arXiv subjects

Yu Zhang

Publications and source records attributed to Yu Zhang.

At least 793 records · Page 44Linked to original sources

arXiv2024

An Improved Algorithm to Identify More Arbitrage Opportunities on Decentralized Exchanges

In decentralized exchanges (DEXs), the arbitrage paths exist abundantly in the form of both arbitrage loops (e.g. the arbitrage path starts from token A and back to token A again in the end, A, B,..., A) and non-loops (e.g. the arbitrage path starts from token A and stops at a different token N, A, B,..., N). The Moore-Bellman-Ford algorithm, often coupled with the ``walk to the root" technique, is commonly employed for detecting arbitrage loops in the token graph of decentralized exchanges (DEXs) such as Uniswap. However, a limitation of this algorithm is its ability to recognize only a limited number of arbitrage loops in each run. Additionally, it cannot specify the starting token of the detected arbitrage loops, further constraining its effectiveness in certain scenarios. Another limitation of this algorithm is its incapacity to detect non-loop arbitrage paths between any specified pairs of tokens. In this paper, we develop a new method to solve these problems by combining the line graph and a modified Moore-Bellman-Ford algorithm (MMBF). This method can help to find more arbitrage loops by detecting at least one arbitrage loop starting from any specified tokens in the DEXs and can detect the non-loop arbitrage paths between any pair of tokens. Then, we applied our algorithm to Uniswap V2 and found more arbitrage loops and non-loops indeed compared with applying the Moore-Bellman-Ford (MBF) combined algorithm. The found arbitrage profit by our method in some arbitrage paths can be even as high as one million dollars, far larger than that found by the MBF combined algorithm. Finally, we statistically compare the distribution of arbitrage path lengths and the arbitrage profit detected by both our method and the MBF combined algorithm, and depict how potential arbitrage opportunities change with time by our method.

q-fin.CP↗

arXiv2024

Velocity, Holding Time and Lifespan of Cryptocurrency in Transactions

The measurement of the velocity of money is still a significant topic. In this paper, we proposed a method to calculate the velocity of money by combining the holding-time distribution and lifespan distribution. By derivation, the velocity of money equals the holding-time distribution's value at zero. When we have much holding-time data, this problem can be converted to a regression problem. After a numeric simulation, we find that the calculating accuracy is high even if we used only a small part of the holding time data, which implies a potential application in measuring the velocity of money in reality, such as digital money. We also tested the methods on Cardano and found that the method can also provide a reasonable estimation of velocity in some cases.

econ.GN↗

arXiv2024

Profit Maximization In Arbitrage Loops

Cyclic arbitrage chances exist abundantly among decentralized exchanges (DEXs), like Uniswap V2. For an arbitrage cycle (loop), researchers or practitioners usually choose a specific token, such as Ether as input, and optimize their input amount to get the net maximal amount of the specific token as arbitrage profit. By considering the tokens' prices from CEXs in this paper, the new arbitrage profit, called monetized arbitrage profit, will be quantified as the product of the net number of a specific token we got from the arbitrage loop and its corresponding price in CEXs. Based on this concept, we put forward three different strategies to maximize the monetized arbitrage profit for each arbitrage loop. The first strategy is called the MaxPrice strategy. Under this strategy, arbitrageurs start arbitrage only from the token with the highest CEX price. The second strategy is called the MaxMax strategy. Under this strategy, we calculate the monetized arbitrage profit for each token as input in turn in the arbitrage loop. Then, we pick up the most maximal monetized arbitrage profit among them as the monetized arbitrage profit of the MaxMax strategy. The third one is called the Convex Optimization strategy. By mapping the MaxMax strategy to a convex optimization problem, we proved that the Convex Optimization strategy could get more profit in theory than the MaxMax strategy, which is proved again in a given example. We also proved that if no arbitrage profit exists according to the MaxMax strategy, then the Convex Optimization strategy can not detect any arbitrage profit, either. However, the empirical data analysis denotes that the profitability of the Convex Optimization strategy is almost equal to that of the MaxMax strategy, and the MaxPrice strategy is not reliable in getting the maximal monetized arbitrage profit compared to the MaxMax strategy.

q-fin.CP↗

arXiv2024

Direct Production of Light Scalar in the Type-I Two-Higgs-Doublet Model at the Lifetime Frontier of LHC

A light pseudoscalar $A$ in the sufficient large $\tanβ$ region of type-I two-Higgs-doublet model (2HDM) can be naturally a long-lived particle (LLP). We focus on $H^{\pm}A$, $HA$ and $AA$ pair productions via the electroweak processes mediated by the bosons at the LHC, including $pp \rightarrow W^\pm/Z \rightarrow H^{\pm}/H A$ and $pp \rightarrow h \rightarrow AA$ at the 14 TeV LHC. The possibility of probing $A$ as a LLP at the FASER-2, FACET, MoEDAL-MAPP-2, MATHUSLA is discussed. We find that FASER-2 fails to probe any parameter space within 0.2 GeV $< m_A <$ 10 GeV for all the considered processes. For 130 $< m_{H\pm} = m_H <$ 400 GeV, FACET, MoEDAL-MAPP-2 and MATHUSLA can probe $\tan β\lesssim 10^{4-6}$ for $m_A \lesssim 3$ GeV, and $\tan β\lesssim 10^{6-8}$ for 3 GeV $\lesssim m_A <$ 10 GeV from $pp \rightarrow W^\pm/Z \rightarrow H^{\pm}/Z A$ processes. And $pp \rightarrow h \rightarrow AA$ process covers similar parameter space. All processes can surpass the current limits.

hep-ph↗

arXiv2024

Ultrafast carrier dynamics throughout the three-dimensional Brillouin zone of the Weyl semimetal PtBi$_2$

Using time- and angle-resolved photoemission spectroscopy, we examine the unoccupied electronic structure and electron dynamics of the type-I Weyl semimetal PtBi$_2$. Using the ability to change the probe photon energy over a wide range, we identify the predicted Weyl points in the unoccupied three-dimensional band structure and we discuss the effect of $k_\perp$ broadening in the normally unoccupied states. We characterise the electron dynamics close to the Weyl point and in other parts of three-dimensional Brillouin zone using $k$-means, an unsupervised machine learning technique. This reveals distinct differences -- in particular, dynamics that are faster in the parts of the Brillouin zone that host most of the bulk Fermi surface than in parts close to the Weyl points.

cond-mat.str-el↗

arXiv2024

Poetry2Image: An Iterative Correction Framework for Images Generated from Chinese Classical Poetry

Text-to-image generation models often struggle with key element loss or semantic confusion in tasks involving Chinese classical poetry.Addressing this issue through fine-tuning models needs considerable training costs. Additionally, manual prompts for re-diffusion adjustments need professional knowledge. To solve this problem, we propose Poetry2Image, an iterative correction framework for images generated from Chinese classical poetry. Utilizing an external poetry dataset, Poetry2Image establishes an automated feedback and correction loop, which enhances the alignment between poetry and image through image generation models and subsequent re-diffusion modifications suggested by large language models (LLM). Using a test set of 200 sentences of Chinese classical poetry, the proposed method--when integrated with five popular image generation models--achieves an average element completeness of 70.63%, representing an improvement of 25.56% over direct image generation. In tests of semantic correctness, our method attains an average semantic consistency of 80.09%. The study not only promotes the dissemination of ancient poetry culture but also offers a reference for similar non-fine-tuning methods to enhance LLM generation.

cs.CV↗

arXiv2024

Three-dimensional quantum Griffiths singularity in bulk iron-pnictide superconductors

The quantum Griffiths singularity (QGS) is a phenomenon driven by quenched disorders that break conventional scaling invariance and result in a divergent dynamical critical exponent during quantum phase transitions (QPT). While this phenomenon has been well-documented in low-dimensional conventional superconductors and in three-dimensional (3D) magnetic metal systems, its presence in 3D superconducting systems and in unconventional high-temperature superconductors (high-Tc SCs) remains unclear. In this study, we report the observation of robust QGS in the superconductor-metal transition (SMT) of both quasi-2D and 3D anisotropic unconventional high-Tc superconductor CaFe1-xNixAsF (x < 5%) bulk single crystals, where the QGS states persist to up to 5.3 K. A comprehensive quantum phase diagram is established that delineates the 3D anisotropic QGS of SMT induced by perpendicular and parallel magnetic field. Our findings reveal the universality of QGS in 3D superconducting systems and unconventional high-Tc SCs, thereby substantially expanding the range of applicability of QGS.

cond-mat.str-el↗

arXiv2024

Multi-reference Quantum Davidson Algorithm for Quantum Dynamics

Simulating quantum systems is one of the most promising tasks where quantum computing can potentially outperform classical computing. However, the robustness needed for reliable simulations of medium to large systems is beyond the reach of existing quantum devices. To address this, Quantum Krylov Subspace (QKS) methods have been developed, enhancing the ability to perform accelerated simulations on noisy intermediate-scale quantum computers. In this study, we introduce and evaluate two QKS methods derived from the QDavidson algorithm, a novel approach for determining the ground and excited states of many-body systems. Unlike other QKS methods that pre-generate the Krylov subspace through real- or imaginary-time evolution, QDavidson iteratively adds basis vectors into the Krylov subspace. This iterative process enables faster convergence with fewer iterations and necessitates shallower circuit depths, marking a significant advancement in the field of quantum simulation.

quant-ph↗

arXiv2024

On Security Weaknesses and Vulnerabilities in Deep Learning Systems

The security guarantee of AI-enabled software systems (particularly using deep learning techniques as a functional core) is pivotal against the adversarial attacks exploiting software vulnerabilities. However, little attention has been paid to a systematic investigation of vulnerabilities in such systems. A common situation learned from the open source software community is that deep learning engineers frequently integrate off-the-shelf or open-source learning frameworks into their ecosystems. In this work, we specifically look into deep learning (DL) framework and perform the first systematic study of vulnerabilities in DL systems through a comprehensive analysis of identified vulnerabilities from Common Vulnerabilities and Exposures (CVE) and open-source DL tools, including TensorFlow, Caffe, OpenCV, Keras, and PyTorch. We propose a two-stream data analysis framework to explore vulnerability patterns from various databases. We investigate the unique DL frameworks and libraries development ecosystems that appear to be decentralized and fragmented. By revisiting the Common Weakness Enumeration (CWE) List, which provides the traditional software vulnerability related practices, we observed that it is more challenging to detect and fix the vulnerabilities throughout the DL systems lifecycle. Moreover, we conducted a large-scale empirical study of 3,049 DL vulnerabilities to better understand the patterns of vulnerability and the challenges in fixing them. We have released the full replication package at https://github.com/codelzz/Vulnerabilities4DLSystem. We anticipate that our study can advance the development of secure DL systems.

cs.SE↗

arXiv2024

Forward-Backward Reasoning in Large Language Models for Mathematical Verification

Self-Consistency samples diverse reasoning chains with answers and chooses the final answer by majority voting. It is based on forward reasoning and cannot further improve performance by sampling more reasoning chains when saturated. To further boost performance, we introduce backward reasoning to verify candidate answers. Specifically, for mathematical tasks, we mask a number in the question and ask the LLM to answer a backward question created by a simple template, i.e., to predict the masked number when a candidate answer is provided. Instead of using forward or backward reasoning alone, we propose FOBAR to combine FOrward and BAckward Reasoning for verification. Extensive experiments on six standard mathematical data sets and three LLMs show that FOBAR achieves state-of-the-art performance. In particular, FOBAR outperforms Self-Consistency, which uses forward reasoning alone, demonstrating that combining forward and forward reasoning is better. In addition, FOBAR performs better than existing verification methods, showing the effectiveness of the simple template used in backward reasoning and the proposed combination. Extensions to non-mathematical problems are also discussed and validated empirically.

cs.CL↗

arXiv2024

Rethinking Guidance Information to Utilize Unlabeled Samples:A Label Encoding Perspective

Empirical Risk Minimization (ERM) is fragile in scenarios with insufficient labeled samples. A vanilla extension of ERM to unlabeled samples is Entropy Minimization (EntMin), which employs the soft-labels of unlabeled samples to guide their learning. However, EntMin emphasizes prediction discriminability while neglecting prediction diversity. To alleviate this issue, in this paper, we rethink the guidance information to utilize unlabeled samples. By analyzing the learning objective of ERM, we find that the guidance information for labeled samples in a specific category is the corresponding label encoding. Inspired by this finding, we propose a Label-Encoding Risk Minimization (LERM). It first estimates the label encodings through prediction means of unlabeled samples and then aligns them with their corresponding ground-truth label encodings. As a result, the LERM ensures both prediction discriminability and diversity, and it can be integrated into existing methods as a plugin. Theoretically, we analyze the relationships between LERM and ERM as well as EntMin. Empirically, we verify the superiority of the LERM under several label insufficient scenarios. The codes are available at https://github.com/zhangyl660/LERM.

cs.CV↗

arXiv2024

UDQL: Bridging The Gap between MSE Loss and The Optimal Value Function in Offline Reinforcement Learning

The Mean Square Error (MSE) is commonly utilized to estimate the solution of the optimal value function in the vast majority of offline reinforcement learning (RL) models and has achieved outstanding performance. However, we find that its principle can lead to overestimation phenomenon for the value function. In this paper, we first theoretically analyze overestimation phenomenon led by MSE and provide the theoretical upper bound of the overestimated error. Furthermore, to address it, we propose a novel Bellman underestimated operator to counteract overestimation phenomenon and then prove its contraction characteristics. At last, we propose the offline RL algorithm based on underestimated operator and diffusion policy model. Extensive experimental results on D4RL tasks show that our method can outperform state-of-the-art offline RL algorithms, which demonstrates that our theoretical analysis and underestimation way are effective for offline RL tasks.

cs.LG↗

arXiv2024

MLIP: Efficient Multi-Perspective Language-Image Pretraining with Exhaustive Data Utilization

Contrastive Language-Image Pretraining (CLIP) has achieved remarkable success, leading to rapid advancements in multimodal studies. However, CLIP faces a notable challenge in terms of inefficient data utilization. It relies on a single contrastive supervision for each image-text pair during representation learning, disregarding a substantial amount of valuable information that could offer richer supervision. Additionally, the retention of non-informative tokens leads to increased computational demands and time costs, particularly in CLIP's ViT image encoder. To address these issues, we propose Multi-Perspective Language-Image Pretraining (MLIP). In MLIP, we leverage the frequency transform's sensitivity to both high and low-frequency variations, which complements the spatial domain's sensitivity limited to low-frequency variations only. By incorporating frequency transforms and token-level alignment, we expand CILP's single supervision into multi-domain and multi-level supervision, enabling a more thorough exploration of informative image features. Additionally, we introduce a token merging method guided by comprehensive semantics from the frequency and spatial domains. This allows us to merge tokens to multi-granularity tokens with a controllable compression rate to accelerate CLIP. Extensive experiments validate the effectiveness of our design.

cs.CV↗

arXiv2024

Negative Prototypes Guided Contrastive Learning for WSOD

Weakly Supervised Object Detection (WSOD) with only image-level annotation has recently attracted wide attention. Many existing methods ignore the inter-image relationship of instances which share similar characteristics while can certainly be determined not to belong to the same category. Therefore, in order to make full use of the weak label, we propose the Negative Prototypes Guided Contrastive learning (NPGC) architecture. Firstly, we define Negative Prototype as the proposal with the highest confidence score misclassified for the category that does not appear in the label. Unlike other methods that only utilize category positive feature, we construct an online updated global feature bank to store both positive prototypes and negative prototypes. Meanwhile, we propose a pseudo label sampling module to mine reliable instances and discard the easily misclassified instances based on the feature similarity with corresponding prototypes in global feature bank. Finally, we follow the contrastive learning paradigm to optimize the proposal's feature representation by attracting same class samples closer and pushing different class samples away in the embedding space. Extensive experiments have been conducted on VOC07, VOC12 datasets, which shows that our proposed method achieves the state-of-the-art performance.

cs.CV↗

arXiv2024

Robust Singing Voice Transcription Serves Synthesis

Note-level Automatic Singing Voice Transcription (AST) converts singing recordings into note sequences, facilitating the automatic annotation of singing datasets for Singing Voice Synthesis (SVS) applications. Current AST methods, however, struggle with accuracy and robustness when used for practical annotation. This paper presents ROSVOT, the first robust AST model that serves SVS, incorporating a multi-scale framework that effectively captures coarse-grained note information and ensures fine-grained frame-level segmentation, coupled with an attention-based pitch decoder for reliable pitch prediction. We also established a comprehensive annotation-and-training pipeline for SVS to test the model in real-world settings. Experimental findings reveal that ROSVOT achieves state-of-the-art transcription accuracy with either clean or noisy inputs. Moreover, when trained on enlarged, automatically annotated datasets, the SVS model outperforms its baseline, affirming the capability for practical application. Audio samples are available at https://rosvot.github.io.

eess.AS↗

arXiv2024

iMove: Exploring Bio-impedance Sensing for Fitness Activity Recognition

Automatic and precise fitness activity recognition can be beneficial in aspects from promoting a healthy lifestyle to personalized preventative healthcare. While IMUs are currently the prominent fitness tracking modality, through iMove, we show bio-impedence can help improve IMU-based fitness tracking through sensor fusion and contrastive learning.To evaluate our methods, we conducted an experiment including six upper body fitness activities performed by ten subjects over five days to collect synchronized data from bio-impedance across two wrists and IMU on the left wrist.The contrastive learning framework uses the two modalities to train a better IMU-only classification model, where bio-impedance is only required at the training phase, by which the average Macro F1 score with the input of a single IMU was improved by 3.22 \% reaching 84.71 \% compared to the 81.49 \% of the IMU baseline model. We have also shown how bio-impedance can improve human activity recognition (HAR) directly through sensor fusion, reaching an average Macro F1 score of 89.57 \% (two modalities required for both training and inference) even if Bio-impedance alone has an average macro F1 score of 75.36 \%, which is outperformed by IMU alone. In addition, similar results were obtained in an extended study on lower body fitness activity classification, demonstrating the generalisability of our approach.Our findings underscore the potential of sensor fusion and contrastive learning as valuable tools for advancing fitness activity recognition, with bio-impedance playing a pivotal role in augmenting the capabilities of IMU-based systems.

eess.SP↗

arXiv2024

SMPLX-Lite: A Realistic and Drivable Avatar Benchmark with Rich Geometry and Texture Annotations

Recovering photorealistic and drivable full-body avatars is crucial for numerous applications, including virtual reality, 3D games, and tele-presence. Most methods, whether reconstruction or generation, require large numbers of human motion sequences and corresponding textured meshes. To easily learn a drivable avatar, a reasonable parametric body model with unified topology is paramount. However, existing human body datasets either have images or textured models and lack parametric models which fit clothes well. We propose a new parametric model SMPLX-Lite-D, which can fit detailed geometry of the scanned mesh while maintaining stable geometry in the face, hand and foot regions. We present SMPLX-Lite dataset, the most comprehensive clothing avatar dataset with multi-view RGB sequences, keypoints annotations, textured scanned meshes, and textured SMPLX-Lite-D models. With the SMPLX-Lite dataset, we train a conditional variational autoencoder model that takes human pose and facial keypoints as input, and generates a photorealistic drivable human avatar.

cs.CV↗

arXiv2024

Realization of 2/3-layer transition metal dichalcogenides

Layered van der Waals transition metal dichalcogenides (TMDCs), generally composed of three atomic X-M-X planes in each layer (M = transition metal, X = chalcogen), provide versatile platforms for exploring diverse quantum phenomena. In each MX2 layer, the M-X bonds are predominantly covalent in nature, as a result, the cleavage of TMDC crystals always occurring between the layers. Here we report the controllable realization of fractional-layer WTe2 via an in-situ scanning tunnelling microscopy (STM) tip manipulation technique. By applying STM tip pulses, hundreds of the topmost Te atoms are removed to form a nanoscale monolayer Te pit in the 1T'-WTe2, thus realizing a brand-new 2/3-layer WTe2. Such a unique configuration undergoes a spontaneous atomic reconstruction, yielding an energy-dependent unidirectional charge-density-wave state with the wavevector and geometry quite distinct from that of pristine 1T'-WTe2. Our results expand the conventional understanding of the TMDCs and are expected to stimulate the research on extraordinary structures and properties based on fractional-layer TMDCs.

cond-mat.mes-hall↗