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Zihao Li

Publications and source records attributed to Zihao Li.

At least 217 records · Page 12Linked to original sources
arXiv2023

Demystifying DeFi MEV Activities in Flashbots Bundle

Decentralized Finance, mushrooming in permissionless blockchains, has attracted a recent surge in popularity. Due to the transparency of permissionless blockchains, opportunistic traders can compete to earn revenue by extracting Miner Extractable Value (MEV), which undermines both the consensus security and efficiency of blockchain systems. The Flashbots bundle mechanism further aggravates the MEV competition because it empowers opportunistic traders with the capability of designing more sophisticated MEV extraction. In this paper, we conduct the first systematic study on DeFi MEV activities in Flashbots bundle by developing ActLifter, a novel automated tool for accurately identifying DeFi actions in transactions of each bundle, and ActCluster, a new approach that leverages iterative clustering to facilitate us to discover known/unknown DeFi MEV activities. Extensive experimental results show that ActLifter can achieve nearly 100% precision and recall in DeFi action identification, significantly outperforming state-of-the-art techniques. Moreover, with the help of ActCluster, we obtain many new observations and discover 17 new kinds of DeFi MEV activities, which occur in 53.12% of bundles but have not been reported in existing studies

cs.CR↗
arXiv2023

Robust and efficient verification of graph states in blind measurement-based quantum computation

Blind quantum computation (BQC) is a secure quantum computation method that protects the privacy of clients. Measurement-based quantum computation (MBQC) is a promising approach for realizing BQC. To obtain reliable results in blind MBQC, it is crucial to verify whether the resource graph states are accurately prepared in the adversarial scenario. However, previous verification protocols for this task are too resource consuming or noise susceptible to be applied in practice. Here, we propose a robust and efficient protocol for verifying arbitrary graph states with any prime local dimension in the adversarial scenario, which leads to a robust and efficient protocol for verifying the resource state in blind MBQC. Our protocol requires only local Pauli measurements and is thus easy to realize with current technologies. Nevertheless, it can achieve the optimal scaling behaviors with respect to the system size and the target precision as quantified by the infidelity and significance level, which has never been achieved before. Notably, our protocol can exponentially enhance the scaling behavior with the significance level.

quant-ph↗
arXiv2023

UnifiedVisionGPT: Streamlining Vision-Oriented AI through Generalized Multimodal Framework

In the current landscape of artificial intelligence, foundation models serve as the bedrock for advancements in both language and vision domains. OpenAI GPT-4 has emerged as the pinnacle in large language models (LLMs), while the computer vision (CV) domain boasts a plethora of state-of-the-art (SOTA) models such as Meta's SAM and DINO, and YOLOS. However, the financial and computational burdens of training new models from scratch remain a significant barrier to progress. In response to this challenge, we introduce UnifiedVisionGPT, a novel framework designed to consolidate and automate the integration of SOTA vision models, thereby facilitating the development of vision-oriented AI. UnifiedVisionGPT distinguishes itself through four key features: (1) provides a versatile multimodal framework adaptable to a wide range of applications, building upon the strengths of multimodal foundation models; (2) seamlessly integrates various SOTA vision models to create a comprehensive multimodal platform, capitalizing on the best components of each model; (3) prioritizes vision-oriented AI, ensuring a more rapid progression in the CV domain compared to the current trajectory of LLMs; and (4) introduces automation in the selection of SOTA vision models, generating optimal results based on diverse multimodal inputs such as text prompts and images. This paper outlines the architecture and capabilities of UnifiedVisionGPT, demonstrating its potential to revolutionize the field of computer vision through enhanced efficiency, versatility, generalization, and performance. Our implementation, along with the unified multimodal framework and comprehensive dataset, is made publicly available at https://github.com/LHBuilder/SA-Segment-Anything.

cs.CV↗
arXiv2023

A SPectroscopic survey of biased halos In the Reionization Era (ASPIRE): JWST Discovers an Overdensity around a Metal Absorption-selected Galaxy at $z\sim5.5$

The launch of ${\it JWST}$ opens a new window for studying the connection between metal-line absorbers and galaxies at the end of the Epoch of Reionization (EoR). Previous studies have detected absorber-galaxy pairs in limited quantities through ground-based observations. To enhance our understanding of the relationship between absorbers and their host galaxies at $z>5$, we utilized the NIRCam Wide Field Slitless Spectroscopy (WFSS) to search for absorber-associated galaxies by detecting their rest-frame optical emission lines (e.g., [OIII] + H$β$). We report the discovery of a MgII-associated galaxy at $z=5.428$ using data from the ${\it JWST}$ ASPIRE program. The MgII absorber is detected on the spectrum of quasar J0305--3150 with a rest-frame equivalent width of 0.74$\mathring{A}$. The associated galaxy has an [OIII] luminosity of $10^{42.5}\ {\rm erg\ s^{-1}}$ with an impact parameter of 24.9 proper kiloparsecs (pkpc). The joint ${\it HST}$-${\it JWST}$ spectral energy distribution (SED) implies a stellar mass and star-formation rate of ${\rm M_* \approx 10^{8.8}}$ ${\rm M_{\odot}}$, ${\rm SFR}\approx 10\ {\rm M_{\odot}\ yr^{-1}}$. Its [OIII] equivalent width and stellar mass are typical of [OIII] emitters at this redshift. Furthermore, connecting the outflow starting time to the SED-derived stellar age, the outflow velocity of this galaxy is $\sim300\ {\rm km\ s^{-1}}$, consistent with theoretical expectations. We identified six additional [OIII] emitters with impact parameters of up to $\sim300$ pkpc at similar redshifts ($|dv|<1000\ {\rm km\ s^{-1}}$). The observed number is consistent with that in cosmological simulations. This pilot study suggests that systematically investigating the absorber-galaxy connection within the ASPIRE program will provide insights into the metal-enrichment history in the early universe.

astro-ph.GA↗
arXiv2023

DiffuRec: A Diffusion Model for Sequential Recommendation

Mainstream solutions to Sequential Recommendation (SR) represent items with fixed vectors. These vectors have limited capability in capturing items' latent aspects and users' diverse preferences. As a new generative paradigm, Diffusion models have achieved excellent performance in areas like computer vision and natural language processing. To our understanding, its unique merit in representation generation well fits the problem setting of sequential recommendation. In this paper, we make the very first attempt to adapt Diffusion model to SR and propose DiffuRec, for item representation construction and uncertainty injection. Rather than modeling item representations as fixed vectors, we represent them as distributions in DiffuRec, which reflect user's multiple interests and item's various aspects adaptively. In diffusion phase, DiffuRec corrupts the target item embedding into a Gaussian distribution via noise adding, which is further applied for sequential item distribution representation generation and uncertainty injection. Afterward, the item representation is fed into an Approximator for target item representation reconstruction. In reverse phase, based on user's historical interaction behaviors, we reverse a Gaussian noise into the target item representation, then apply a rounding operation for target item prediction. Experiments over four datasets show that DiffuRec outperforms strong baselines by a large margin.

cs.IR↗
arXiv2023

MAGNIF: A Tentative Lensed Rotating Disk at $z=8.34$ detected by JWST NIRCam WFSS with Dynamical Forward Modeling

We report galaxy MACS0416-Y3 behind the lensing cluster MACSJ0416.1--2403 as a tentative rotating disk at $z=8.34$ detected through its [OIII]$\lambda5007$ emission in JWST NIRCam wide-field slitless spectroscopic observations. The discovery is based on our new grism dynamical modeling methodology for JWST NIRCam slitless spectroscopy, using the data from ``Median-band Astrophysics with the Grism of NIRCam in Frontier Fields'' (MAGNIF), a JWST Cycle-2 program. The [OIII]$\lambda5007$ emission line morphology in grism data shows velocity offsets compared to the F480M direct imaging, suggestive of rotation. Assuming a geometrically thin disk model, we constrain the rotation velocity of $v_{\rm rot}=58^{+53}_{-35}$ km s$^{-1}$ via forward modeling of the two-dimensional (2D) spectrum. We obtain the kinematic ratio of $v_{\rm rot}/σ_v=1.6^{+1.9}_{-0.9}$, where $σ_v$ is the velocity dispersion, in line with a quasi-stable thin disk. The resulting dynamical mass is estimated to be $\log(M_{\rm dyn}/M_{\odot})=8.4^{+0.5}_{-0.7}$. If the rotation confirmed, our discovery suggests that rotating gaseous disks may have already existed within 600 million years after Big Bang.

astro-ph.GA↗
arXiv2023

The Mass-Metallicity Relation of Dwarf Galaxies at Cosmic Noon from JWST Observations

We present a study of the mass-metallicity relation (MZR) of 51 dwarf galaxies ($M_\star\approx 10^{6.5} - 10^{9.5}~M_\odot$) at $z = 2-3$ from the Abell 2744 and SMACS J0723-3732 galaxy cluster fields. These dwarf galaxies are identified and confirmed by deep JWST/NIRISS imaging and slitless grism spectroscopic observations. By taking advantage of the superior performance of JWST and the gravitational lensing effect, we extend the previous MZR relation at $z=2-3$ to a much lower mass regime down by $\approx$ 2.5 orders of magnitude as compared with previous studies. We find that the MZR has a shallower slope at the low-mass end ($M_\star<10^{9}~M_\odot$), with a slope turnover point of $\approx$ $10^9~M_\odot$. This implies that the dominating feedback processes in dwarf galaxies may be different from that in massive galaxies. From $z=3$ to $z=2$, the metallicity of the dwarf galaxies is enhanced by $\approx0.09$ dex for a given stellar mass, consistent with the mild evolution found in galaxies with higher mass. Furthermore, we confirm the existence of a fundamental metallicity relation (FMR) between the gas-phase metallicity, stellar mass, and star formation rate in dwarf galaxies at $z=2-3$. Our derived FMR, which has no significant redshift evolution, can be used as a benchmark to understand the origin of the anti-correlation between the SFR and metallicity of dwarf galaxies in the high-$z$ Universe.

astro-ph.GA↗
arXiv2023

Laminar: A New Serverless Stream-based Framework with Semantic Code Search and Code Completion

This paper introduces Laminar, a novel serverless framework based on dispel4py, a parallel stream-based dataflow library. Laminar efficiently manages streaming workflows and components through a dedicated registry, offering a seamless serverless experience. Leveraging large lenguage models, Laminar enhances the framework with semantic code search, code summarization, and code completion. This contribution enhances serverless computing by simplifying the execution of streaming computations, managing data streams more efficiently, and offering a valuable tool for both researchers and practitioners.

cs.DC↗
arXiv2023

A Review of Gaps between Web 4.0 and Web 3.0 Intelligent Network Infrastructure

World Wide Web is speeding up its pace into an intelligent and decentralized ecosystem, as seen in the campaign of Web 3.0 and forthcoming Web 4.0. Marked by the Europe Commission's latest mention of Web 4.0, a race towards strategic Web 4.0 success has started. Web 4.0 is committed to bringing the next technological transition with an open, secure, trustworthy fairness and digital ecosystem for individuals and businesses in private and public sectors. Despite overlapping scopes and objectives of Web 3.0 and Web 4.0 from academic and industrial perspectives, there are distinct and definitive features and gaps for the next generation of WWW. In this review, a brief introduction to WWW development unravels the entangled but consistent requirement of a more vivid web experience, enhancing human-centric experience in both societal and technical aspects. Moreover, the review brings a decentralized intelligence prospect of view on native AI entities for Web 4.0, envisioning sustainable, autonomous and decentralized AI services for the entire Web 4.0 environment, powering a self-sustainable Decentralized Physical and Software Infrastructure for Computing Force Network, Semantic Network, Virtual/Mixed Reality, and Privacy-preserving content presumption. The review aims to reveal that Web 4.0 offers native intelligence with focused thinking on utilizing decentralized physical infrastructure, in addition to sole requirements on decentralization, bridging the gap between Web 4.0 and Web 3.0 advances with the latest future-shaping blockchain-enabled computing and network routing protocols.

cs.NI↗
arXiv2023

DeepGATGO: A Hierarchical Pretraining-Based Graph-Attention Model for Automatic Protein Function Prediction

Automatic protein function prediction (AFP) is classified as a large-scale multi-label classification problem aimed at automating protein enrichment analysis to eliminate the current reliance on labor-intensive wet-lab methods. Currently, popular methods primarily combine protein-related information and Gene Ontology (GO) terms to generate final functional predictions. For example, protein sequences, structural information, and protein-protein interaction networks are integrated as prior knowledge to fuse with GO term embeddings and generate the ultimate prediction results. However, these methods are limited by the difficulty in obtaining structural information or network topology information, as well as the accuracy of such data. Therefore, more and more methods that only use protein sequences for protein function prediction have been proposed, which is a more reliable and computationally cheaper approach. However, the existing methods fail to fully extract feature information from protein sequences or label data because they do not adequately consider the intrinsic characteristics of the data itself. Therefore, we propose a sequence-based hierarchical prediction method, DeepGATGO, which processes protein sequences and GO term labels hierarchically, and utilizes graph attention networks (GATs) and contrastive learning for protein function prediction. Specifically, we compute embeddings of the sequence and label data using pre-trained models to reduce computational costs and improve the embedding accuracy. Then, we use GATs to dynamically extract the structural information of non-Euclidean data, and learn general features of the label dataset with contrastive learning by constructing positive and negative example samples. Experimental results demonstrate that our proposed model exhibits better scalability in GO term enrichment analysis on large-scale datasets.

q-bio.QM↗
arXiv2023

Correlation-Aware Mutual Learning for Semi-supervised Medical Image Segmentation

Semi-supervised learning has become increasingly popular in medical image segmentation due to its ability to leverage large amounts of unlabeled data to extract additional information. However, most existing semi-supervised segmentation methods only focus on extracting information from unlabeled data, disregarding the potential of labeled data to further improve the performance of the model. In this paper, we propose a novel Correlation Aware Mutual Learning (CAML) framework that leverages labeled data to guide the extraction of information from unlabeled data. Our approach is based on a mutual learning strategy that incorporates two modules: the Cross-sample Mutual Attention Module (CMA) and the Omni-Correlation Consistency Module (OCC). The CMA module establishes dense cross-sample correlations among a group of samples, enabling the transfer of label prior knowledge to unlabeled data. The OCC module constructs omni-correlations between the unlabeled and labeled datasets and regularizes dual models by constraining the omni-correlation matrix of each sub-model to be consistent. Experiments on the Atrial Segmentation Challenge dataset demonstrate that our proposed approach outperforms state-of-the-art methods, highlighting the effectiveness of our framework in medical image segmentation tasks. The codes, pre-trained weights, and data are publicly available.

cs.CV↗
arXiv2023

Reinforcement Learning with Human Feedback: Learning Dynamic Choices via Pessimism

In this paper, we study offline Reinforcement Learning with Human Feedback (RLHF) where we aim to learn the human's underlying reward and the MDP's optimal policy from a set of trajectories induced by human choices. RLHF is challenging for multiple reasons: large state space but limited human feedback, the bounded rationality of human decisions, and the off-policy distribution shift. In this paper, we focus on the Dynamic Discrete Choice (DDC) model for modeling and understanding human choices. DCC, rooted in econometrics and decision theory, is widely used to model a human decision-making process with forward-looking and bounded rationality. We propose a \underline{D}ynamic-\underline{C}hoice-\underline{P}essimistic-\underline{P}olicy-\underline{O}ptimization (DCPPO) method. \ The method involves a three-stage process: The first step is to estimate the human behavior policy and the state-action value function via maximum likelihood estimation (MLE); the second step recovers the human reward function via minimizing Bellman mean squared error using the learned value functions; the third step is to plug in the learned reward and invoke pessimistic value iteration for finding a near-optimal policy. With only single-policy coverage (i.e., optimal policy) of the dataset, we prove that the suboptimality of DCPPO almost matches the classical pessimistic offline RL algorithm in terms of suboptimality's dependency on distribution shift and dimension. To the best of our knowledge, this paper presents the first theoretical guarantees for off-policy offline RLHF with dynamic discrete choice model.

cs.LG↗
arXiv2023

Provably Efficient Representation Learning with Tractable Planning in Low-Rank POMDP

In this paper, we study representation learning in partially observable Markov Decision Processes (POMDPs), where the agent learns a decoder function that maps a series of high-dimensional raw observations to a compact representation and uses it for more efficient exploration and planning. We focus our attention on the sub-classes of \textit{$γ$-observable} and \textit{decodable POMDPs}, for which it has been shown that statistically tractable learning is possible, but there has not been any computationally efficient algorithm. We first present an algorithm for decodable POMDPs that combines maximum likelihood estimation (MLE) and optimism in the face of uncertainty (OFU) to perform representation learning and achieve efficient sample complexity, while only calling supervised learning computational oracles. We then show how to adapt this algorithm to also work in the broader class of $γ$-observable POMDPs.

cs.LG↗
arXiv2023

deController: A Web3 Native Cyberspace Infrastructure Perspective

Web3 brings an emerging outlook for the value of decentralization, boosting the decentralized infrastructure. People can benefit from Web3, facilitated by the advances in distributed ledger technology, to read, write and own web content, services and applications more freely without revealing their real identities. Although the features and merits of Web3 have been widely discussed, the network architecture of Web3 and how to achieve complete decentralization considering law compliance in Web3 are still unclear. Here, we propose a perspective of Web3 architecture, deController, consisting of underlay and overlay network as Web3 infrastructures to underpin services and applications. The functions of underlay and overlay and their interactions are illustrated. Meanwhile, the security and privacy of Web3 are analyzed based on a novel design of three-tier identities cooperating with deController. Furthermore, the impacts of laws on privacy and cyber sovereignty to achieve Web3 are discussed.

cs.NI↗
arXiv2023

Truthful Fair Mechanisms for Allocating Mixed Divisible and Indivisible Goods

We study the problem of designing truthful and fair mechanisms when allocating a mixture of divisible and indivisible goods. We first show that there does not exist an EFM (envy-free for mixed goods) and truthful mechanism in general. This impossibility result holds even if there is only one indivisible good and one divisible good and there are only two agents. Thus, we focus on some more restricted settings. Under the setting where agents have binary valuations on indivisible goods and identical valuations on a single divisible good (e.g., money), we design an EFM and truthful mechanism. When agents have binary valuations over both divisible and indivisible goods, we first show there exist EFM and truthful mechanisms when there are only two agents or when there is a single divisible good. On the other hand, we show that the mechanism maximizing Nash welfare cannot ensure EFM and truthfulness simultaneously.

cs.GT↗
arXiv2023

M$^2$DAR: Multi-View Multi-Scale Driver Action Recognition with Vision Transformer

Ensuring traffic safety and preventing accidents is a critical goal in daily driving, where the advancement of computer vision technologies can be leveraged to achieve this goal. In this paper, we present a multi-view, multi-scale framework for naturalistic driving action recognition and localization in untrimmed videos, namely M$^2$DAR, with a particular focus on detecting distracted driving behaviors. Our system features a weight-sharing, multi-scale Transformer-based action recognition network that learns robust hierarchical representations. Furthermore, we propose a new election algorithm consisting of aggregation, filtering, merging, and selection processes to refine the preliminary results from the action recognition module across multiple views. Extensive experiments conducted on the 7th AI City Challenge Track 3 dataset demonstrate the effectiveness of our approach, where we achieved an overlap score of 0.5921 on the A2 test set. Our source code is available at \url{https://github.com/PurdueDigitalTwin/M2DAR}.

cs.CV↗
arXiv2023

SigRec: Automatic Recovery of Function Signatures in Smart Contracts

Millions of smart contracts have been deployed onto Ethereum for providing various services, whose functions can be invoked. For this purpose, the caller needs to know the function signature of a callee, which includes its function id and parameter types. Such signatures are critical to many applications focusing on smart contracts, e.g., reverse engineering, fuzzing, attack detection, and profiling. Unfortunately, it is challenging to recover the function signatures from contract bytecode, since neither debug information nor type information is present in the bytecode. To address this issue, prior approaches rely on source code, or a collection of known signatures from incomplete databases or incomplete heuristic rules, which, however, are far from adequate and cannot cope with the rapid growth of new contracts. In this paper, we propose a novel solution that leverages how functions are handled by Ethereum virtual machine (EVM) to automatically recover function signatures. In particular, we exploit how smart contracts determine the functions to be invoked to locate and extract function ids, and propose a new approach named type-aware symbolic execution (TASE) that utilizes the semantics of EVM operations on parameters to identify the number and the types of parameters. Moreover, we develop SigRec, a new tool for recovering function signatures from contract bytecode without the need of source code and function signature databases. The extensive experimental results show that SigRec outperforms all existing tools, achieving an unprecedented 98.7 percent accuracy within 0.074 seconds. We further demonstrate that the recovered function signatures are useful in attack detection, fuzzing and reverse engineering of EVM bytecode.

cs.CR↗
arXiv2023

Recordism: A social-scientific prospect of blockchain from social, legal, financial, and technological perspectives

Blockchain technology has the potential to revolutionize the architecture of cyberspace by transforming the way information is stored, circulated, and exchanged in cyberspace through decentralization, transparency, and de-identification. This means that ordinary participants can simultaneously become traders, miners, retailers, and customers, thus breaking down barriers, reducing the information gap between participants in the community, and contributing to the futuristic metaverse with an open, progressive, and equal ideology. The impact of this information transformation empowered by blockchain extends to our understanding of methodology, legal governance in cyberspace, and financial and technological development. This study asks: what are the implications of the blockchain-driven information revolution for society and social sciences? In order to answer this main question, the paper focuses on four key perspectives: methodological, legal, financial, and technical. Through the analysis of these four perspectives, the paper provides a comprehensive understanding of the impact of blockchain on society, the social sciences, and technology, making a contribution to current scholarship. It finds that blockchain is not only an innovative cognition method, but also a community representative, serving as a source of trust, a governance watchdog, an enforcer of cyber laws, and an incubator for future technologies. Despite some challenges in integrating blockchain with existing social structures, this paper concludes that blockchain has the potential to play a significant role in shaping the future.

cs.CY↗