arXiv subjects

Yang Wang

Publications and source records attributed to Yang Wang.

At least 505 records · Page 28Linked to original sources

arXiv2023

Iterative Design of An Accessible Crypto Wallet for Blind Users

Crypto wallets are a key touch-point for cryptocurrency use. People use crypto wallets to make transactions, manage crypto assets, and interact with decentralized apps (dApps). However, as is often the case with emergent technologies, little attention has been paid to understanding and improving accessibility barriers in crypto wallet software. We present a series of user studies that explored how both blind and sighted individuals use MetaMask, one of the most popular non-custodial crypto wallets. We uncovered inter-related accessibility, learnability, and security issues with MetaMask. We also report on an iterative redesign of MetaMask to make it more accessible for blind users. This process involved multiple evaluations with 44 novice crypto wallet users, including 20 sighted users, 23 blind users, and one user with low vision. Our study results show notable improvements for accessibility after two rounds of design iterations. Based on the results, we discuss design implications for creating more accessible and secure crypto wallets for blind users.

cs.CR↗

arXiv2023

iCOIL: Scenario Aware Autonomous Parking Via Integrated Constrained Optimization and Imitation Learning

Autonomous parking (AP) is an emering technique to navigate an intelligent vehicle to a parking space without any human intervention. Existing AP methods based on mathematical optimization or machine learning may lead to potential failures due to either excessive execution time or lack of generalization. To fill this gap, this paper proposes an integrated constrained optimization and imitation learning (iCOIL) approach to achieve efficient and reliable AP. The iCOIL method has two candidate working modes, i.e., CO and IL, and adopts a hybrid scenario analysis (HSA) model to determine the better mode under various scenarios. We implement and verify iCOIL on the Macao Car Racing Metaverse (MoCAM) platform. Results show that iCOIL properly adapts to different scenarios during the entire AP procedure, and achieves significantly larger success rates than other benchmarks.

cs.RO↗

arXiv2023

STAR-RIS-UAV Aided Coordinated Multipoint Cellular System for Multi-user Networks

Different with conventional reconfigurable intelligent surface (RIS), simultaneous transmitting and reflecting RIS (STAR-RIS) can reflect and transmit the signals to the receiver. In this paper, to serve more ground users and increase the deployment flexibility, we investigate an unmanned aerial vehicle equipped with a STAR-RIS (STAR-RIS-UAV) aided wireless communications for multi-user networks. Energy splitting (ES) and mode switching (MS) protocols are considered to control the reflection and transmission coefficients of STAR-RIS elements. To maximize the sum rate of the STAR-RIS-UAV aided coordinated multipoint cellular system for multi-user networks, the corresponding beamforming vectors as well as transmitted and reflected coefficients matrices are optimized. Specifically, instead of adopting the alternating optimization, we design an iteration method to optimize all variables for both ES and MS protocols at the same time. Simulation results reveal that STAR-RIS-UAV aided wireless communication system has a much higher sum rate than the system with conventional RIS or without RIS. Furthermore, the proposed structure is more flexible than a fixed STAR-RIS and could greatly promote the sum rate.

cs.IT↗

arXiv2023

Phase Transition in Extended Thermodynamics Triggers Sub-shocks

Extended thermodynamics commonly uses polynomial moments to model non-equilibrium transportation, but faces a crisis due to sub-shocks, which are anomalous discontinuities in gas properties when predicting shock waves. The cause of sub-shocks is still unclear, challenging the validity of extended thermodynamics. This paper reveals, for the first time, that sub-shocks arise from intrinsic limitations of polynomials leading to a discontinuous phase transition. Therefore extended thermodynamics necessitates alternative moments beyond polynomials to avoid sub-shocks.

physics.flu-dyn↗

arXiv2023

From river flow to spatial flow: flow map via river flow directions assignment algorithm

Flow maps are thematic maps that visualize object movements across space with a tree layout, in which the underlying tree structure is similar to a natural river system. In this paper, we present a novel and automated approach named RFDA-FM for flow maps from one origin to multiple destinations using a river extraction algorithm in digital elevation models (DEM). The RFDA-FM first models the mapping space as a flat surface by a DEM. A maze-solving algorithm (MSA) for river extraction is then adapted to calculate the flow path from one destination to the origin by constraining its searching directions, direction weights, and searching ranges according to the quality criteria of flow maps. All flow paths from the destinations to the origin are obtained iteratively based on the MSA according to their importance, which is defined by considering their length. Finally, these paths are smoothly rendered with varying widths according to their volume using Bézier curves. A comparison with existing approaches indicates that the flow maps generated by RFDA-FM can be better at keeping nodes away from edges without node overlaps and edge crosses. Two extension experiments demonstrate that RFDA-FM is applicable to heterogeneous mapping space or mapping space with obstacle areas. The parameter analysis shows that RFDA-FM can intuitively control the layouts of flow maps. Project website: https://github.com/TrentonWei/FlowMap

cs.GR↗

arXiv2023

Semantic-Aware Graph Matching Mechanism for Multi-Label Image Recognition

Multi-label image recognition aims to predict a set of labels that present in an image. The key to deal with such problem is to mine the associations between image contents and labels, and further obtain the correct assignments between images and their labels. In this paper, we treat each image as a bag of instances, and formulate the task of multi-label image recognition as an instance-label matching selection problem. To model such problem, we propose an innovative Semantic-aware Graph Matching framework for Multi-Label image recognition (ML-SGM), in which Graph Matching mechanism is introduced owing to its good performance of excavating the instance and label relationship. The framework explicitly establishes category correlations and instance-label correspondences by modeling the relation among content-aware (instance) and semantic-aware (label) category representations, to facilitate multi-label image understanding and reduce the dependency of large amounts of training samples for each category. Specifically, we first construct an instance spatial graph and a label semantic graph respectively and then incorporate them into a constructed assignment graph by connecting each instance to all labels. Subsequently, the graph network block is adopted to aggregate and update all nodes and edges state on the assignment graph to form structured representations for each instance and label. Our network finally derives a prediction score for each instance-label correspondence and optimizes such correspondence with a weighted cross-entropy loss. Empirical results conducted on generic multi-label image recognition demonstrate the superiority of our proposed method. Moreover, the proposed method also shows advantages in multi-label recognition with partial labels and multi-label few-shot learning, as well as outperforms current state-of-the-art methods with a clear margin.

cs.CV↗

arXiv2023

Inferring High-level Geographical Concepts via Knowledge Graph and Multi-scale Data Integration: A Case Study of C-shaped Building Pattern Recognition

Effective building pattern recognition is critical for understanding urban form, automating map generalization, and visualizing 3D city models. Most existing studies use object-independent methods based on visual perception rules and proximity graph models to extract patterns. However, because human vision is a part-based system, pattern recognition may require decomposing shapes into parts or grouping them into clusters. Existing methods may not recognize all visually aware patterns, and the proximity graph model can be inefficient. To improve efficiency and effectiveness, we integrate multi-scale data using a knowledge graph, focusing on the recognition of C-shaped building patterns. First, we use a property graph to represent the relationships between buildings within and across different scales involved in C-shaped building pattern recognition. Next, we store this knowledge graph in a graph database and convert the rules for C-shaped pattern recognition and enrichment into query conditions. Finally, we recognize and enrich C-shaped building patterns using rule-based reasoning in the built knowledge graph. We verify the effectiveness of our method using multi-scale data with three levels of detail (LODs) collected from the Gaode Map. Our results show that our method achieves a higher recall rate of 26.4% for LOD1, 20.0% for LOD2, and 9.1% for LOD3 compared to existing approaches. We also achieve recognition efficiency improvements of 0.91, 1.37, and 9.35 times, respectively.

cs.CV↗

arXiv2023

High dynamic range open-loop current measurement based on diamond quantum magnetometer achieving ppm scale precision

Negatively charged nitrogen-vacancy (NV) centers in diamond have been extensively studied as a promising high sensitivity solid-state magnetic field sensor at room temperature. However, their use for current sensing applications is limited due to the challenge of integration and miniaturization of the diamond NV sensor. Here, we demonstrate an integrated NV sensor fabricated with standard microfabrication process. The sensor device incorporated with a toroidal magnetic yoke enables a high-precision wide range direct current sensing with galvanic isolation. The performance of the diamond NV current sensor in an open loop configuration has been investigated. A current measuring range of 0 A ~ 1000 A with an uncertainty of 46 ppm are achieved. Taking advantage of dual spin resonance modulation, temperature drift is suppressed to 10 ppm/K. This configuration opens new possibilities as a robust and scalable platform for current quantum sensing technologies.

physics.ins-det↗

arXiv2023

Unpacking How Decentralized Autonomous Organizations (DAOs) Work in Practice

Decentralized Autonomous Organizations (DAOs) have emerged as a novel way to coordinate a group of (pseudonymous) entities towards a shared vision (e.g., promoting sustainability), utilizing self-executing smart contracts on blockchains to support decentralized governance and decision-making. In just a few years, over 4,000 DAOs have been launched in various domains, such as investment, education, health, and research. Despite such rapid growth and diversity, it is unclear how these DAOs actually work in practice and to what extent they are effective in achieving their goals. Given this, we aim to unpack how (well) DAOs work in practice. We conducted an in-depth analysis of a diverse set of 10 DAOs of various categories and smart contracts, leveraging on-chain (e.g., voting results) and off-chain data (e.g., community discussions) as well as our interviews with DAO organizers/members. Specifically, we defined metrics to characterize key aspects of DAOs, such as the degrees of decentralization and autonomy. We observed CompoundDAO, AssangeDAO, Bankless, and Krausehouse having poor decentralization in voting, while decentralization has improved over time for one-person-one-vote DAOs (e.g., Proof of Humanity). Moreover, the degree of autonomy varies among DAOs, with some (e.g., Compound and Krausehouse) relying more on third parties than others. Lastly, we offer a set of design implications for future DAO systems based on our findings.

cs.CY↗

arXiv2023

AI-assisted reconstruction of cosmic velocity field from redshift-space spatial distribution of halos

The peculiar velocities of dark matter halos are crucial to study many issues in cosmology and galaxy evolution. In this study, by using the state-of-the-art deep learning technique, a UNet-based neural network, we propose to reconstruct the peculiar velocity field from the redshift-space distribution of dark matter halos. Through a point-to-point comparison and examination of various statistical properties, we demonstrate that, the reconstructed velocity field is in good agreement with the ground truth. The power spectra of various velocity field components, including velocity magnitude, divergence and vorticity, can be successfully recovered when $k\lesssim 1.1$ $h/\rm Mpc$ (the Nyquist frequency of the simulations) at about 80% accuracy. This approach is very promising and presents an alternative method to correct the redshift-space distortions using the measured 3D spatial information of halos. Additionally, for the reconstruction of the momentum field of halos, UNet achieves similar good results. Hence the applications in various aspects of cosmology are very broad, such as correcting redshift errors and improving measurements in the structure of the cosmic web, the kinetic Sunyaev-Zel'dovich effect, BAO reconstruction, etc.

astro-ph.CO↗

arXiv2023

Recent Few-Shot Object Detection Algorithms: A Survey with Performance Comparison

The generic object detection (GOD) task has been successfully tackled by recent deep neural networks, trained by an avalanche of annotated training samples from some common classes. However, it is still non-trivial to generalize these object detectors to the novel long-tailed object classes, which have only few labeled training samples. To this end, the Few-Shot Object Detection (FSOD) has been topical recently, as it mimics the humans' ability of learning to learn, and intelligently transfers the learned generic object knowledge from the common heavy-tailed, to the novel long-tailed object classes. Especially, the research in this emerging field has been flourishing in recent years with various benchmarks, backbones, and methodologies proposed. To review these FSOD works, there are several insightful FSOD survey articles [58, 59, 74, 78] that systematically study and compare them as the groups of fine-tuning/transfer learning, and meta-learning methods. In contrast, we review the existing FSOD algorithms from a new perspective under a new taxonomy based on their contributions, i.e., data-oriented, model-oriented, and algorithm-oriented. Thus, a comprehensive survey with performance comparison is conducted on recent achievements of FSOD. Furthermore, we also analyze the technical challenges, the merits and demerits of these methods, and envision the future directions of FSOD. Specifically, we give an overview of FSOD, including the problem definition, common datasets, and evaluation protocols. The taxonomy is then proposed that groups FSOD methods into three types. Following this taxonomy, we provide a systematic review of the advances in FSOD. Finally, further discussions on performance, challenges, and future directions are presented.

cs.CV↗

arXiv2023

High-rate sub-GHz linewidth bichromatic entanglement source for quantum networking

The generation of entangled photon pairs which are compatible with quantum devices and standard telecommunication channels are critical for the development of long range fiber quantum networks. Aside from wavelength, bandwidth matching and high fidelity of produced pairs are necessary for high interfacing efficiency. High-rate, robust entanglement sources that satisfy all these conditions remain an outstanding experimental challenge. In this work, we study an entanglement source based on four-wave mixing in a diamond configuration in a warm rubidium vapor. We theoretically and experimentally investigate a new operating regime and demonstrate an entanglement source which produces highly non-degenerate $795$ and $1324$-nm photon pairs. With this source we are able to achieve in-fiber entangled pair generation rates greater than $10^7\, /s$, orders of magnitude higher than previously reported atomic sources. Additionally, given our source's native compatibility with telecom infrastructure and atomic systems, it is an important step towards scalable quantum networks.

quant-ph↗

arXiv2023

Scalable High-Rate Twin-Field Quantum Key Distribution Networks without Constraint of Probability and Intensity

Implementation of a twin-field quantum key distribution network faces limitations, including the low tolerance of interference errors for phase-matching type protocols and the strict constraint regarding intensity and probability for sending-or-not-sending type protocols. Here, we propose a two-photon twin-field quantum key distribution protocol and achieve twin-field-type two-photon interference through post-matching phase-correlated single-photon interference events. We exploit the non-interference mode as the code mode to highly tolerate interference errors, and the two-photon interference naturally removes the intensity and probability constraint. Therefore, our protocol can transcend the abovementioned limitations while breaking the secret key capacity of repeaterless quantum key distribution. Simulations show that for a four-user networks, under which each node with fixed system parameters can dynamically switch different attenuation links, the key rates of our protocol for all six links can either exceed or approach the secret key capacity. However, the key rates of all links are lower than the key capacity when using phase-matching type protocols. Additionally, four of the links could not extract the key when using sending-or-not-sending type protocols. We anticipate that our protocol can facilitate the development of practical and efficient quantum networks.

quant-ph↗

arXiv2023

Delving into Discrete Normalizing Flows on SO(3) Manifold for Probabilistic Rotation Modeling

Normalizing flows (NFs) provide a powerful tool to construct an expressive distribution by a sequence of trackable transformations of a base distribution and form a probabilistic model of underlying data. Rotation, as an important quantity in computer vision, graphics, and robotics, can exhibit many ambiguities when occlusion and symmetry occur and thus demands such probabilistic models. Though much progress has been made for NFs in Euclidean space, there are no effective normalizing flows without discontinuity or many-to-one mapping tailored for SO(3) manifold. Given the unique non-Euclidean properties of the rotation manifold, adapting the existing NFs to SO(3) manifold is non-trivial. In this paper, we propose a novel normalizing flow on SO(3) by combining a Mobius transformation-based coupling layer and a quaternion affine transformation. With our proposed rotation normalizing flows, one can not only effectively express arbitrary distributions on SO(3), but also conditionally build the target distribution given input observations. Extensive experiments show that our rotation normalizing flows significantly outperform the baselines on both unconditional and conditional tasks.

cs.CV↗

arXiv2023

Safe Zeroth-Order Optimization Using Linear Programs

To solve unmodeled optimization problems with hard constraints, this paper proposes a novel zeroth-order approach called Safe Zeroth-order Optimization using Linear Programs (SZO-LP). The SZO-LP method solves a linear program in each iteration to find a descent direction, followed by a step length determination. We prove that, under mild conditions, the iterates of SZO-LP have an accumulation point that is also the primal of a KKT pair. We then apply SZO-LP to solve an Optimal Power Flow (OPF) problem on the IEEE 30-bus system. The results demonstrate that SZO-LP requires less computation time and samples compared to state-of-the-art approaches.

math.OC↗

arXiv2023

Approximation bounds for norm constrained neural networks with applications to regression and GANs

This paper studies the approximation capacity of ReLU neural networks with norm constraint on the weights. We prove upper and lower bounds on the approximation error of these networks for smooth function classes. The lower bound is derived through the Rademacher complexity of neural networks, which may be of independent interest. We apply these approximation bounds to analyze the convergences of regression using norm constrained neural networks and distribution estimation by GANs. In particular, we obtain convergence rates for over-parameterized neural networks. It is also shown that GANs can achieve optimal rate of learning probability distributions, when the discriminator is a properly chosen norm constrained neural network.

cs.LG↗

arXiv2023

Ferrimagnetic Regulation of Weyl Fermions in a Noncentrosymmetric Magnetic Weyl Semimetal

The study of interaction between electromagnetism and elementary particles is a long-standing topic in physics. Likewise, the connection between particle physics and emergent phenomena in condensed matter physics is a recurring theme and condensed matter physics has often provided a platform for investigating the interplay between particles and fields in cases that have not been observed in high-energy physics, so far. Here, using angle-resolved photoemission spectroscopy, we provide a new example of this by visualizing the electronic structure of a noncentrosymmetric magnetic Weyl semimetal candidate NdAlSi in both the paramagnetic and ferrimagnetic states. We observe surface Fermi arcs and bulk Weyl fermion dispersion as well as the regulation of Weyl fermions by ferrimagnetism. Our results establish NdAlSi as a magnetic Weyl semimetal and provide the first experimental observation of ferrimagnetic regulation of Weyl fermions in condensed matter.

cond-mat.str-el↗

arXiv2023

Experimental quantum secret sharing based on phase encoding of coherent states

Quantum secret sharing (QSS) is one of the basic communication primitives in future quantum networks which addresses part of the basic cryptographic tasks of multiparty communication and computation. Nevertheless, it is a challenge to provide a practical QSS protocol with security against general attacks. A QSS protocol that balances security and practicality is still lacking. Here, we propose a QSS protocol with simple phase encoding of coherent states among three parties. Removing the requirement of impractical entangled resources and the need for phase randomization, our protocol can be implemented with accessible technology. We provide the finite-key analysis against coherent attacks and implement a proof-of-principle experiment to demonstrate our scheme's feasibility. Our scheme achieves a key rate of 85.3 bps under a 35 dB channel loss. Combined with security against general attacks and accessible technology, our protocol is a promising candidate for practical multiparty quantum communication networks.

quant-ph↗