretical support on how contracts benefit security protocols. This paper aims to give a systematic analysis of smart contract (SC)-based security protocols to fulfill the gap of unclear arguments and statements. We firstly investigate \textit{state of the art studies} and establish a formalized model of smart contract protocols with well-defined syntax and assumptions. Then, we apply our formal framework to two concrete instructions to explore corresponding advantages and desirable properties. Through our analysis, we abstract three generic properties (\textit{non-repudiation, non-equivocation, and non-frameability}) and accordingly identify two patterns. (1) a smart contract can be as an autonomous subscriber to assist the trusted third party (TTP); (2) a smart contract can replace traditional TTP. To the best of our knowledge, this is the first study to provide in-depth discussions of SC-based security protocols from a strictly theoretical perspective.

cs.CR↗

arXiv2022

SoK: TEE-assisted Confidential Smart Contract

The blockchain-based smart contract lacks privacy since the contract state and instruction code are exposed to the public. Combining smart-contract execution with Trusted Execution Environments (TEEs) provides an efficient solution, called TEE-assisted smart contracts, for protecting the confidentiality of contract states. However, the combination approaches are varied, and a systematic study is absent. Newly released systems may fail to draw upon the experience learned from existing protocols, such as repeating known design mistakes or applying TEE technology in insecure ways. In this paper, we first investigate and categorize the existing systems into two types: the layer-one solution and layer-two solution. Then, we establish an analysis framework to capture their common lights, covering the desired properties (for contract services), threat models, and security considerations (for underlying systems). Based on our taxonomy, we identify their ideal functionalities and uncover the fundamental flaws and reasons for the challenges in each specification design. We believe that this work would provide a guide for the development of TEE-assisted smart contracts, as well as a framework to evaluate future TEE-assisted confidential contract systems.

cs.CR↗

arXiv2022

Emergent superconductivity in van der Waals Kagome material Pd3P2S8 under high pressure

Kagome lattice systems have been proposed to host rich physics, which provide an excellent platform to explore unusual quantum states. Here, we report on the discovery of superconductivity in van der Waals material Pd3P2S8 under pressure. The superconductivity is observed in Pd3P2S8 for those pressures where the temperature dependence of the resistivity changes from a semiconducting-like behavior to that of a normal metal. The superconducting transition temperature Tc increases with applied pressure and reaches ~ 6.83 K at 79.5 GPa. Combining high-pressure XRD, Raman spectroscopy and theoretical calculations, our results demonstrate that the observed superconductivity induced by high pressure in Pd3P2S8 is closely related to the formation of amorphous phase, which results from the structural instability due to the enhanced coupling between interlayer Pd and S atoms upon compression.

cond-mat.supr-con↗

arXiv2022

Dual-Stage Approach Toward Hyperspectral Image Super-Resolution

Hyperspectral image produces high spectral resolution at the sacrifice of spatial resolution. Without reducing the spectral resolution, improving the resolution in the spatial domain is a very challenging problem. Motivated by the discovery that hyperspectral image exhibits high similarity between adjacent bands in a large spectral range, in this paper, we explore a new structure for hyperspectral image super-resolution (DualSR), leading to a dual-stage design, i.e., coarse stage and fine stage. In coarse stage, five bands with high similarity in a certain spectral range are divided into three groups, and the current band is guided to study the potential knowledge. Under the action of alternative spectral fusion mechanism, the coarse SR image is super-resolved in band-by-band. In order to build model from a global perspective, an enhanced back-projection method via spectral angle constraint is developed in fine stage to learn the content of spatial-spectral consistency, dramatically improving the performance gain. Extensive experiments demonstrate the effectiveness of the proposed coarse stage and fine stage. Besides, our network produces state-of-the-art results against existing works in terms of spatial reconstruction and spectral fidelity.

eess.IV↗

arXiv2022

Influence of light quark loops on the Wigner phase with Dyson-Schwinger equations approach

We study the influence of light quark loops on the Wigner phase by solving coupled Dyson-Schwinger equations for quark propagator and gluon propagator. We take the gluon propagator in the Nambu phase from $N_f$ = 2 unquenched lattice QCD and choose various phenomenological models for the quark-gluon vertex. The gluon propagator in Winger phase is assumed to be different from that in the Nambu phase only due to the vacuum polarization of the quark loop. We obtain the Wigner solution of the coupled equations, compared with that from solving only the equation of the quark propagator. We discussed the corrections by the light quark loops and the dependence on various models of the quark-gluon vertex.

hep-ph↗

arXiv2022

DR.VIC: Decomposition and Reasoning for Video Individual Counting

Pedestrian counting is a fundamental tool for understanding pedestrian patterns and crowd flow analysis. Existing works (e.g., image-level pedestrian counting, crossline crowd counting et al.) either only focus on the image-level counting or are constrained to the manual annotation of lines. In this work, we propose to conduct the pedestrian counting from a new perspective - Video Individual Counting (VIC), which counts the total number of individual pedestrians in the given video (a person is only counted once). Instead of relying on the Multiple Object Tracking (MOT) techniques, we propose to solve the problem by decomposing all pedestrians into the initial pedestrians who existed in the first frame and the new pedestrians with separate identities in each following frame. Then, an end-to-end Decomposition and Reasoning Network (DRNet) is designed to predict the initial pedestrian count with the density estimation method and reason the new pedestrian's count of each frame with the differentiable optimal transport. Extensive experiments are conducted on two datasets with congested pedestrians and diverse scenes, demonstrating the effectiveness of our method over baselines with great superiority in counting the individual pedestrians. Code: https://github.com/taohan10200/DRNet.

cs.CV↗

arXiv2022

Manipulation of Dirac band curvature and momentum-dependent g-factor in a kagome magnet YMn6Sn6

The Zeeman effect describes the energy change of an atomic quantum state in magnetic field. The magnitude and the direction of this change depend on the dimensionless Lande g-factor. In quantum solids, the response of the Bloch electron states to the magnetic field also exhibits the Zeeman effect with an effective g-factor that was theoretically predicted to be dependent on the momentum. While typically negligible in many ordinary solids, the momentum-dependent variation of the g-factor is theorized to be substantially enhanced in many topological and magnetic systems. However, the momentum-dependence of the g-factor is notoriously difficult to extract and it is yet to be directly experimentally measured. In this work, we report the experimental discovery of a strongly momentum-dependent g-factor in a kagome magnet YMn6Sn6. Using spectroscopic-imaging scanning tunneling microscopy, we map the evolution of a massive Dirac band in the vicinity of the Fermi level as a function of magnetic field. We find that electronic states at different lattice momenta exhibit markedly different Zeeman energy shifts, giving rise to an anomalous g-factor that peaks around the Dirac point. Our work provides the first momentum-resolved visualization of Dirac band curvature manipulation by magnetic field, which should in principle be highly relevant to other topological kagome magnets.

cond-mat.str-el↗