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Parton And-hadron China Institute of Atomic Energy (PACIAE) is a multipurpose Monte Carlo event generator developed to describe a wide range of high-energy collisions, including lepton-lepton, lepton-hadron, lepton-nucleus, hadron-hadron, hadron-nucleus, and nucleus-nucleus collisions. It is built based on the PYTHIA program, and incorporates parton and hadron cascades to address the nuclear medium effects. PACIAE 4.0 is the new generation of PACIAE model surpassing the version 3.0. In PACIAE 4.0, the old fixed-format FORTRAN 77 code has been refactored and rewritten by the free-format modern Fortran and C++ languages. The C++-based PYTHIA 8.3 is interfaced in, while previous versions connected to the Fortran-based PYTHIA 6.4 only. Several improvements are also introduced, which enable PACIAE 4.0 to contain more physics and features to model the high-energy collisions. This is the first attempt to transition PACIAE from Fortran to C++.

To safeguard sensitive user data, web developers typically rely on an implicit access-control policies, which they implement using access checks and query filters. This ad-hoc approach is error-prone, as these scattered checks and filters are easy to misplace or misspecify; and the lack of an explicit policy precludes external access-control enforcement. More critically, it is difficult to divine what policy is embedded in application code and what data the application may access -- an issue that worsens as development teams evolve. This paper tackles policy extraction: the task of extracting the access-control policy embedded in an application by summarizing its data queries. An extracted policy, once vetted for errors, can stand alone as a specification for the application's data access, and can be enforced to ensure compliance as code changes over time. We introduce Ote, a policy extractor for Ruby-on-Rails web applications. Ote uses concolic execution to explore execution paths through the application, generating traces of SQL queries and conditions that trigger them. It then merges and simplifies these traces into a final policy that aligns with the observed behaviors. We applied Ote to three real-world applications and compare extracted policies to handwritten ones, revealing several errors in the latter.

We present a minimalistic representation model for the head direction (HD) system, aiming to learn a high-dimensional representation of head direction that captures essential properties of HD cells. Our model is a representation of rotation group $U(1)$, and we study both the fully connected version and convolutional version. We demonstrate the emergence of Gaussian-like tuning profiles and a 2D circle geometry in both versions of the model. We also demonstrate that the learned model is capable of accurate path integration., Comment: Workshop on Symmetry and Geometry in Neural Representations (NeurReps) at NeurIPS 2024, Extended Abstract Track

Query determinacy is decidable for project-select views and a project-select-join query with no self joins, as long as the selection predicates are in a first-order theory for which satisfiability is decidable.

Beyond-triple fact representations including hyper-relational facts with auxiliary key-value pairs, temporal facts with additional timestamps, and nested facts implying relationships between facts, are gaining significant attention. However, existing link prediction models are usually designed for one specific type of facts, making it difficult to generalize to other fact representations. To overcome this limitation, we propose a Unified Hierarchical Representation learning framework (UniHR) for unified knowledge graph link prediction. It consists of a unified Hierarchical Data Representation (HiDR) module and a unified Hierarchical Structure Learning (HiSL) module as graph encoder. The HiDR module unifies hyper-relational KGs, temporal KGs, and nested factual KGs into triple-based representations. Then HiSL incorporates intra-fact and inter-fact message passing, focusing on enhancing the semantic information within individual facts and enriching the structural information between facts. Experimental results across 7 datasets from 3 types of KGs demonstrate that our UniHR outperforms baselines designed for one specific kind of KG, indicating strong generalization capability of HiDR form and the effectiveness of HiSL module. Code and data are available at https://github.com/Lza12a/UniHR.

This paper studies a distributed estimation problem for time-varying/time-invariant large-scale interconnected systems (LISs). A fully distributed estimator is presented by recursively solving a distributed modified Riccati equation (DMRE) with decoupling variables. By partitioning the LIS based on the transition matrix's block structure, it turns out that the stability of the subsystem is independent of the global LIS if the decoupling variable is selected as the number of out-neighbors. Additionally, it is revealed that any LIS can be equivalently represented by a Markov system. Based on this insight, we show that the stability decoupling above can also be achieved if the decoupling variable equals the number of in-neighbors. Then, the distributed estimator is proved to be stable if the DMRE remains uniformly bounded. When the LIS is considered time-invariant, and by analyzing the spectral radius of a linear operator, it is proved that the DMRE is uniformly bounded if and only if a linear matrix inequality is feasible. Based on the boundedness result, we also show that the distributed estimator converges to a unique steady state for any initial condition. Finally, simulations verify the effectiveness of the proposed methods., Comment: 15 pages, 4 figures

Drug-target interaction (DTI) prediction is crucial for drug development and repositioning. Methods using heterogeneous graph neural networks (HGNNs) for DTI prediction have become a promising approach, with attention-based models often achieving excellent performance. However, these methods typically overlook edge features when dealing with heterogeneous biomedical networks. We propose a heterogeneous network-based contrastive learning method called HNCL-DTI, which designs a heterogeneous graph attention network to predict potential/novel DTIs. Specifically, our HNCL-DTI utilizes contrastive learning to collaboratively learn node representations from the perspective of both node-based and edge-based attention within the heterogeneous structure of biomedical networks. Experimental results show that HNCL-DTI outperforms existing advanced baseline methods on benchmark datasets, demonstrating strong predictive ability and practical effectiveness. The data and source code are available at https://github.com/Zaiwen/HNCL-DTI.

Large language models (LLMs) have significantly advanced performance across a spectrum of natural language processing (NLP) tasks. Yet, their application to knowledge graphs (KGs), which describe facts in the form of triplets and allow minimal hallucinations, remains an underexplored frontier. In this paper, we investigate the integration of LLMs with KGs by introducing a specialized KG Language (KGL), where a sentence precisely consists of an entity noun, a relation verb, and ends with another entity noun. Despite KGL's unfamiliar vocabulary to the LLM, we facilitate its learning through a tailored dictionary and illustrative sentences, and enhance context understanding via real-time KG context retrieval and KGL token embedding augmentation. Our results reveal that LLMs can achieve fluency in KGL, drastically reducing errors compared to conventional KG embedding methods on KG completion. Furthermore, our enhanced LLM shows exceptional competence in generating accurate three-word sentences from an initial entity and interpreting new unseen terms out of KGs., Comment: NeurIPS 2024 (spotlight)

The recently developed retrieval-augmented generation (RAG) technology has enabled the efficient construction of domain-specific applications. However, it also has limitations, including the gap between vector similarity and the relevance of knowledge reasoning, as well as insensitivity to knowledge logic, such as numerical values, temporal relations, expert rules, and others, which hinder the effectiveness of professional knowledge services. In this work, we introduce a professional domain knowledge service framework called Knowledge Augmented Generation (KAG). KAG is designed to address the aforementioned challenges with the motivation of making full use of the advantages of knowledge graph(KG) and vector retrieval, and to improve generation and reasoning performance by bidirectionally enhancing large language models (LLMs) and KGs through five key aspects: (1) LLM-friendly knowledge representation, (2) mutual-indexing between knowledge graphs and original chunks, (3) logical-form-guided hybrid reasoning engine, (4) knowledge alignment with semantic reasoning, and (5) model capability enhancement for KAG. We compared KAG with existing RAG methods in multihop question answering and found that it significantly outperforms state-of-theart methods, achieving a relative improvement of 19.6% on 2wiki and 33.5% on hotpotQA in terms of F1 score. We have successfully applied KAG to two professional knowledge Q&A tasks of Ant Group, including E-Government Q&A and E-Health Q&A, achieving significant improvement in professionalism compared to RAG methods., Comment: 33 pages

The Rapid and accurate identification of Gamma-Ray Bursts (GRBs) is crucial for unraveling their origins. However, current burst search algorithms frequently miss low-threshold signals or lack universality for observations. In this study, we propose a novel approach utilizing transfer learning experiment based on convolutional neural network (CNN) to establish a universal GRB identification method, which validated successfully using GECAM-B data. By employing data augmentation techniques, we enhance the diversity and quantity of the GRB sample. We develop a 1D CNN model with a multi-scale feature cross fusion module (MSCFM) to extract features from samples and perform classification. The comparative results demonstrated significant performance improvements following pre-training and transferring on a large-scale dataset. Our optimal model achieved an impressive accuracy of 96.41% on the source dataset of GECAM-B, and identified three previously undiscovered GRBs by contrast with manual analysis of GECAM-B observations. These innovative transfer learning and data augmentation methods presented in this work hold promise for applications in multi-satellite exploration scenarios characterized by limited data sets and a scarcity of labeled samples in high-energy astronomy., Comment: 17 pages, 7 figures

A non-extensive (3+1)-dimensional hydrodynamic model for multi-particle production processes, NEX-CLVisc, is developed in the framework of CLVisc where the viscous corrections are turned off. It assumes that the non-extensive effects consistently exist in the initial conditions set by the optical Glauber model, the equation of state and the hadron kinetic freeze-out procedure. The model is then applied to simulate the pseudo-rapidity ($\eta$) distribution, the transverse momentum ($p_{\rm T}$) spectra and the $p_{\rm T}$-differential elliptic flow ($v_2$) of charged particles in Pb-Pb collisions at $\sqrt{s_{\rm NN}}=$ 2.76 TeV and 5.02 TeV, respectively. It is found that the model can reasonably well reproduce the experimental data of the $\eta$ distribution and the charged-particle spectra in a $p_{\rm T}$ range up to 6-8 GeV/c. When compared with the ideal hydrodynamic model, the $p_{\rm T}$-differential $v_2$ of charged particles is suppressed in the NEX-CLVisc model, which is similar to that observed in the hydrodynamic model with a shear viscous correction. Moreover, due to the lack of the viscous corrections and the event-by-event fluctuation, the model can only describe the $p_{\rm T}$-differential $v_2$ up to 3-4 GeV/c, which is smaller than its applicable range for the particle $p_{\rm T}$ spectra., Comment: 9 pages, 4 figures, 4 tables. Revised accroding to the referee's comments

This paper compared physics-informed neural network (PINN), conventional neural network (NN) and traditional numerical discretization methods on solving differential equations (DEs) through literature investigation and experimental validation. We focused on the soft-constrained PINN approach and formalized its mathematical framework and computational flow for solving Ordinary DEs and Partial DEs (ODEs/PDEs). The working mechanism and its accuracy and efficiency were experimentally verified by solving typical linear and non-linear oscillator ODEs. We demonstrate that the DeepXDE-based implementation of PINN is not only light code and efficient in training, but also flexible across CPU/GPU platforms. PINN greatly reduces the need for labeled data: when the nonlinearity of the ODE is weak, a very small amount of supervised training data plus a few unsupervised collocation points are sufficient to predict the solution; in the minimalist case, only one or two training points (with initial values) are needed for first- or second-order ODEs, respectively. We also find that, with the aid of collocation points and the use of physical information, PINN has the ability to extrapolate data outside the time domain of the training set, and especially is robust to noisy data, thus with enhanced generalization capabilities. Training is accelerated when the gains obtained along with the reduction in the amount of data outweigh the delay caused by the increase in the loss function terms. The soft-constrained PINN can easily impose a physical law (e.g., conservation of energy) constraint by adding a regularization term to the total loss function, thus improving the solution performance to ODEs that obey this physical law. Furthermore, PINN can also be used for stiff ODEs, PDEs, and other types of DEs, and is becoming a favorable catalyst for the era of Digital Twins., Comment: 24 pages, 7 figures, 2 tables, etc. Ready for submission

Recent breakthroughs have ushered the quantum network into a new era, where quantum information can be stored, transferred, and processed across multiple nodes on a metropolitan scale. A key challenge in this new era is enhancing the capabilities of individual nodes, providing precise and robust control over multiple qubits and advanced functionality for scalable quantum networks. Here, we report on precise and complex control in a hybrid quantum node based on a diamond color center. We demonstrate hybrid coherent control by entangling three types of qubits: an electron spin as an interface qubit, a nuclear spin with long memory time, and a flying photonic qubit, with their qubit frequencies spanning three distinct regimes from the optical domain to the rf domain. By incorporating two additional memory qubits, we encode three memory qubits into a logical state using the three-qubit repetition code and entangle this logical qubit with a photonic qubit. Leveraging hybrid qubits and precise control, we repeatedly read out the error syndromes of memory qubits through the electron spin, serving as an auxiliary qubit, then apply a real-time feedback operation to correct bit-flip errors. We execute and verify active error correction for up to twelve rounds and demonstrate the improvement over the uncorrected counterpart. Our results demonstrate the feasibility of several key functionalities for next-generation quantum repeaters, paving the way towards full-fledged metropolitan-scale quantum networks for a wide range of practical applications., Comment: 8 pages, 3 figures

A physics-informed neural network (PINN) is used to produce a variety of self-trapped necklace solutions of the (2+1)-dimensional nonlinear Schr\"{o}dinger/Gross-Pitaevskii equation. We elaborate the analysis for the existence and evolution of necklace patterns with integer, half-integer, and fractional reduced orbital angular momenta by means of PINN. The patterns exhibit phenomena similar to rotation of rigid bodies and centrifugal force. Even though the necklaces slowly expand (or shrink), they preserve their structure in the course of the quasi-stable propagation over several diffraction lengths, which is completely different from the ordinary fast diffraction-dominated dynamics. By comparing different ingredients, including the training time, loss value and $\mathbb{L}_{2}$ error, PINN accurately predicts specific nonlinear dynamical properties of the evolving necklace patterns. Furthermore, we perform the data-driven discovery of parameters for both clean and perturbed training data, adding $1\%$ random noise in the latter case. The results reveal that PINN not only effectively emulates the solution of partial differential equations, but also offers applications for predicting the nonlinear dynamics of physically relevant types of patterns., Comment: 19 pages, 9 figures, to be published in Optics Express

Inspired by the BESIII newest observation of X(2370) glueball-like particle, we search its productions in both $e^+e^-$ collisions at $\sqrt{s}=$ 4.95 GeV and proton-proton (pp) collisions at $\sqrt{s}=$ 13 TeV with a parton and hadron cascade model PACIAE. In this model, the final partonic state (FPS) and the final hadronic state (FHS) are consecutively simulated and recorded. The X(2370) glueball- or tetraquark-state is then, respectively, recombined by two gluons or four quarks $ss\bar{s}\bar{s}$ in the FPS using the quantum statistical mechanics inspired dynamically constrained phase-space coalescence (DCPC) model. The X(2370) molecular-state is recombined by the baryon-antibaryon of $\Lambda$-$\bar{\Lambda}$ or $\Sigma$-$\bar{\Sigma}$, or by three mesons of $\pi^+\pi^{-}\eta'$, $K^+K^-\eta'$, or $K_S^0K_S^0\eta'$ in the FHS using DCPC model. In both $e^+e^-$ and pp collisions, significant discrepancies in the yields, the transverse momentum spectra and the rapidity distributions among the X(2370) glueball-, tetraquark-, and molecular-state are observed. These discrepancies are proposed as valuable criteria identifying the X(2370) different states from each other. Our results not only support the BESIII observation of glueball-like particle $\rm X(2370)$ production in $e^+e^-$ collisions, but also serve as a prediction for the $\rm X(2370)$ production in pp collisions. We strongly suggest the experimental measurement of the X(2370) glueball-like particle production in pp collisions at the LHC energies., Comment: 6 pages, 5 figures

Nanometric solid solution alloys are utilized in a broad range of fields, including catalysis, energy storage, medical application, and sensor technology. Unfortunately, the synthesis of these alloys becomes increasingly challenging as the disparity between the metal elements grows, due to differences in atomic sizes, melting points, and chemical affinities. This study utilized a data-driven approach incorporating sample balancing enhancement techniques and multilayer perceptron (MLP) algorithms to improve the model's ability to handle imbalanced data, significantly boosting the efficiency of experimental parameter optimization. Building on this enhanced data processing framework, we developed an entropy-engineered synthesis approach specifically designed to produce stable, nanometric copper and cobalt (CuCo) solid solution alloys. Under conditions of -0.425 V (vs. RHE), the CuCo alloy exhibited nearly 100% Faraday efficiency (FE) and a high ammonia production rate of 232.17 mg h-1 mg-1. Stability tests in a simulated industrial environment showed that the catalyst maintained over 80% FE and an ammonia production rate exceeding 170 mg h-1 mg-1 over a testing period of 120 hours, outperforming most reported catalysts. To delve deeper into the synergistic interaction mechanisms between Cu and Co, in situ Raman spectroscopy was utilized for realtime monitoring, and density functional theory (DFT) calculations further substantiated our findings. These results not only highlight the exceptional catalytic performance of the CuCo alloy but also reflect the effective electronic and energy interactions between the two metals., Comment: We found some mistakes and revisions are needed. It will take a long time

Superconducting qubits, a promising candidate for universal quantum computing, currently face limitations in chip size due to reproducibility, wiring complexity, and packaging modes. Distributed quantum modules offer a viable strategy for constructing larger quantum information processing systems, though universal quantum gate operations between remote qubits have yet to be realized. Here, we demonstrate high-fidelity perfect entanglers between two remote superconducting quantum devices over 30 cm distance, leveraging the standing-wave modes in the coaxial cable connecting them. We achieve cross-entropy benchmarking (XEB) fidelities of $(99.15 \pm 0.02)\%$ and $(98.04 \pm 0.04)\%$ for CNOT and CZ gates, respectively, which are more efficient and universal than existing state transfer or feedback-based protocols. This advancement significantly enhances the feasibility of universal distributed quantum information processing, essential for the future development of large-scale quantum systems.

Two-dimensional materials with Janus structure host novel physical properties due to their inversional symmetry breaking. However, it remains elusive to synthesize Janus monolayer crystals with tailored long-range magnetic orders. Here, we have developed a general method to fabricate uniform Janus CrTeSe monolayers by selective selenization of preformed CrTe2 monolayers with molecular beam epitaxy. The uniform Janus structure of CrTeSe with high crystal quality is confirmed by high-resolution scanning transmission electron microscopy. Spin-polarized scanning tunneling microscopy/spectroscopy measurements unveil that the Janus CrTeSe undergoes a charge density wave (CDW) transition and a robust antiferromagnetic order. The magnetic anisotropy of CrTeSe is drastically altered compared to monolayer CrTe2 by the breaking symmetries induced from the Janus structure and the CDW transition, as is substantiated with first principles calculations. Our research achieves the construction of large-area Janus structures, and artificially tailors the electronic and magnetic properties of Janus systems at the two-dimensional limit., Comment: 19 pages, 4 figures

Deconfined quantum critical points (DQCPs) are proposed as unconventional second-order phase transitions beyond the Landau-Ginzburg-Wilson paradigm. The nature and experimental realizations of DQCPs are crucial issues of importance. We illustrate the potential for DQCPs between the valence bond solid state and the antiferromagnetic phase to arise in optical lattices containing frustrated dipolar bosons subject to hard-core constraints. The emergence of DQCPs is comprehended through the fusion of two Berezinskii-Kosterlitz-Thouless (BKT) transitions. The DQCPs and the BKTs are confirmed by the finite-size scaling of ground-state fidelity susceptibilities. The numerical analysis reveals varying critical exponents of the correlation length in DQCPs and the logarithmic scaling in BKTs, respectively. This work offers a promising platform for realizing DQCPs and provides valuable insights into their nature within the framework of topological phase transitions., Comment: 9 pages, 8 figures

We propose a neural network for both forward and inverse continuous nonlinear Fourier transforms, NFT and INFT respectively. We demonstrate the network's capability to perform NFT and INFT for a random mix of NFDM-QAM signals. The network transformations (NFT and INFT) exhibit true characteristics of these transformations; they are significantly different for low and high-power input pulses. The network shows adequate accuracy with an RMSE of 5e-3 for forward and 3e-2 for inverse transforms. We further show that the trained network can be used to perform general nonlinear Fourier transforms on arbitrary pulses beyond the training pulse types.

We use bisection to provide an algebraic proof that the Chern form on the convolution algebra of an \'etale groupoid is closed., Comment: 23 pages

The parton and hadron cascade model {\footnotesize PACIAE} is employed to confirm the BESIII newest observation of glueball-like particle $\rm X(2370)$ production in $e^+e^-$ collisions at $\sqrt{s}=4.95\,\mathrm{GeV}$. We coalesce the $\rm X(2370)$ glueball state with two gluons in the simulated partonic final state by the Dynamically Constrained Phase-space Coalescence ({\footnotesize DCPC}) model. Alternative configuration of $\rm X(2370)$ molecular state is recombined in the simulated hadronic final state with $\pi^{+},\pi^{-},K^{+}, K^{-},K_{S}^{0},K_{S}^{0}$ and $\eta'$ by {\footnotesize DCPC} model. The resulted particle transverse momentum spectrum and rapidity distribution, etc. show a significant discrepancy between the two states. They are not only serving as criteria to distinguish the $\rm X(2370)$ glueball state or molecular state, but also confirming the BESIII observation of glueball-like particle $\rm X(2370)$ productions in $e^+e^-$ collisions., Comment: 4 pages, 2 figures

Type I gamma-ray bursts (GRBs) are believed to originate from compact binary merger usually with duration less than 2 seconds for the main emission. However, recent observations of GRB 211211A and GRB 230307A indicate that some merger-origin GRBs could last much longer. Since they show strikingly similar properties (indicating a common mechanism) which are different from the classic "long"-short burst (e.g. GRB 060614), forming an interesting subclass of type I GRBs, we suggest to name them as type IL GRBs. By identifying the first peak of GRB 230307A as a quasi-thermal precursor, we find that the prompt emission of type IL GRB is composed of three episodes: (1) a precursor followed by a short quiescent (or weak emission) period, (2) a long-duration main emission, and (3) an extended emission. With this burst pattern, a good candidate, GRB 170228A, was found in the Fermi/GBM archive data, and subsequent temporal and spectral analyses indeed show that GRB 170228A falls in the same cluster with GRB 211211A and GRB 230307A in many diagnostic figures. Thus this burst pattern could be a good reference for rapidly identifying type IL GRB and conducting low-latency follow-up observation. We estimated the occurrence rate and discussed the physical origins and implications for the three emission episodes of type IL GRBs. Our analysis suggests the pre-merger precursor model, especially the super flare model, is more favored for type IL GRBs.

Knowledge Graph Embedding (KGE) is a common method for Knowledge Graphs (KGs) to serve various artificial intelligence tasks. The suitable dimensions of the embeddings depend on the storage and computing conditions of the specific application scenarios. Once a new dimension is required, a new KGE model needs to be trained from scratch, which greatly increases the training cost and limits the efficiency and flexibility of KGE in serving various scenarios. In this work, we propose a novel KGE training framework MED, through which we could train once to get a croppable KGE model applicable to multiple scenarios with different dimensional requirements, sub-models of the required dimensions can be cropped out of it and used directly without any additional training. In MED, we propose a mutual learning mechanism to improve the low-dimensional sub-models performance and make the high-dimensional sub-models retain the capacity that low-dimensional sub-models have, an evolutionary improvement mechanism to promote the high-dimensional sub-models to master the knowledge that the low-dimensional sub-models can not learn, and a dynamic loss weight to balance the multiple losses adaptively. Experiments on 3 KGE models over 4 standard KG completion datasets, 3 real application scenarios over a real-world large-scale KG, and the experiments of extending MED to the language model BERT show the effectiveness, high efficiency, and flexible extensibility of MED.

Graph Neural Network (GNN), with the main idea of encoding graph structure information of graphs by propagation and aggregation, has developed rapidly. It achieved excellent performance in representation learning of multiple types of graphs such as homogeneous graphs, heterogeneous graphs, and more complex graphs like knowledge graphs. However, merely stacking GNN layers may not improve the model's performance and can even be detrimental. For the phenomenon of performance degradation in deep GNNs, we propose a new perspective. Unlike the popular explanations of over-smoothing or over-squashing, we think the issue arises from the interference of low-quality node representations during message propagation. We introduce a simple and general method, SF-GNN, to address this problem. In SF-GNN, we define two representations for each node, one is the node representation that represents the feature of the node itself, and the other is the message representation specifically for propagating messages to neighbor nodes. A self-filter module evaluates the quality of the node representation and decides whether to integrate it into the message propagation based on this quality assessment. Experiments on node classification tasks for both homogeneous and heterogeneous graphs, as well as link prediction tasks on knowledge graphs, demonstrate that our method can be applied to various GNN models and outperforms state-of-the-art baseline methods in addressing deep GNN degradation.

Drug-drug interactions (DDIs) can result in various pharmacological changes, which can be categorized into different classes known as DDI events (DDIEs). In recent years, previously unobserved/unseen DDIEs have been emerging, posing a new classification task when unseen classes have no labelled instances in the training stage, which is formulated as a zero-shot DDIE prediction (ZS-DDIE) task. However, existing computational methods are not directly applicable to ZS-DDIE, which has two primary challenges: obtaining suitable DDIE representations and handling the class imbalance issue. To overcome these challenges, we propose a novel method named ZeroDDI for the ZS-DDIE task. Specifically, we design a biological semantic enhanced DDIE representation learning module, which emphasizes the key biological semantics and distills discriminative molecular substructure-related semantics for DDIE representation learning. Furthermore, we propose a dual-modal uniform alignment strategy to distribute drug pair representations and DDIE semantic representations uniformly in a unit sphere and align the matched ones, which can mitigate the issue of class imbalance. Extensive experiments showed that ZeroDDI surpasses the baselines and indicate that it is a promising tool for detecting unseen DDIEs. Our code has been released in https://github.com/wzy-Sarah/ZeroDDI., Comment: Accepted by IJCAI2024

The recent focus on microbes in human medicine highlights their potential role in the genetic framework of diseases. To decode the complex interactions among genes, microbes, and diseases, computational predictions of gene-microbe-disease (GMD) associations are crucial. Existing methods primarily address gene-disease and microbe-disease associations, but the more intricate triple-wise GMD associations remain less explored. In this paper, we propose a Heterogeneous Causal Metapath Graph Neural Network (HCMGNN) to predict GMD associations. HCMGNN constructs a heterogeneous graph linking genes, microbes, and diseases through their pairwise associations, and utilizes six predefined causal metapaths to extract directed causal subgraphs, which facilitate the multi-view analysis of causal relations among three entity types. Within each subgraph, we employ a causal semantic sharing message passing network for node representation learning, coupled with an attentive fusion method to integrate these representations for predicting GMD associations. Our extensive experiments show that HCMGNN effectively predicts GMD associations and addresses association sparsity issue by enhancing the graph's semantics and structure.

Natural language question answering (QA) over structured data sources such as tables and knowledge graphs (KGs) have been widely investigated, for example with Large Language Models (LLMs). The main solutions include question to formal query parsing and retrieval-based answer generation. However, current methods of the former often suffer from weak generalization, failing to dealing with multiple sources simultaneously, while the later is limited in trustfulness. In this paper, we propose UnifiedTQA, a trustful QA framework that can simultaneously support multiple types of structured data in a unified way. To this end, it adopts an LLM-friendly and unified knowledge representation method called Condition Graph (CG), and uses an LLM and demonstration-based two-level method for CG querying. For enhancement, it is also equipped with dynamic demonstration retrieval. We have evaluated UnifiedTQA with 5 benchmarks covering 3 types of structured data. It outperforms 2 existing unified structured data QA methods and in comparison with the baselines that are specific to a data type, it achieves state-of-the-art on 2 of them. Further more, we demonstrates potential of our method for more general QA tasks, QA over mixed structured data and QA across structured data.

Knowledge graph (KG) completion aims to find out missing triples in a KG. Some tasks, such as link prediction and instance completion, have been proposed for KG completion. They are triple-level tasks with some elements in a missing triple given to predict the missing element of the triple. However, knowing some elements of the missing triple in advance is not always a realistic setting. In this paper, we propose a novel graph-level automatic KG completion task called Triple Set Prediction (TSP) which assumes none of the elements in the missing triples is given. TSP is to predict a set of missing triples given a set of known triples. To properly and accurately evaluate this new task, we propose 4 evaluation metrics including 3 classification metrics and 1 ranking metric, considering both the partial-open-world and the closed-world assumptions. Furthermore, to tackle the huge candidate triples for prediction, we propose a novel and efficient subgraph-based method GPHT that can predict the triple set fast. To fairly compare the TSP results, we also propose two types of methods RuleTensor-TSP and KGE-TSP applying the existing rule- and embedding-based methods for TSP as baselines. During experiments, we evaluate the proposed methods on two datasets extracted from Wikidata following the relation-similarity partial-open-world assumption proposed by us, and also create a complete family data set to evaluate TSP results following the closed-world assumption. Results prove that the methods can successfully generate a set of missing triples and achieve reasonable scores on the new task, and GPHT performs better than the baselines with significantly shorter prediction time. The datasets and code for experiments are available at https://github.com/zjukg/GPHT-for-TSP., Comment: Paper accepted by TKDE in 2024

Improving the performance of large language models (LLMs) in complex question-answering (QA) scenarios has always been a research focal point. Recent studies have attempted to enhance LLMs' performance by combining step-wise planning with external retrieval. While effective for advanced models like GPT-3.5, smaller LLMs face challenges in decomposing complex questions, necessitating supervised fine-tuning. Previous work has relied on manual annotation and knowledge distillation from teacher LLMs, which are time-consuming and not accurate enough. In this paper, we introduce a novel framework for enhancing LLMs' planning capabilities by using planning data derived from knowledge graphs (KGs). LLMs fine-tuned with this data have improved planning capabilities, better equipping them to handle complex QA tasks that involve retrieval. Evaluations on multiple datasets, including our newly proposed benchmark, highlight the effectiveness of our framework and the benefits of KG-derived planning data., Comment: EMNLP2024 Findings

Entity matching (EM), the task of identifying whether two descriptions refer to the same entity, is essential in data management. Traditional methods have evolved from rule-based to AI-driven approaches, yet current techniques using large language models (LLMs) often fall short due to their reliance on static knowledge and rigid, predefined prompts. In this paper, we introduce Libem, a compound AI system designed to address these limitations by incorporating a flexible, tool-oriented approach. Libem supports entity matching through dynamic tool use, self-refinement, and optimization, allowing it to adapt and refine its process based on the dataset and performance metrics. Unlike traditional solo-AI EM systems, which often suffer from a lack of modularity that hinders iterative design improvements and system optimization, Libem offers a composable and reusable toolchain. This approach aims to contribute to ongoing discussions and developments in AI-driven data management., Comment: 2 pages, compound ai systems 2024

We propose a theoretical scheme to enhance the sensitivity of a quantum fiber-optical gyroscope (QFOG) via a non-Gaussian-state probe based on quadrature measurements of the optical field. The non-Gaussian-state probe utilizes the product state comprising a photon-added coherent state (PACS) with photon excitations and a coherent state CS. We study the sensitivity of the QFOG, and find that it can be significantly enhanced through increasing the photon excitations in the PACS probe. We investigate the influence of photon loss on the performance of QFOG and demonstrate that the PACS probe exhibits robust resistance to photon loss. Furthermore, we compare the performance of the QFOG using the PACS probe against two Gaussian-state probes: the CS probe and the squeezed state (SS) probe and indicate that the PACS probe offers a significant advantage in terms of sensitivity, regardless of photon loss, under the constraint condition of the same total number of input photons. Particularly, it is found that the sensitivity of the PACS probe can be three orders of magnitude higher than that of two Gaussian-state probes for certain values of the measured parameter. The capabilities of the non-Gaussian state probe on enhancing the sensitivity and resisting photon loss could have a wide-ranging impact on future high-performance QFOGs., Comment: 10 pages, 8 figures

This study investigates the excesses observed in the diphoton and $b\bar b$ data around $95\;{\rm GeV}$ within the framework of the $B-L$ supersymmetric model (B-LSSM). Comparing with the minimal supersymmetric standard model, the B-LSSM incorporates two singlet chiral Higgs bosons which mix with the SM-like Higgs boson due to the gauge kinetic mixing effect. The richer Higgs sector indicates that designating the B-LSSM specific CP-even Higgs state as the lightest Higgs boson has great potential to explain the excesses at around $95\;{\rm GeV}$. Considering the two-loop effective potential corrections to the squared Higgs mass matrix, it is found that the signal strengthes $\mu(h_{95})_{\gamma\gamma}$, $\mu(h_{95})_{b b}$ can be described simultaneously in the experimental $1\sigma$ interval. And the B-LSSM specific parameters $\tan\beta'$, $B_\eta$, $g_{YB}$, $M_{Z'}$ affect the theoretical predictions on the light Higgs boson masses and signal strengthes $\mu(h_{95})_{\gamma\gamma}$, $\mu(h_{95})_{bb}$ significantly., Comment: 28 pages, 5 figures. arXiv admin note: text overlap with arXiv:2405.07243

Accurately predicting antibody-antigen binding residues, i.e., paratopes and epitopes, is crucial in antibody design. However, existing methods solely focus on uni-modal data (either sequence or structure), disregarding the complementary information present in multi-modal data, and most methods predict paratopes and epitopes separately, overlooking their specific spatial interactions. In this paper, we propose a novel Multi-modal contrastive learning and Interaction informativeness estimation-based method for Paratope and Epitope prediction, named MIPE, by using both sequence and structure data of antibodies and antigens. MIPE implements a multi-modal contrastive learning strategy, which maximizes representations of binding and non-binding residues within each modality and meanwhile aligns uni-modal representations towards effective modal representations. To exploit the spatial interaction information, MIPE also incorporates an interaction informativeness estimation that computes the estimated interaction matrices between antibodies and antigens, thereby approximating them to the actual ones. Extensive experiments demonstrate the superiority of our method compared to baselines. Additionally, the ablation studies and visualizations demonstrate the superiority of MIPE owing to the better representations acquired through multi-modal contrastive learning and the interaction patterns comprehended by the interaction informativeness estimation., Comment: This paper is accepted by IJCAI 2024

Learning high-quality multi-modal entity representations is an important goal of multi-modal knowledge graph (MMKG) representation learning, which can enhance reasoning tasks within the MMKGs, such as MMKG completion (MMKGC). The main challenge is to collaboratively model the structural information concealed in massive triples and the multi-modal features of the entities. Existing methods focus on crafting elegant entity-wise multi-modal fusion strategies, yet they overlook the utilization of multi-perspective features concealed within the modalities under diverse relational contexts. To address this issue, we introduce a novel framework with Mixture of Modality Knowledge experts (MoMoK for short) to learn adaptive multi-modal entity representations for better MMKGC. We design relation-guided modality knowledge experts to acquire relation-aware modality embeddings and integrate the predictions from multi-modalities to achieve joint decisions. Additionally, we disentangle the experts by minimizing their mutual information. Experiments on four public MMKG benchmarks demonstrate the outstanding performance of MoMoK under complex scenarios., Comment: Work in progress. Code and data will be released at https://github.com/zjukg/MoMoK

This paper investigates the conformal isometry hypothesis as a potential explanation for hexagonal periodic patterns in grid cell response maps. The hypothesis posits that grid cell activity forms a high-dimensional vector in neural space, encoding the agent's position in 2D physical space. As the agent moves, this vector rotates within a 2D manifold in the neural space, driven by a recurrent neural network. The conformal hypothesis suggests that this neural manifold is a conformally isometric embedding of physical space, where local displacements in neural space are proportional to those in physical space. In this paper, we conduct numerical experiments to show that this hypothesis leads to the hexagon periodic patterns of grid cells, agnostic to the choice of transformation models. Furthermore, we present a theoretical understanding that hexagon patterns emerge by minimizing our loss function because hexagon flat torus exhibits minimal deviation from local conformal isometry. In addition, we propose a conformal modulation of the agent's input velocity, enabling the recurrent neural network of grid cells to satisfy the conformal isometry hypothesis automatically., Comment: arXiv admin note: text overlap with arXiv:2310.19192

Knowledge graphs (KGs) provide reliable external knowledge for a wide variety of AI tasks in the form of structured triples. Knowledge graph pre-training (KGP) aims to pre-train neural networks on large-scale KGs and provide unified interfaces to enhance different downstream tasks, which is a key direction for KG management, maintenance, and applications. Existing works often focus on purely research questions in open domains, or they are not open source due to data security and privacy in real scenarios. Meanwhile, existing studies have not explored the training efficiency and transferability of KGP models in depth. To address these problems, We propose a framework MuDoK to achieve multi-domain collaborative pre-training and efficient prefix prompt tuning to serve diverse downstream tasks like recommendation and text understanding. Our design is a plug-and-play prompt learning approach that can be flexibly adapted to different downstream task backbones. In response to the lack of open-source benchmarks, we constructed a new multi-domain KGP benchmark called KPI with two large-scale KGs and six different sub-domain tasks to evaluate our method and open-sourced it for subsequent research. We evaluated our approach based on constructed KPI benchmarks using diverse backbone models in heterogeneous downstream tasks. The experimental results show that our framework brings significant performance gains, along with its generality, efficiency, and transferability., Comment: Work in progress. Code and data will be open-sourced at https://github.com/zjukg/MuDoK

In this study, we explore the quantum critical phenomena in generalized Aubry-Andr\'{e} models, with a particular focus on the scaling behavior at various filling states. Our approach involves using quantum fidelity susceptibility to precisely identify the mobility edges in these systems. Through a finite-size scaling analysis of the fidelity susceptibility, we are able to determine both the correlation-length critical exponent and the dynamical critical exponent at the critical point of the generalized Aubry-Andr\'{e} model. Based on the Diophantine equation conjecture, we can determines the number of subsequences of the Fibonacci sequence and the corresponding scaling functions for a specific filling fraction, as well as the universality class. Our findings demonstrate the effectiveness of employing the generalized fidelity susceptibility for the analysis of unconventional quantum criticality and the associated universal information of quasiperiodic systems in cutting-edge quantum simulation experiments., Comment: 9 pages, 5 figures

We investigate topological gauge-theory terms and quantum criticality in a spin-1 Kitaev chain with general single-ion anisotropies (SIAs). The ground-state phase diagram, including the Kitaev spin liquid (KSL) and gapless dimer phases, is determined by the infinite time evolving block decimation (iTEBD) method. A quantum phase transition between the KSL and dimer phases occurs by varying uniaxial SIA, analogous to the confinement-deconfinement transition in the lattice Schwinger model with a topological $\theta$ angle of $\pi$. Introducing rhombic SIA shifts this angle from $\pi$, resulting in $y$- and $x$-ferroquadrupole phases. The transition between these phases can occur through a crossover in the KSL phase or a genuine phase transition along a deconfined line. We map the spin-1 Hamiltonian to an effective spin-1/2 PXP Hamiltonian, with uniaxial SIA corresponding to uniform detuning and rhombic SIA to staggered detuning. We explore the hierarchical fragmentation of the Hilbert space, revealing that quantum many-body scars (QMBSs) emerge under weak uniform detuning, while slow dynamics under large staggered detuning is accurately captured by a second-order effective Hamiltonian via the Schrieffer-Wolff transformation. Our work establishes a framework for simulating topological $\theta$ angles and ergodicity-breaking dynamics, bridging higher-spin generalizations of scarred models with lattice gauge theories, potentially realizable using state-of-the-art cold-atom quantum simulators., Comment: 12 pages, 8 figures

Spectral graph convolutional network (SGCN) is a kind of graph neural networks (GNN) based on graph signal filters, and has shown compelling expressivity for modeling graph-structured data. Most SGCNs adopt polynomial filters and learn the coefficients from the training data. Many of them focus on which polynomial basis leads to optimal expressive power and models' architecture is little discussed. In this paper, we propose a general form in terms of spectral graph convolution, where the coefficients of polynomial basis are stored in a third-order tensor. Then, we show that the convolution block in existing SGCNs can be derived by performing a certain coefficient decomposition operation on the coefficient tensor. Based on the generalized view, we develop novel spectral graph convolutions CoDeSGC-CP and -Tucker by tensor decomposition CP and Tucker on the coefficient tensor. Extensive experimental results demonstrate that the proposed convolutions achieve favorable performance improvements.

By using the Fermi/Gamma-ray Burst Monitor data of the X-ray bursts (XRBs) of SGR J1935+2154, we investigate the temporal clustering of the bursts and the cumulative distribution of the waiting time and fluence/flux. It is found that the bursts occurring in the episode hosting FRB 20200428 have obviously shorter waiting times than those in the other episodes. The general statistical properties of the XRBs further indicate they could belong to a self-organized critical (SOC) system (e.g., starquakes), making them very similar to the earthquake phenomena. Then, according to a unified scaling law between the waiting time and energy of the earthquakes as well as their aftershocks, we implement an analogy analysis on the XRBs and find that the FRB episode owns more dependent burst events than the other episodes. It is indicated that the fast radio burst (FRB) emission could be produced by the interaction between different burst events, which could correspond to a collision between different seismic/Alfven waves or different explosion outflows. Such a situation could appear when the magnetar enters into a global intensive activity period.

Multi-modal knowledge graphs (MMKG) store structured world knowledge containing rich multi-modal descriptive information. To overcome their inherent incompleteness, multi-modal knowledge graph completion (MMKGC) aims to discover unobserved knowledge from given MMKGs, leveraging both structural information from the triples and multi-modal information of the entities. Existing MMKGC methods usually extract multi-modal features with pre-trained models and employ a fusion module to integrate multi-modal features with triple prediction. However, this often results in a coarse handling of multi-modal data, overlooking the nuanced, fine-grained semantic details and their interactions. To tackle this shortfall, we introduce a novel framework MyGO to process, fuse, and augment the fine-grained modality information from MMKGs. MyGO tokenizes multi-modal raw data as fine-grained discrete tokens and learns entity representations with a cross-modal entity encoder. To further augment the multi-modal representations, MyGO incorporates fine-grained contrastive learning to highlight the specificity of the entity representations. Experiments on standard MMKGC benchmarks reveal that our method surpasses 20 of the latest models, underlining its superior performance. Code and data are available at https://github.com/zjukg/MyGO, Comment: Working in progress; Repo is available at https://github.com/zjukg/MyGO

Many supervised learning tasks have intrinsic symmetries, such as translational and rotational symmetry in image classifications. These symmetries can be exploited to enhance performance. We formulate the symmetry constraints into a concise mathematical form. We design two ways to adopt the constraints into the cost function, thereby shaping the cost landscape in favour of parameter choices which respect the given symmetry. Unlike methods that alter the neural network circuit ansatz to impose symmetry, our method only changes the classical post-processing of gradient descent, which is simpler to implement. We call the method symmetry-guided gradient descent (SGGD). We illustrate SGGD in entanglement classification of Werner states and in a binary classification task in a 2-D feature space. In both cases, the results show that SGGD can accelerate the training, improve the generalization ability, and remove vanishing gradients, especially when the training data is biased., Comment: Phys. Rev. A 110, 022406. Published in 5 August 2024

Despite the immense potential of Dual Single-Atom Compounds (DSACs), the challenges in their synthesis process, including complexity, stability, purity, and scalability, remain primary concerns in current research. Here, we present a general strategy, termed "Entropy-Engineered Middle-In Synthesis of Dual Single-Atom Compounds" (EEMIS-DSAC), which is meticulously crafted to produce a diverse range of DSACs, effectively addressing the aforementioned issues. Our strategy integrates the advantages of both bottom-up and top-down paradigms, proposing a new insight to optimize the catalyst structure. The as-fabricated DSACs exhibited excellent activity and stability in the nitrate reduction reaction (NO3RR). In a significant advancement, our prototypical CuNi DSACs demonstrated outstanding performance under conditions reminiscent of industrial wastewater. Specifically, under a NO3- concentration of 2000 ppm, it yielded a Faradaic efficiency (FE) for NH3 of 96.97 %, coupled with a mass productivity of 131.47 mg h-1 mg-1 and an area productivity of 10.06 mg h-1 cm-2. Impressively, even under a heightened NO3- concentration of 0.5 M, the FE for NH3 peaked at 90.61 %, with mass productivity reaching 1024.50 mg h-1 mg-1 and an area productivity of 78.41 mg h-1 cm-2. This work underpins the potential of the EEMIS-DSAC approach, signaling a promising frontier for high-performing DSACs.

Gamma-ray bursts (GRBs) are believed to launch relativistic jets, which generate prompt emission by internal processes, and produce long-lasting afterglows by driving external shocks into surrounding medium. However, how the jet powers the external shock is poorly known. The unprecedented observations of the keV-MeV emission with GECAM and the TeV emission with LHAASO of the brightest-of-all-time GRB 221009A offer a great opportunity to study the prompt-to-afterglow transition and the impact of jet on the early dynamics of external shock. In this letter, we find that the cumulative light curve of keV-MeV emission could well fit the rising stage of the TeV light curve of GRB 221009A, with a time delay, $4.45^{+0.26}_{-0.26}$\,s, of TeV emission. Moreover, both the rapid increase in the initial stage and the excess from about \T+260\,s to 270\,s in the TeV light curve are tracking the light-curve bumps in the prompt keV-MeV emission. The close relation between the keV-MeV and TeV emission reveals the continuous energy-injection into the external shock. Assuming an energy-injection rate exactly following the keV-MeV flux of GRB 221009A, including the very early precursor, we build a continuous energy-injection model where the jet Lorentz factor is derived from the TeV time delay, and the TeV data is well fitted, with the TeV excesses interpreted by inverse Compton (IC) scatterings of the inner-coming prompt emission by the energetic electrons in external shock., Comment: ApJL accepted version; analytical derivation of SSC flux scaling with shock energy added

Multi-modal knowledge graph completion (MMKGC) aims to automatically discover the unobserved factual knowledge from a given multi-modal knowledge graph by collaboratively modeling the triple structure and multi-modal information from entities. However, real-world MMKGs present challenges due to their diverse and imbalanced nature, which means that the modality information can span various types (e.g., image, text, numeric, audio, video) but its distribution among entities is uneven, leading to missing modalities for certain entities. Existing works usually focus on common modalities like image and text while neglecting the imbalanced distribution phenomenon of modal information. To address these issues, we propose a comprehensive framework NativE to achieve MMKGC in the wild. NativE proposes a relation-guided dual adaptive fusion module that enables adaptive fusion for any modalities and employs a collaborative modality adversarial training framework to augment the imbalanced modality information. We construct a new benchmark called WildKGC with five datasets to evaluate our method. The empirical results compared with 21 recent baselines confirm the superiority of our method, consistently achieving state-of-the-art performance across different datasets and various scenarios while keeping efficient and generalizable. Our code and data are released at https://github.com/zjukg/NATIVE, Comment: Accepted by SIGIR 2024 as a full paper

In this work, we study masses and decays of triply-heavy pentaquarks $QQQn\bar{n}(Q=b,c)$ in the unified MIT bag model. We construct the color-spin wave functions of the triply-heavy pentaquarks we address and use numerical variational method to compute all ground-state masses of these system. By excluding the scattering states in these configurations, we compute the decay width ratios of each decay channels relative to the maximum width for the compact pentaquark states, obtaining main decay modes of the triply-heavy pentaquark systems., Comment: 12 pages, 10 tables, 3 figures and two appendices, to be published in Chinese Physics C

Knowledge graphs (KGs) have become vitally important in modern recommender systems, effectively improving performance and interpretability. Fundamentally, recommender systems aim to identify user interests based on historical interactions and recommend suitable items. However, existing works overlook two key challenges: (1) an interest corresponds to a potentially large set of related items, and (2) the lack of explicit, fine-grained exploitation of KG information and interest connectivity. This leads to an inability to reflect distinctions between entities and interests when modeling them in a single way. Additionally, the granularity of concepts in the knowledge graphs used for recommendations tends to be coarse, failing to match the fine-grained nature of user interests. This homogenization limits the precise exploitation of knowledge graph data and interest connectivity. To address these limitations, we introduce a novel embedding-based model called InBox. Specifically, various knowledge graph entities and relations are embedded as points or boxes, while user interests are modeled as boxes encompassing interaction history. Representing interests as boxes enables containing collections of item points related to that interest. We further propose that an interest comprises diverse basic concepts, and box intersection naturally supports concept combination. Across three training steps, InBox significantly outperforms state-of-the-art methods like HAKG and KGIN on recommendation tasks. Further analysis provides meaningful insights into the variable value of different KG data for recommendations. In summary, InBox advances recommender systems through box-based interest and concept modeling for sophisticated knowledge graph exploitation., Comment: VLDB 2024 under submission

Answering logical queries on knowledge graphs (KG) poses a significant challenge for machine reasoning. The primary obstacle in this task stems from the inherent incompleteness of KGs. Existing research has predominantly focused on addressing the issue of missing edges in KGs, thereby neglecting another aspect of incompleteness: the emergence of new entities. Furthermore, most of the existing methods tend to reason over each logical operator separately, rather than comprehensively analyzing the query as a whole during the reasoning process. In this paper, we propose a query-aware prompt-fused framework named Pro-QE, which could incorporate existing query embedding methods and address the embedding of emerging entities through contextual information aggregation. Additionally, a query prompt, which is generated by encoding the symbolic query, is introduced to gather information relevant to the query from a holistic perspective. To evaluate the efficacy of our model in the inductive setting, we introduce two new challenging benchmarks. Experimental results demonstrate that our model successfully handles the issue of unseen entities in logical queries. Furthermore, the ablation study confirms the efficacy of the aggregator and prompt components., Comment: Accepted by COLING 2024

The configuration of most robotic systems lies in continuous transformation groups. However, in mobile robot trajectory tracking, many recent works still naively utilize optimization methods for elements in vector space without considering the manifold constraint of the robot configuration. In this letter, we propose a geometric model predictive control (MPC) framework for wheeled mobile robot trajectory tracking. We first derive the error dynamics of the wheeled mobile robot trajectory tracking by considering its manifold constraint and kinematic constraint simultaneously. After that, we utilize the relationship between the Lie group and Lie algebra to convexify the tracking control problem, which enables us to solve the problem efficiently. Thanks to the Lie group formulation, our method tracks the trajectory more smoothly than existing nonlinear MPC. Simulations and physical experiments verify the effectiveness of our proposed methods. Our pure Python-based simulation platform is publicly available to benefit further research in the community.