arXiv subjects

Ye Wang

Publications and source records attributed to Ye Wang.

At least 289 records · Page 16Linked to original sources

arXiv2022

The Evolution of Blockchain: from Lit to Dark

Transactions submitted through the blockchain peer-to-peer (P2P) network may leak out exploitable information. We study the economic incentives behind the adoption of blockchain dark venues, where users' transactions are observable only by miners on these venues. We show that miners may not fully adopt dark venues to preserve rents extracted from arbitrageurs, hence creating execution risk for users. The dark venue neither eliminates frontrunning risk nor reduces transaction costs. It strictly increases the payoff of miners, weakly increases the payoff of users, and weakly reduces arbitrageurs' profits. We provide empirical support for our main implications, and show that they are economically significant. A 1% increase in the probability of being frontrun raises users' adoption rate of the dark venue by 0.6%. Arbitrageurs' cost-to-revenue ratio increases by a third with a dark venue.

q-fin.GN↗

arXiv2022

Multi-Band Wi-Fi Sensing with Matched Feature Granularity

Complementary to the fine-grained channel state information (CSI) from the physical layer and coarse-grained received signal strength indicator (RSSI) measurements, the mid-grained spatial beam attributes (e.g., beam SNR) that are available at millimeter-wave (mmWave) bands during the mandatory beam training phase can be repurposed for Wi-Fi sensing applications. In this paper, we propose a multi-band Wi-Fi fusion method for Wi-Fi sensing that hierarchically fuses the features from both the fine-grained CSI at sub-6 GHz and the mid-grained beam SNR at 60 GHz in a granularity matching framework. The granularity matching is realized by pairing two feature maps from the CSI and beam SNR at different granularity levels and linearly combining all paired feature maps into a fused feature map with learnable weights. To further address the issue of limited labeled training data, we propose an autoencoder-based multi-band Wi-Fi fusion network that can be pre-trained in an unsupervised fashion. Once the autoencoder-based fusion network is pre-trained, we detach the decoders and append multi-task sensing heads to the fused feature map by fine-tuning the fusion block and re-training the multi-task heads from the scratch. The multi-band Wi-Fi fusion framework is thoroughly validated by in-house experimental Wi-Fi sensing datasets spanning three tasks: 1) pose recognition; 2) occupancy sensing; and 3) indoor localization. Comparison to four baseline methods (i.e., CSI-only, beam SNR-only, input fusion, and feature fusion) demonstrates the granularity matching improves the multi-task sensing performance. Quantitative performance is evaluated as a function of the number of labeled training data, latent space dimension, and fine-tuning learning rates.

cs.NI↗

arXiv2022

Cyclic Arbitrage in Decentralized Exchanges

Decentralized Exchanges (DEXes) enable users to create markets for exchanging any pair of cryptocurrencies. The direct exchange rate of two tokens may not match the cross-exchange rate in the market, and such price discrepancies open up arbitrage possibilities with trading through different cryptocurrencies cyclically. In this paper, we conduct a systematic investigation on cyclic arbitrages in DEXes. We propose a theoretical framework for studying cyclic arbitrage. With our framework, we analyze the profitability conditions and optimal trading strategies of cyclic transactions. We further examine exploitable arbitrage opportunities and the market size of cyclic arbitrages with transaction-level data of Uniswap V2. We find that traders have executed 292,606 cyclic arbitrages over eleven months and exploited more than 138 million USD in revenue. However, the revenue of the most profitable unexploited opportunity is persistently higher than 1 ETH (4,000 USD), which indicates that DEX markets may not be efficient enough. By analyzing how traders implement cyclic arbitrages, we find that traders can utilize smart contracts to issue atomic transactions and the atomic implementations could mitigate users' financial loss in cyclic arbitrage from the price impact.

q-fin.TR↗

arXiv2021

Significant-loophole-free test of Kochen-Specker contextuality using two species of atomic-ions

Quantum measurements cannot be thought of as revealing preexisting results, even when they do not disturb any other measurement in the same trial. This feature is called contextuality and is crucial for the quantum advantage in computing. Here, we report the first observation of quantum contextuality simultaneously free of the detection, sharpness and compatibility loopholes. The detection and sharpness loopholes are closed by adopting a hybrid two-ion system and highly efficient fluorescence measurements offering a detection efficiency of $100\%$ and a measurement repeatability $>98\%$. The compatibility loophole is closed by targeting correlations between observables for two different ions in a Paul trap, a $^{171}\mathrm{Yb}^{+}$ ion and a $^{138}\mathrm{Ba}^{+}$ ion, chosen so measurements on each ion use different operation laser wavelengths, fluorescence wavelengths, and detectors. The experimental results show a violation of the bound for the most adversarial noncontextual models and open a new way to certify quantum systems.

quant-ph↗

arXiv2021

Batch Optimization of Frequency-Modulated Pulses for Robust Two-qubit Gates in Ion Chains

Two-qubit gates in trapped-ion quantum computers are generated by applying spin-dependent forces that temporarily entangle the internal state of the ion with its motion. Laser pulses are carefully designed to generate a maximally entangling gate between the ions while minimizing any residual entanglement between the motion and the ion. The quality of the gates suffers when the actual experimental parameters differ from the ideal case. Here, we improve the robustness of frequency-modulated Mølmer-Sørensen gates to motional mode-frequency offsets by optimizing the average performance over a range of systematic errors using batch optimization. We then compare this method with frequency-modulated gates optimized for ideal parameters that include an analytic robustness condition. Numerical simulations show good performance up to 12 ions, and the method is experimentally demonstrated on a two-ion chain.

quant-ph↗

arXiv2021

The Dust Mass of Supernova Remnants in M31

The dust temperature and mass of the supernova remnants (SNRs) in M31 are estimated by fitting the infrared spectral energy distribution calculated from the images in the Spitzer/IRAC4 and MIPS24, Herschel/PACS70, 100, 160, and Herschel/SPIRE250, 350$μ$m band. Twenty SNRs with relatively reliable photometry exhibit an average dust temperature of $20.1^{+1.8}_{-1.5}$K, which is higher than the surrounding and indicating the heating effect of supernova explosion. The dust mass of these SNRs ranges from about 100 to 800$ M_{\odot}$, much bigger than the SNRs in the Milky Way. On the other hand, this yields the dust surface density of $0.10^{+0.07}_{-0.04}{ M_{\odot} \rm pc^{-2}}$, about half of the surrounding area, which implies that about half dust in the SNRs is destroyed by the supernova explosion. The dust temperature, the radius, and thus the dust mass all demonstrate that the studied SNRs are old and very likely in the snowplow or even fade away phase because of the limitation by the far distance and observation resolution of M31, and the results can serve as a reference to the final effect of supernova explosion on the surrounding dust.

astro-ph.GA↗

arXiv2021

Ramsey numbers of graphs with most degrees bounded in random graphs

For graphs $F$ and $G$, let $F\to G$ signify that any red/blue edge coloring of $F$ contains a monochromatic $G$. Denote by ${\cal G}(N,p)$ the random graph space of order $N$ and edge probability $p$. Using the regularity method, one can show that for any fixed $p\in (0,1]$, almost all graphs $F\in{\cal G}(cn,p)$ have $F\to G$ for any graph $G$ of order $n$ and all but at most $m$ degrees bounded, where $c$ is an integer depending on $p$ and $m$. Note that $r(K_{m,n})\sim 2^m n$ and $r(K_m+\overline{K}_n)\sim 2^m n$ as $n\to\infty$, for which we investigate the relation between $c$ and $p$. Let $N=\lfloor c\,2^m n\rfloor$ with $c>1$ and $p_u,p_\ell=\frac{1}{c^{1/m}}(1\pm \sqrt{\frac{M\log n}{n}}\,)$, where $M=M(c,m)>0$. It is shown that $p_u$ and $p_\ell$ are Ramsey thresholds of $K_{m,n}$ in ${\cal G}(N,p)$. Namely, almost all $F\in{\cal G}(N,p_u)$ and almost no $F\in{\cal G}(N,p_\ell)$ have $F\to K_{m,n}$, respectively. Moreover, it is shown that $p_u$ and $p_\ell$ are (ordinary) upper threshold and lower threshold of $K_m+\overline{K}_n$ to appear in ${\cal G}(N,p/2)$, respectively. We show that ${\cal G}(N,p/2)$ can be identified as the set of red (or blue) graphs obtained from $F\in{\cal G}(N,p)$ by red/blue edge coloring of $F$ with probability $1/2$ for each color, which leads to the definition of the weak Ramsey thresholds.

math.CO↗

arXiv2021

MOST-GAN: 3D Morphable StyleGAN for Disentangled Face Image Manipulation

Recent advances in generative adversarial networks (GANs) have led to remarkable achievements in face image synthesis. While methods that use style-based GANs can generate strikingly photorealistic face images, it is often difficult to control the characteristics of the generated faces in a meaningful and disentangled way. Prior approaches aim to achieve such semantic control and disentanglement within the latent space of a previously trained GAN. In contrast, we propose a framework that a priori models physical attributes of the face such as 3D shape, albedo, pose, and lighting explicitly, thus providing disentanglement by design. Our method, MOST-GAN, integrates the expressive power and photorealism of style-based GANs with the physical disentanglement and flexibility of nonlinear 3D morphable models, which we couple with a state-of-the-art 2D hair manipulation network. MOST-GAN achieves photorealistic manipulation of portrait images with fully disentangled 3D control over their physical attributes, enabling extreme manipulation of lighting, facial expression, and pose variations up to full profile view.

cs.CV↗

arXiv2021

CGNN: Traffic Classification with Graph Neural Network

Traffic classification associates packet streams with known application labels, which is vital for network security and network management. With the rise of NAT, port dynamics, and encrypted traffic, it is increasingly challenging to obtain unified traffic features for accurate classification. Many state-of-the-art traffic classifiers automatically extract features from the packet stream based on deep learning models such as convolution networks. Unfortunately, the compositional and causal relationships between packets are not well extracted in these deep learning models, which affects both prediction accuracy and generalization on different traffic types. In this paper, we present a chained graph model on the packet stream to keep the chained compositional sequence. Next, we propose CGNN, a graph neural network based traffic classification method, which builds a graph classifier over automatically extracted features over the chained graph. Extensive evaluation over real-world traffic data sets, including normal, encrypted and malicious labels, show that, CGNN improves the prediction accuracy by 23\% to 29\% for application classification, by 2\% to 37\% for malicious traffic classification, and reaches the same accuracy level for encrypted traffic classification. CGNN is quite robust in terms of the recall and precision metrics. We have extensively evaluated the parameter sensitivity of CGNN, which yields optimized parameters that are quite effective for traffic classification.

cs.LG↗

arXiv2021

Behavior of Liquidity Providers in Decentralized Exchanges

Decentralized exchanges (DEXes) have introduced an innovative trading mechanism, where it is not necessary to match buy-orders and sell-orders to execute a trade. DEXes execute each trade individually, and the exchange rate is automatically determined by the ratio of assets reserved in the market. Therefore, apart from trading, financial players can also liquidity providers, benefiting from transaction fees from trades executed in DEXes. Although liquidity providers are essential for the functionality of DEXes, it is not clear how liquidity providers behave in such markets. In this paper, we aim to understand how liquidity providers react to market information and how they benefit from providing liquidity in DEXes. We measure the operations of liquidity providers on Uniswap and analyze how they determine their investment strategy based on market changes. We also reveal their returns and risks of investments in different trading pair categories, i.e., stable pairs, normal pairs, and exotic pairs. Further, we investigate the movement of liquidity between trading pools. To the best of our knowledge, this is the first work that systematically studies the behavior of liquidity providers in DEXes.

q-fin.CP↗

arXiv2021

Multi-Semantic Image Recognition Model and Evaluating Index for explaining the deep learning models

Although deep learning models are powerful among various applications, most deep learning models are still a black box, lacking verifiability and interpretability, which means the decision-making process that human beings cannot understand. Therefore, how to evaluate deep neural networks with explanations is still an urgent task. In this paper, we first propose a multi-semantic image recognition model, which enables human beings to understand the decision-making process of the neural network. Then, we presents a new evaluation index, which can quantitatively assess the model interpretability. We also comprehensively summarize the semantic information that affects the image classification results in the judgment process of neural networks. Finally, this paper also exhibits the relevant baseline performance with current state-of-the-art deep learning models.

cs.CV↗

arXiv2021

A Biologically Plausible Audio-Visual Integration Model for Continual Learning

The problem of catastrophic forgetting has a history of more than 30 years and has not been completely solved yet. Since the human brain has natural ability to perform continual lifelong learning, learning from the brain may provide solutions to this problem. In this paper, we propose a novel biologically plausible audio-visual integration model (AVIM) based on the assumption that the integration of audio and visual perceptual information in the medial temporal lobe during learning is crucial to form concepts and make continual learning possible. Specifically, we use multi-compartment Hodgkin-Huxley neurons to build the model and adopt the calcium-based synaptic tagging and capture as the model's learning rule. Furthermore, we define a new continual learning paradigm to simulate the possible continual learning process in the human brain. We then test our model under this new paradigm. Our experimental results show that the proposed AVIM can achieve state-of-the-art continual learning performance compared with other advanced methods such as OWM, iCaRL and GEM. Moreover, it can generate stable representations of objects during learning. These results support our assumption that concept formation is essential for continuous lifelong learning and suggest the proposed AVIM is a possible concept formation mechanism.

cs.NE↗

arXiv2021

Best Subset Selection: Statistical Computing Meets Quantum Computing

With the rapid development of quantum computers, quantum algorithms have been studied extensively. However, quantum algorithms tackling statistical problems are still lacking. In this paper, we propose a novel non-oracular quantum adaptive search (QAS) method for the best subset selection problems. QAS performs almost identically to the naive best subset selection method but reduces its computational complexity from $O(D)$ to $O(\sqrt{D}\log_2D)$, where $D=2^p$ is the total number of subsets over $p$ covariates. Unlike existing quantum search algorithms, QAS does not require the oracle information of the true solution state and hence is applicable to various statistical learning problems with random observations. Theoretically, we prove QAS attains any arbitrary success probability $q \in (0.5, 1)$ within $O(\log_2D)$ iterations. When the underlying regression model is linear, we propose a quantum linear prediction method that is faster than its classical counterpart. We further introduce a hybrid quantum-classical strategy to avoid the capacity bottleneck of existing quantum computing systems and boost the success probability of QAS by majority voting. The effectiveness of this strategy is justified by both theoretical analysis and extensive empirical experiments on quantum and classical computers.

stat.ME↗

arXiv2021

STRODE: Stochastic Boundary Ordinary Differential Equation

Perception of time from sequentially acquired sensory inputs is rooted in everyday behaviors of individual organisms. Yet, most algorithms for time-series modeling fail to learn dynamics of random event timings directly from visual or audio inputs, requiring timing annotations during training that are usually unavailable for real-world applications. For instance, neuroscience perspectives on postdiction imply that there exist variable temporal ranges within which the incoming sensory inputs can affect the earlier perception, but such temporal ranges are mostly unannotated for real applications such as automatic speech recognition (ASR). In this paper, we present a probabilistic ordinary differential equation (ODE), called STochastic boundaRy ODE (STRODE), that learns both the timings and the dynamics of time series data without requiring any timing annotations during training. STRODE allows the usage of differential equations to sample from the posterior point processes, efficiently and analytically. We further provide theoretical guarantees on the learning of STRODE. Our empirical results show that our approach successfully infers event timings of time series data. Our method achieves competitive or superior performances compared to existing state-of-the-art methods for both synthetic and real-world datasets.

cs.LG↗

arXiv2021

How to Account for Alternatives When Comparing Effects: Revisiting 'Bringing Education to Afghan Girls'

This paper uses a "principal strata" approach to decompose treatment effects and interpret why a schooling intervention that yielded exceptional initial effects yielded substantially smaller effects in a replication years later. The specific application is a set of 2008 and 2015 replications of an intervention aiming to increase primary education for girls in rural Afghanistan. The intervention offers a new schooling option, and as such, its effects depend on how individuals use alternatives that already exist. The principal strata approach accounts variation in use patterns when comparing effects across the replications. Our findings show that even though the share of girls for whom the intervention would be valuable dropped considerably in 2015 as compared to 2008, the intervention was even more efficaciousness for those who continued to benefit from it.

stat.AP↗

arXiv2021

EEG-GNN: Graph Neural Networks for Classification of Electroencephalogram (EEG) Signals

Convolutional neural networks (CNN) have been frequently used to extract subject-invariant features from electroencephalogram (EEG) for classification tasks. This approach holds the underlying assumption that electrodes are equidistant analogous to pixels of an image and hence fails to explore/exploit the complex functional neural connectivity between different electrode sites. We overcome this limitation by tailoring the concepts of convolution and pooling applied to 2D grid-like inputs for the functional network of electrode sites. Furthermore, we develop various graph neural network (GNN) models that project electrodes onto the nodes of a graph, where the node features are represented as EEG channel samples collected over a trial, and nodes can be connected by weighted/unweighted edges according to a flexible policy formulated by a neuroscientist. The empirical evaluations show that our proposed GNN-based framework outperforms standard CNN classifiers across ErrP, and RSVP datasets, as well as allowing neuroscientific interpretability and explainability to deep learning methods tailored to EEG related classification problems. Another practical advantage of our GNN-based framework is that it can be used in EEG channel selection, which is critical for reducing computational cost, and designing portable EEG headsets.

cs.LG↗

arXiv2021

Robust Machine Learning via Privacy/Rate-Distortion Theory

Robust machine learning formulations have emerged to address the prevalent vulnerability of deep neural networks to adversarial examples. Our work draws the connection between optimal robust learning and the privacy-utility tradeoff problem, which is a generalization of the rate-distortion problem. The saddle point of the game between a robust classifier and an adversarial perturbation can be found via the solution of a maximum conditional entropy problem. This information-theoretic perspective sheds light on the fundamental tradeoff between robustness and clean data performance, which ultimately arises from the geometric structure of the underlying data distribution and perturbation constraints.

cs.LG↗

arXiv2021

Personalized Popular Music Generation Using Imitation and Structure

Many practices have been presented in music generation recently. While stylistic music generation using deep learning techniques has became the main stream, these models still struggle to generate music with high musicality, different levels of music structure, and controllability. In addition, more application scenarios such as music therapy require imitating more specific musical styles from a few given music examples, rather than capturing the overall genre style of a large data corpus. To address requirements that challenge current deep learning methods, we propose a statistical machine learning model that is able to capture and imitate the structure, melody, chord, and bass style from a given example seed song. An evaluation using 10 pop songs shows that our new representations and methods are able to create high-quality stylistic music that is similar to a given input song. We also discuss potential uses of our approach in music evaluation and music therapy.

cs.SD↗