Search

We present a flow-based method for simulating and calculating nucleation rates of first-order phase transitions in scalar field theory on a lattice. Motivated by recent advancements in machine learning tools, particularly normalizing flows for lattice field theory, we propose the ``partitioning flow-based Markov chain Monte Carlo (PFMCMC) sampling" method to address two challenges encountered in normalizing flow applications for lattice field theory: the ``mode-collapse" and ``rare-event sampling" problems. Using a (2+1)-dimensional real scalar model as an example, we demonstrate the effectiveness of our PFMCMC method in modeling highly hierarchical order parameter probability distributions and simulating critical bubble configurations. These simulations are then used to facilitate the calculation of nucleation rates. We anticipate the application of this method to (3+1)-dimensional theories for studying realistic cosmological phase transitions., Comment: 31 pages, 14 figures, 1 table

Understanding and leveraging the 3D structures of proteins is central to a variety of biological and drug discovery tasks. While deep learning has been applied successfully for structure-based protein function prediction tasks, current methods usually employ distinct training for each task. However, each of the tasks is of small size, and such a single-task strategy hinders the models' performance and generalization ability. As some labeled 3D protein datasets are biologically related, combining multi-source datasets for larger-scale multi-task learning is one way to overcome this problem. In this paper, we propose a neural network model to address multiple tasks jointly upon the input of 3D protein structures. In particular, we first construct a standard structure-based multi-task benchmark called Protein-MT, consisting of 6 biologically relevant tasks, including affinity prediction and property prediction, integrated from 4 public datasets. Then, we develop a novel graph neural network for multi-task learning, dubbed Heterogeneous Multichannel Equivariant Network (HeMeNet), which is E(3) equivariant and able to capture heterogeneous relationships between different atoms. Besides, HeMeNet can achieve task-specific learning via the task-aware readout mechanism. Extensive evaluations on our benchmark verify the effectiveness of multi-task learning, and our model generally surpasses state-of-the-art models.

Next-generation nano and quantum devices have increasingly complex 3D structure. As the dimensions of these devices shrink to the nanoscale, their performance is often governed by interface quality or precise chemical or dopant composition. Here we present the first phase-sensitive extreme ultraviolet imaging reflectometer. It combines the excellent phase stability of coherent high-harmonic sources, the unique chemical- and phase-sensitivity of extreme ultraviolet reflectometry, and state-of-the-art ptychography imaging algorithms. This tabletop microscope can non-destructively probe surface topography, layer thicknesses, and interface quality, as well as dopant concentrations and profiles. High-fidelity imaging was achieved by implementing variable-angle ptychographic imaging, by using total variation regularization to mitigate noise and artifacts in the reconstructed image, and by using a high-brightness, high-harmonic source with excellent intensity and wavefront stability. We validate our measurements through multiscale, multimodal imaging to show that this technique has unique advantages compared with other techniques based on electron and scanning-probe microscopies., Comment: 47 pages, 16 figures (4 in main text, 12 supplement) 2 tables

Large Language Models (LLMs) have emerged as powerful tools across various domains within cyber security. Notably, recent studies are increasingly exploring LLMs applied to the context of blockchain security (BS). However, there remains a gap in a comprehensive understanding regarding the full scope of applications, impacts, and potential constraints of LLMs on blockchain security. To fill this gap, we undertake a literature review focusing on the studies that apply LLMs in blockchain security (LLM4BS). Our study aims to comprehensively analyze and understand existing research, and elucidate how LLMs contribute to enhancing the security of blockchain systems. Through a thorough examination of existing literature, we delve into the integration of LLMs into various aspects of blockchain security. We explore the mechanisms through which LLMs can bolster blockchain security, including their applications in smart contract auditing, transaction anomaly detection, vulnerability repair, program analysis of smart contracts, and serving as participants in the cryptocurrency community. Furthermore, we assess the challenges and limitations associated with leveraging LLMs for enhancing blockchain security, considering factors such as scalability, privacy concerns, and ethical concerns. Our thorough review sheds light on the opportunities and potential risks of tasks on LLM4BS, providing valuable insights for researchers, practitioners, and policymakers alike.

In this paper, we generalize the Poincar\'e-Lyapunov method for systems with linear type centers to study nilpotent centers in switching polynomial systems and use it to investigate the bi-center problem of planar $Z_2$-equivariant cubic switching systems associated with two symmetric nilpotent singular points. With a properly designed perturbation, 6 explicit bi-center conditions for such polynomial systems are derived. Then, based on the $6$ center conditions, by using Bogdanov-Takens bifurcation theory with general perturbations, we prove that there exist at least $20$ small-amplitude limit cycles around the nilpotent bi-center for a class of $Z_2$-equivariant cubic switching systems. This is a new lower bound of cyclicity for such cubic polynomial systems, increased from $12$ to $20$.

In this paper, we explore a new generative approach for learning visual representations. Our method, DARL, employs a decoder-only Transformer to predict image patches autoregressively. We find that training with Mean Squared Error (MSE) alone leads to strong representations. To enhance the image generation ability, we replace the MSE loss with the diffusion objective by using a denoising patch decoder. We show that the learned representation can be improved by using tailored noise schedules and longer training in larger models. Notably, the optimal schedule differs significantly from the typical ones used in standard image diffusion models. Overall, despite its simple architecture, DARL delivers performance remarkably close to state-of-the-art masked prediction models under the fine-tuning protocol. This marks an important step towards a unified model capable of both visual perception and generation, effectively combining the strengths of autoregressive and denoising diffusion models.

In trapped ion system, accurate thermometry of ion is crucial for evaluating the system state and precisely performing quantum operations. However, when the motional state of a single ion is far away from the ground state, the spatial dimension of the phonon state sharply increases, making it difficult to realize accurate and mode-resolved thermometry with existing methods. In this work, we apply deep learning for the first time to the thermometry of trapped ion, providing an efficient and mode-resolved method for accurately estimating large mean phonon numbers. Our trained neural network model can be directly applied to other experimental setups without retraining or post-processing, as long as the related parameters are covered by the model's effective range, and it can also be conveniently extended to other parameter ranges. We have conducted experimental verification based on our surface trap, of which the result has shown the accuracy and efficiency of the method for thermometry of single ion under large mean phonon number, and its mode resolution characteristic can make it better applied to the characterization of system parameters, such as evaluating cooling effectiveness, analyzing surface trap noise.

The nature of the progenitor systems and explosion mechanisms that give rise to Type Ia supernovae (SNe Ia) are still debated. The interaction signature of circumstellar material (CSM) being swept up by expanding ejecta can constrain the type of system from which it was ejected. Most previous studies have focused on finding CSM ejected shortly before the SN Ia explosion still residing close to the explosion site, resulting in short delay times until the interaction starts. We use a sample of 3627 SNe Ia from the Zwicky Transient Facility discovered between 2018 and 2020 and search for interaction signatures over 100 days after peak brightness. By binning the late-time light curve data to push the detection limit as deep as possible, we identify potential late-time rebrightening in 3 SNe Ia (SN 2018grt, SN 2019dlf, SN 2020tfc). The late-time detections occur between 550 and 1450 d after peak brightness, have mean absolute $r$-band magnitudes of -16.4 to -16.8 mag and last up to a few hundred days, significantly brighter than the late-time CSM interaction discovered in the prototype SN 2015cp. The late-time detections all occur within 0.8 kpc of the host nucleus and are not easily explained by nuclear activity, another transient at a similar sky position, or data quality issues. This suggests environment or specific progenitor characteristics playing a role in producing potential CSM signatures in these SNe Ia. By simulating the ZTF survey we estimate that <0.5 per cent of normal SNe Ia display late-time strong H $\alpha$-dominated CSM interaction. This is equivalent to an absolute rate of $8_{-4}^{+20}$ to $54_{-26}^{+91}$ Gpc$^{-3}$ yr$^{-1}$ assuming a constant SN Ia rate of $2.4\times10^{-5}$ Mpc$^{-3}$ yr$^{-1}$ for $z \leq 0.1$. Weaker interaction signatures, more similar to the strength seen in SN 2015cp, could be more common but are difficult to constrain with our survey depth., Comment: 24 pages, 13 figures, 6 tables, A&A accepted

Generalist Large Language Models (LLMs), such as GPT-4, have shown considerable promise in various domains, including medical diagnosis. Rare diseases, affecting approximately 300 million people worldwide, often have unsatisfactory clinical diagnosis rates primarily due to a lack of experienced physicians and the complexity of differentiating among many rare diseases. In this context, recent news such as "ChatGPT correctly diagnosed a 4-year-old's rare disease after 17 doctors failed" underscore LLMs' potential, yet underexplored, role in clinically diagnosing rare diseases. To bridge this research gap, we introduce RareBench, a pioneering benchmark designed to systematically evaluate the capabilities of LLMs on 4 critical dimensions within the realm of rare diseases. Meanwhile, we have compiled the largest open-source dataset on rare disease patients, establishing a benchmark for future studies in this domain. To facilitate differential diagnosis of rare diseases, we develop a dynamic few-shot prompt methodology, leveraging a comprehensive rare disease knowledge graph synthesized from multiple knowledge bases, significantly enhancing LLMs' diagnostic performance. Moreover, we present an exhaustive comparative study of GPT-4's diagnostic capabilities against those of specialist physicians. Our experimental findings underscore the promising potential of integrating LLMs into the clinical diagnostic process for rare diseases. This paves the way for exciting possibilities in future advancements in this field., Comment: KDD2024

We present photometric and spectroscopic data sets for SN 2020pvb, a Type IIn-P supernova (SN) similar to SNe 1994W, 2005cl, 2009kn and 2011ht, with a precursor outburst detected (PS1 w-band ~ -13.8 mag) around four months before the B-band maximum light. SN 2020pvb presents a relatively bright light curve peaking at M_B = -17.95 +- 0.30 mag and a plateau lasting at least 40 days before it went in solar conjunction. After this, the object is no longer visible at phases > 150 days above -12.5 mag in the B-band, suggesting that the SN 2020pvb ejecta interacts with a dense spatially confined circumstellar envelope. SN 2020pvb shows in its spectra strong Balmer lines and a forest of FeII lines with narrow P Cygni profiles. Using archival images from the Hubble Space Telescope, we constrain the progenitor of SN 2020pvb to have a luminosity of log(L/L_sun) <= 5.4, ruling out any single star progenitor over 50 M_sun. All in all, SN 2020pvb is a Type IIn-P whose progenitor star had an outburst ~ 0.5 yr before the final explosion, the material lost during this outburst is probably playing a role in shaping the physical properties of the supernova., Comment: 14 pages + 10 appendix pages, 12 figures + 2 appendix figures, 8 appendix tables. Accepted for publication in A&A

Low-code programming (LCP) refers to programming using models at higher levels of abstraction, resulting in less manual and more efficient programming, and reduced learning effort for amateur developers. Many LCP tools have rapidly evolved and have benefited from the concepts of visual programming languages (VPLs) and programming by demonstration (PBD). With huge increase in interest in using large language models (LLMs) in software engineering, LLM-based LCP has began to become increasingly important. However, the technical principles and application scenarios of traditional approaches to LCP and LLM-based LCP are significantly different. Understanding these key differences and characteristics in the application of the two approaches to LCP by users is crucial for LCP providers in improving existing and developing new LCP tools, and in better assisting users in choosing the appropriate LCP technology. We conducted an empirical study of both traditional LCP and LLM-based LCP. We analyzed developers' discussions on Stack Overflow (SO) over the past three years and then explored the similarities and differences between traditional LCP and LLM-based LCP features and developer feedback. Our findings reveal that while traditional LCP and LLM-based LCP share common primary usage scenarios, they significantly differ in scope, limitations and usage throughout the software development lifecycle, particularly during the implementation phase. We also examine how LLMs impact and integrate with LCP, discussing the latest technological developments in LLM-based LCP, such as its integration with VPLs and the application of LLM Agents in software engineering.

We describe a set of new methods to partially automate linguistic phylogenetic inference given (1) cognate sets with their respective protoforms and sound laws, (2) a mapping from phones to their articulatory features and (3) a typological database of sound changes. We train a neural network on these sound change data to weight articulatory distances between phones and predict intermediate sound change steps between historical protoforms and their modern descendants, replacing a linguistic expert in part of a parsimony-based phylogenetic inference algorithm. In our best experiments on Tukanoan languages, this method produces trees with a Generalized Quartet Distance of 0.12 from a tree that used expert annotations, a significant improvement over other semi-automated baselines. We discuss potential benefits and drawbacks to our neural approach and parsimony-based tree prediction. We also experiment with a minimal generalization learner for automatic sound law induction, finding it comparably effective to sound laws from expert annotation. Our code is publicly available at https://github.com/cmu-llab/aiscp., Comment: Accepted to LChange 2023

An ancient and counterintuitive phenomenon know as the Mpemba effect (water can cool faster when initially heated up) showcases the critical role of initial conditions in relaxation processes. How to realize and utilize this effect for speeding up relaxation is an important but challenging task in purely quantum system till now. Here, we report the first experiment, as far as we know,about the strong Mpemba effect in a single trapped ion system in which an exponentially expedited relaxation in time is observed by preparing an optimal initial state with no excitation of the slowest decaying mode. Also, we find that the condition of realizing such effect coincides with the Liouvillian exceptional point, featuring the coalescence of both the eigenvalues and the eigenmodes of the system. Our work provides an efficient strategy to exponentially accelerate relaxations of quantum system to their stationary state, and suggests a link unexplored yet between the Mpemba effect and the non-Hermitian physics. It could open up the door to engineer a wide range of dissipative quantum systems by utilizing the anomalous Mpemba effect, for applications in quantum simulation and quantum information processing.

We present observational evidence of late-time interaction between the ejecta of the hydrogen-poor Type Ib supernova (SN) 2019yvr and hydrogen-rich circumstellar material (CSM), similar to the Type Ib SN 2014C. A narrow H{\alpha} emission line appears simultaneously with a break in the light-curve decline rate at around 80-100 d after explosion. From the interaction delay and the ejecta velocity, under the assumption that the CSM is detached from the progenitor, we estimate the CSM inner radius to be located at ~6.5-9.1 {\times} 10^{15} cm. The H{\alpha} emission line persists throughout the nebular phase at least up to +420 d post-explosion, with a full width at half maximum of ~2000 km/s. Assuming a steady mass-loss, the estimated mass-loss rate from the luminosity of the H{\alpha} line is ~3-7 {\times} 10^{-5} M_\odot yr^{-1}. From hydrodynamical modelling and analysis of the nebular spectra, we find a progenitor He-core mass of 3-4 M{_\odot}, which would imply an initial mass of 13-15 M{_\odot}. Our result supports the case of a relatively low-mass progenitor possibly in a binary system as opposed to a higher mass single star undergoing a luminous blue variable phase., Comment: 8 pages, 5 figures, published in MNRAS

We study nonuniform Sobolev spaces, i.e., spaces of functions whose partial derivatives lie in possibly different Lebesgue spaces. Although standard proofs do not apply, we show that nonuniform Sobolev spaces share similar properties as the classical ones. These spaces arise naturally in the study of certain PDEs. For instance, we illustrate that nonuniform fractional Sobolev spaces are useful in the study of local estimates for solutions of heat equations and the convergence of Schr\"odinger operators. In this work we extend recent advances on local energy estimates for solutions of heat equations and the convergence of Schr\"odinger operators to nonuniform fractional Sobolev spaces., Comment: 42 pages

We use soft x-ray vector-ptychographic tomography to determine the three-dimensional magnetization field in superparamagnetic nanoparticles self-assembled at the liquid-liquid interface and reveal the magnetic order induced by layered structure. The spins in individual nanoparticles become more aligned with increasing number of layers, resulting in a larger net magnetization. Our experimental results show a magnetic short-range order in the monolayer due to the proliferation of thermally induced magnetic vortices and a magnetic long-range order in the bilayer and trilayer, stemming from the strengthened dipolar interactions that effectively suppress thermal fluctuations. We also observe a screening effect of magnetic vortices and the attractive interaction between the magnetic vortices with opposite topological charges. Our work demonstrates the crucial role of layered structure in shaping the magnetization of nanoparticle assemblies, providing new opportunities to modulate these properties through strategic layer engineering.

CMS and ATLAS have reported small excesses in the search for low-mass Higgs bosons in the di-photon decay channel at exactly the same mass, $95.4~$GeV. These searches rely on improved analysis techniques, enhancing in particular the discrimination against the $Z \to e^+e^-$ background. In models beyond the Standard Model (SM) that extend the Higgs sector with triplets, doubly-charged Higgs bosons are predicted which can contribute substantially to the di-photon decay rate of a light Higgs boson. The Georgi-Machacek (GM) Model is of particular interest in this context, since despite containing Higgs triplets it preserves the electroweak $\rho$-parameter to be$~$1 at the tree level. We show that within the GM model, a Higgs boson with a mass of $\sim 95~$GeV with a di-photon decay rate as observed by CMS and ATLAS can be well described. We discuss the di-photon excess in conjunction with an excess in the $b \bar b$ final state observed at LEP and an excess observed by CMS in the di-tau final state, which have been found at comparable masses with local significances of about $2\sigma$ and $3\sigma$, respectively. The presence of a Higgs boson at about $95~$GeV within the GM model would imply good prospects of the searches for additional light Higgs bosons. In particular, the observed excess in the di-photon channel would be expected to be correlated in the GM model with a light doubly-charged Higgs boson in the mass range between $100~$GeV and $200~$GeV, which motivates dedicated searches in upcoming LHC Runs., Comment: 26 pages, 7 figures, 1 table, matches the published version

To empower smart contracts with the promising capabilities of cryptography, Ethereum officially introduced a set of cryptographic APIs that facilitate basic cryptographic operations within smart contracts, such as elliptic curve operations. However, since developers are not necessarily cryptography experts, requiring them to directly interact with these basic APIs has caused real-world security issues and potential usability challenges. To guide future research and solutions to these challenges, we conduct the first empirical study on Ethereum cryptographic practices. Through the analysis of 91,484,856 Ethereum transactions, 500 crypto-related contracts, and 483 StackExchange posts, we provide the first in-depth look at cryptographic tasks developers need to accomplish and identify five categories of obstacles they encounter. Furthermore, we conduct an online survey with 78 smart contract practitioners to explore their perspectives on these obstacles and elicit the underlying reasons. We find that more than half of practitioners face more challenges in cryptographic tasks compared to general business logic in smart contracts. Their feedback highlights the gap between low-level cryptographic APIs and high-level tasks they need to accomplish, emphasizing the need for improved cryptographic APIs, task-based templates, and effective assistance tools. Based on these findings, we provide practical implications for further improvements and outline future research directions., Comment: To appear at ICSE'24

Haze obscures remote sensing images, hindering valuable information extraction. To this end, we propose RSHazeNet, an encoder-minimal and decoder-minimal framework for efficient remote sensing image dehazing. Specifically, regarding the process of merging features within the same level, we develop an innovative module called intra-level transposed fusion module (ITFM). This module employs adaptive transposed self-attention to capture comprehensive context-aware information, facilitating the robust context-aware feature fusion. Meanwhile, we present a cross-level multi-view interaction module (CMIM) to enable effective interactions between features from various levels, mitigating the loss of information due to the repeated sampling operations. In addition, we propose a multi-view progressive extraction block (MPEB) that partitions the features into four distinct components and employs convolution with varying kernel sizes, groups, and dilation factors to facilitate view-progressive feature learning. Extensive experiments demonstrate the superiority of our proposed RSHazeNet. We release the source code and all pre-trained models at \url{https://github.com/chdwyb/RSHazeNet}.

Recent advancements in foundation models, typically trained with self-supervised learning on large-scale and diverse datasets, have shown great potential in medical image analysis. However, due to the significant spatial heterogeneity of medical imaging data, current models must tailor specific structures for different datasets, making it challenging to leverage the abundant unlabeled data. In this work, we propose a universal foundation model for medical image analysis that processes images with heterogeneous spatial properties using a unified structure. To accomplish this, we propose spatially adaptive networks (SPAD-Nets), a family of networks that dynamically adjust the structures to adapt to the spatial properties of input images, to build such a universal foundation model. We pre-train a spatial adaptive visual tokenizer (SPAD-VT) and then a spatial adaptive Vision Transformer (SPAD-ViT) via masked image modeling (MIM) on 55 public medical image datasets. The pre-training data comprises over 9 million image slices, representing the largest, most comprehensive, and most diverse dataset to our knowledge for pre-training universal foundation models for medical image analysis. The experimental results on downstream medical image classification and segmentation tasks demonstrate the superior performance and label efficiency of our model. Our code is available at https://github.com/function2-llx/PUMIT.

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, Comment: This submission serves as our full paper version with the appendix

Human visual recognition system shows astonishing capability of compressing visual information into a set of tokens containing rich representations without label supervision. One critical driving principle behind it is perceptual grouping. Despite being widely used in computer vision in the early 2010s, it remains a mystery whether perceptual grouping can be leveraged to derive a neural visual recognition backbone that generates as powerful representations. In this paper, we propose the Perceptual Group Tokenizer, a model that entirely relies on grouping operations to extract visual features and perform self-supervised representation learning, where a series of grouping operations are used to iteratively hypothesize the context for pixels or superpixels to refine feature representations. We show that the proposed model can achieve competitive performance compared to state-of-the-art vision architectures, and inherits desirable properties including adaptive computation without re-training, and interpretability. Specifically, Perceptual Group Tokenizer achieves 80.3% on ImageNet-1K self-supervised learning benchmark with linear probe evaluation, marking a new progress under this paradigm., Comment: The International Conference on Learning Representations (ICLR) 2024

In recent years, certain luminous extragalactic optical transients have been observed to last only a few days. Their short observed duration implies a different powering mechanism from the most common luminous extragalactic transients (supernovae) whose timescale is weeks. Some short-duration transients, most notably AT2018cow, display blue optical colours and bright radio and X-ray emission. Several AT2018cow-like transients have shown hints of a long-lived embedded energy source, such as X-ray variability, prolonged ultraviolet emission, a tentative X-ray quasiperiodic oscillation, and large energies coupled to fast (but subrelativistic) radio-emitting ejecta. Here we report observations of minutes-duration optical flares in the aftermath of an AT2018cow-like transient, AT2022tsd (the "Tasmanian Devil"). The flares occur over a period of months, are highly energetic, and are likely nonthermal, implying that they arise from a near-relativistic outflow or jet. Our observations confirm that in some AT2018cow-like transients the embedded energy source is a compact object, either a magnetar or an accreting black hole., Comment: 79 pages, 3 figures (main text) + 7 figures (extended data) + 2 figures (supplementary information). Published online in Nature on 15 November 2023

Recognition and reasoning are two pillars of visual understanding. However, these tasks have an imbalance in focus; whereas recent advances in neural networks have shown strong empirical performance in visual recognition, there has been comparably much less success in solving visual reasoning. Intuitively, unifying these two tasks under a singular framework is desirable, as they are mutually dependent and beneficial. Motivated by the recent success of multi-task transformers for visual recognition and language understanding, we propose a unified neural architecture for visual recognition and reasoning with a generic interface (e.g., tokens) for both. Our framework enables the principled investigation of how different visual recognition tasks, datasets, and inductive biases can help enable spatiotemporal reasoning capabilities. Noticeably, we find that object detection, which requires spatial localization of individual objects, is the most beneficial recognition task for reasoning. We further demonstrate via probing that implicit object-centric representations emerge automatically inside our framework. Intriguingly, we discover that certain architectural choices such as the backbone model of the visual encoder have a significant impact on visual reasoning, but little on object detection. Given the results of our experiments, we believe that visual reasoning should be considered as a first-class citizen alongside visual recognition, as they are strongly correlated but benefit from potentially different design choices.

The carbon capture, utilization, and storage (CCUS) framework is an essential component in reducing greenhouse gas emissions, with its success hinging on the comprehensive knowledge of subsurface geology and geomechanics. Passive seismic event relocation and fault detection serve as indispensable tools, offering vital insights into subsurface structures and fluid migration pathways. Accurate identification and localization of seismic events, however, face significant challenges, including the necessity for high-quality seismic data and advanced computational methods. To address these challenges, we introduce a novel deep learning method, DeFault, specifically designed for passive seismic source relocation and fault delineating for passive seismic monitoring projects. By leveraging data domain-adaptation, DeFault allows us to train a neural network with labeled synthetic data and apply it directly to field data. Using DeFault, the passive seismic sources are automatically clustered based on their recording time and spatial locations, and subsequently, faults and fractures are delineated accordingly. We demonstrate the efficacy of DeFault on a field case study involving CO2 injection related microseismic data from the Decatur, Illinois area. Our approach accurately and efficiently relocated passive seismic events, identified faults and aided in the prevention of potential geological hazards. Our results highlight the potential of DeFault as a valuable tool for passive seismic monitoring, emphasizing its role in ensuring CCUS project safety. This research bolsters the understanding of subsurface characterization in CCUS, illustrating machine learning's capacity to refine these methods. Ultimately, our work bear significant implications for CCUS technology deployment, an essential strategy in combating climate change.