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Understanding the binding dynamics of liquid molecules is of fundamental importance in physical and life sciences. However, nanoscale fast dynamics pose great challenges for experimental characterization. Conventionally, the binding dynamics have been assumed to be memoryless. Here, we integrate large scale computer simulation, scaling theory, and real-time single particle tracking microscopy with high spatiotemporal precision to unveil a universal memory effect in the binding dynamics of liquid molecules. This binding memory can be quantified by a binding time autocorrelation function, whose power-law decay depends not only on the binding affinity, but also on the topological and materials properties of the surrounding environment. Context-dependent biomolecular binding memory is likely exploited by biological systems to regulate biochemical reactions and biophysical processes. Deciphering this binding memory offers a novel strategy to probe complex biological systems and advanced soft materials.

Designing novel proteins that bind to small molecules is a long-standing challenge in computational biology, with applications in developing catalysts, biosensors, and more. Current computational methods rely on the assumption that the binding pose of the target molecule is known, which is not always feasible, as conformations of novel targets are often unknown and tend to change upon binding. In this work, we formulate proteins and molecules as unified biotokens, and present AtomFlow, a novel deep generative model under the flow-matching framework for the design of ligand-binding proteins from the 2D target molecular graph alone. Operating on representative atoms of biotokens, AtomFlow captures the flexibility of ligands and generates ligand conformations and protein backbone structures iteratively. We consider the multi-scale nature of biotokens and demonstrate that AtomFlow can be effectively trained on a subset of structures from the Protein Data Bank, by matching flow vector field using an SE(3) equivariant structure prediction network. Experimental results show that our method can generate high fidelity ligand-binding proteins and achieve performance comparable to the state-of-the-art model RFDiffusionAA, while not requiring bound ligand structures. As a general framework, AtomFlow holds the potential to be applied to various biomolecule generation tasks in the future.

We study a center-of-mass-conserving Brownian complex Sachdev-Ye-Kitaev model with long-range (power-law) interactions characterized by $1/r^\eta$. The kinetic constraint and long-range interactions conspire to yield rich hydrodynamics associated with the conserved charge, which we reveal by computing the Schwinger-Keldysh effective action. Our result shows that charge transport in this system can be subdiffusive, diffusive, or superdiffusive, with the dynamical exponent controlled by $\eta$. We further employ a doubled Hilbert space methodology to derive an effective action for the out-of-time-order correlator (OTOC), from which we obtain the phase diagram delineating regimes where the lightcone is linear or logarithmic. Our results provide a concrete example of a quantum many-body system with kinetic constraint and long-range interactions in which the emergent hydrodynamic modes and OTOC can be computed analytically., Comment: 4.5 + 10 pages, 4 figures

In this work, we conduct a three-coupled-channel analysis of the $Z_c(3900)$ structure, focusing on the $D\bar{D}^*$, $J/\psi \pi$, and $\rho \eta_c$ channels, based on the one-boson exchange model. Drawing from previous study on the exotic state $T_{cc}$, we only utilize one more parameter to construct the interactions between the channels. Our model successfully reproduces the experimental line shapes of the invariant mass distribution at $\sqrt{s} = 4.23$ and $4.26$ GeV for the three channels. Additionally, the finite-volume energy levels in our model show agreement with current LQCD conclusion. Detailed analysis suggests that the $Z_c(3900)$ peaks in the $\pi J/\psi$ and $\rho \eta_c$ distributions primarily arise from the triangle loop involving the $D_1 \bar{D} D^*$ intermediate system. In the $D\bar{D}^*$ distribution, the threshold peak is generated by the cascade decay mechanism enhanced by a triangle diagram. Moreover, we find a virtual pole located far from the threshold, indicating that $Z_c(3900)$ peaks are not associated with physical pole. We conclude that the $Z_c(3900)$ peaks are predominantly caused by the threshold cusp.

Quantum many-body scars are rare eigenstates hidden within the chaotic spectra of many-body systems, representing a weak violation of the eigenstate thermalization hypothesis (ETH). Identifying these scars, as well as other non-thermal states in complex quantum systems, remains a significant challenge. Besides exact scar states, the nature of other non-thermal states lacking simple analytical characterization remains an open question. In this study, we employ tools from quantum machine learning -- specifically, quantum convolutional neural networks (QCNNs), to explore hidden non-thermal states in chaotic many-body systems. Our simulations demonstrate that QCNNs achieve over 99% single-shot measurement accuracy in identifying all known scars. Furthermore, we successfully identify new non-thermal states in models such as the xorX model, the PXP model, and the far-coupling Su-Schrieffer-Heeger model. In the xorX model, some of these non-thermal states can be approximately described as spin-wave modes of specific quasiparticles. We further develop effective tight-binding Hamiltonians within the quasiparticle subspace to capture key features of these many-body eigenstates. Finally, we validate the performance of QCNNs on IBM quantum devices, achieving single-shot measurement accuracy exceeding 63% under real-world noise and errors, with the aid of error mitigation techniques. Our results underscore the potential of QCNNs to uncover hidden non-thermal states in quantum many-body systems., Comment: 13 pages, 17 figures

We study the intravalley spin-polarized superconductivity in rhombohedral tetralayer graphene, which has been discovered experimentally in Han $et$ $al$ arXiv:2408.15233. We construct a minimal model for the intravalley spin-polarized superconductivity, assuming a simplified anisotropic interaction that depends only on the angle between the incoming and outgoing momenta. Despite the absence of Fermi surface nesting, we show that the superconductivity appears near the Van Hove singularity with the maximal $T_c$ near a bifurcation point of the peaks in the density of states. We identify the topological $p+ip$, topological $h+ih$, and the non-topological nodal $f$-wave pairings as the possible states, which are all pair density wave orders due to the intravalley nature. Furthermore, these pair density wave orders require a finite attractive threshold for superconductivity, resulting in narrow superconducting regions, consistent with experimental findings. We discuss the possible pairing mechanisms and point out that the Kohn-Luttinger mechanism is a plausible explanation for the intravalley spin-polarized superconductivity in the rhombohedral tetralayer graphene., Comment: 7+5 pages, 3+2 figures

The increasing volume of data in relational databases and the expertise needed for writing SQL queries pose challenges for users to access and analyze data. Text-to-SQL (Text2SQL) solves the issues by utilizing natural language processing (NLP) techniques to convert natural language into SQL queries. With the development of Large Language Models (LLMs), a range of LLM-based Text2SQL methods have emerged. This survey provides a comprehensive review of LLMs in Text2SQL tasks. We review benchmark datasets, prompt engineering methods, fine-tuning methods, and base models in LLM-based Text2SQL methods. We provide insights in each part and discuss future directions in this field.

Using $(27.12\pm 0.14)\times10^8$ $\psi(3686)$ decays and data samples of $e^+e^-$ collisions with $\sqrt{s}$ from 4.130 to 4.780~GeV collected with the BESIII detector, we report the first observation of the electromagnetic Dalitz transition $h_c\to e^+e^-\eta_c$ with a statistical significance of $5.4\sigma$. We measure the ratio of the branching fractions $\frac{\mathcal{B}(h_c\rightarrow e^+e^-\eta_c)}{\mathcal{B}(h_c\rightarrow \gamma \eta_c)}$ separately for the $h_c$ samples produced via $\psi(3686)\to\pi^0h_c$ and $e^+e^-\to\pi^+\pi^-h_c$. The average ratio is determined to be $(0.59\pm0.10(\text{stat.})\pm0.04(\text{syst.}))\%$, where the uncertainty includes both statistical and systematic components.

For the first time, robust superconductivity has been independently observed in twisted WSe$_2$ bilayers by two separate groups [Y. Xia et al., arXiv:2405.14784; Y. Guo et al., arXiv:2406.03418.]. In light of this, we explore the possibility of a universal superconducting pairing mechanism in twisted WSe$_2$ bilayers. Using a continuum band structure model and a phenomenological boson-mediated effective electron-electron attraction, we find that intervalley intralayer pairing predominates over interlayer pairing. Notably, despite different experimental conditions, both twisted WSe$_2$ samples exhibit a comparable effective attraction strength. This consistency suggests that the dominant pairing glue is likely independent of the twist angle and layer polarization, pointing to a universal underlying boson-induced pairing mechanism.

We investigate magic-angle twisted bilayer graphene (MATBG) with proximity-induced Ising and Rashba spin-orbit couplings (SOC) in the top layer, as recently achieved experimentally. Utilizing the Bistritzer-MacDonald model with SOCs, we reveal a rich single-particle topological phase diagram featuring topological flat bands across different twist angles and interlayer hopping energies. The evolution of Dirac cones and Chern numbers is examined to understand the topological phase transitions. We find that all phases can be achieved with an experimentally accessible SOC strength ($\sim$1 meV) in systems with angles very close to the magic angle. Furthermore, the van Hove singularity for each topological flat band splits in the presence of SOC, significantly altering the electronic properties. Additionally, we investigate the spin textures of each band in momentum space, discovering a skyrmion-like spin texture in the center of the moir\'e Brillouin zone, which is correlated with the topological phase transitions and can be tuned via the SOCs and an out-of-plane electric field. Our findings provide a comprehensive understanding of the topological flat bands, establishing a foundation for grasping the intrinsic and rich roles of SOCs in MATBG., Comment: 19 pages, 20 figures

We study an interacting composite $(1+1/n)$ Abelian helical edge state made of a regular helical liquid carrying charge $e$ and a (fractionalized) helical liquid carrying charge $e/n$. A systematic framework is developed for these composite $(1+1/n)$ Abelian helical edge states with $n=1,2,3$. For $n=2$, the composite edge state consists of a regular helical Luttinger liquid and a fractional topological insulator (the Abelian $Z_4$ topological order) edge state arising from half-filled conjugated Chern bands. The composite edge state with $n=2$ is pertinent to the recent twisted MoTe$_2$ experiment, suggesting a possible fractional topological insulator with conductance $\frac{3}{2}\frac{e^2}{h}$ per edge. Using bosonization, we construct generic phase diagrams in the presence of $weak$ Rashba spin-orbit coupling. In addition to a phase of free bosons, we find a time-reversal symmetry-breaking localized insulator, two perfect positive drag phases, a perfect negative drag phase (for $n=2,3$), a time-reversal symmetric Anderson localization (only for $n=1$), and a disorder-dominated metallic phase analogous to the $\nu=2/3$ disordered fractional quantum Hall edges (only for $n=3$). We further compute the two-terminal edge-state conductance, the primary experimental characterization for the (fractional) topological insulator. Remarkably, the negative drag phase gives rise to an unusual edge-state conductance, $(1-1/n)\frac{e^2}{h}$, not directly associated with the filling factor. We further investigate the effect of an applied in-plane magnetic field. For $n>1$, the applied magnetic field can result in a phase with edge-state conductance $\frac{1}{n}\frac{e^2}{h}$, providing another testable signature. Our work establishes a systematic understanding of the composite $(1+1/n)$ Abelian helical edge, paving the way for future experimental and theoretical studies., Comment: 22 pages and 6 figures. Published version

Algebraic boundaries of convex semi-algebraic sets are closely related to polynomial optimization problems. Building upon Rainer Sinn's work, we refine the stratification of iterated singular loci to a Whitney (a) stratification, which gives a list of candidates of varieties whose dual is an irreducible component of the algebraic boundary of the dual convex body. We also present an algorithm based on Teissier's criterion to compute Whitney (a) stratifications, which employs conormal spaces and prime decomposition.

Twin support vector machine (TSVM) is a very classical and practical classifier for pattern classification. However, the traditional TSVM has two limitations. Firstly, it uses the L_2-norm distance metric that leads to its sensitivity to outliers. Second, it needs to select the appropriate kernel function and the kernel parameters for nonlinear classification. To effectively avoid these two problems, this paper proposes a robust capped L_1-norm kernel-free quadratic surface twin support vector machine (CL_1QTSVM). The strengths of our model are briefly summarized as follows. 1) The robustness of our model is further improved by employing the capped L_1 norm distance metric. 2) Our model is a kernel-free method that avoids the time-consuming process of selecting appropriate kernel functions and kernel parameters. 3) The introduction of L_2-norm regularization term to improve the generalization ability of the model. 4) To efficiently solve the proposed model, an iterative algorithm is developed. 5) The convergence, time complexity and existence of locally optimal solutions of the developed algorithms are further discussed. Numerical experiments on numerous types of datasets validate the classification performance and robustness of the proposed model.

Recently, video object segmentation (VOS) networks typically use memory-based methods: for each query frame, the mask is predicted by space-time matching to memory frames. Despite these methods having superior performance, they suffer from two issues: 1) Challenging data can destroy the space-time coherence between adjacent video frames. 2) Pixel-level matching will lead to undesired mismatching caused by the noises or distractors. To address the aforementioned issues, we first propose to generate an auxiliary frame between adjacent frames, serving as an implicit short-temporal reference for the query one. Next, we learn a prototype for each video object and prototype-level matching can be implemented between the query and memory. The experiment demonstrated that our network outperforms the state-of-the-art method on the DAVIS 2017, achieving a J&F score of 86.4%, and attains a competitive result 85.0% on YouTube VOS 2018. In addition, our network exhibits a high inference speed of 32+ FPS., Comment: Accepted by ICME 2024. 6 pages, 10 figures

The key to device-edge co-inference paradigm is to partition models into computation-friendly and computation-intensive parts across the device and the edge, respectively. However, for Graph Neural Networks (GNNs), we find that simply partitioning without altering their structures can hardly achieve the full potential of the co-inference paradigm due to various computational-communication overheads of GNN operations over heterogeneous devices. We present GCoDE, the first automatic framework for GNN that innovatively Co-designs the architecture search and the mapping of each operation on Device-Edge hierarchies. GCoDE abstracts the device communication process into an explicit operation and fuses the search of architecture and the operations mapping in a unified space for joint-optimization. Also, the performance-awareness approach, utilized in the constraint-based search process of GCoDE, enables effective evaluation of architecture efficiency in diverse heterogeneous systems. We implement the co-inference engine and runtime dispatcher in GCoDE to enhance the deployment efficiency. Experimental results show that GCoDE can achieve up to $44.9\times$ speedup and $98.2\%$ energy reduction compared to existing approaches across various applications and system configurations., Comment: Accepted by DAC'24

We study theoretically the magic-angle twisted bilayer graphene with proximity-induced Ising and Rashba spin-orbit couplings on the top layer. Topological flat bands (with three distinct phases) are generically realized by the spin-orbit couplings. Using a mean field analysis, we find that (partial) valley polarization prevails for a wide range of doping, suppressing the usual superconductivity with a pairing between time-reversal partners. Remarkably, we uncover that observable unconventional intervalley interband phonon-mediated superconductivity (with the highest $T_c\approx 1.2$K) can coexist with strong valley imbalance due to the approximate Fermi surface nesting between two flat bands not related by time-reversal symmetry, and the dominant pairing is an intersublattice Ising pairing, corresponding to a mixture of $p$- and $d$-waves. In contrast, the intrasublattice Ising phonon-mediated superconductivity with $s$- and $f$-wave mixing emerges in the absence of valley imbalance. Our work reveals an unprecedented route of realizing unconventional superconductivity., Comment: 9+16 pages, 4+5 figures; published version

The classification of carotid artery ultrasound images is a crucial means for diagnosing carotid plaques, holding significant clinical relevance for predicting the risk of stroke. Recent research suggests that utilizing plaque segmentation as an auxiliary task for classification can enhance performance by leveraging the correlation between segmentation and classification tasks. However, this approach relies on obtaining a substantial amount of challenging-to-acquire segmentation annotations. This paper proposes a novel weakly supervised auxiliary task learning network model (WAL-Net) to explore the interdependence between carotid plaque classification and segmentation tasks. The plaque classification task is primary task, while the plaque segmentation task serves as an auxiliary task, providing valuable information to enhance the performance of the primary task. Weakly supervised learning is adopted in the auxiliary task to completely break away from the dependence on segmentation annotations. Experiments and evaluations are conducted on a dataset comprising 1270 carotid plaque ultrasound images from Wuhan University Zhongnan Hospital. Results indicate that the proposed method achieved an approximately 1.3% improvement in carotid plaque classification accuracy compared to the baseline network. Specifically, the accuracy of mixed-echoic plaques classification increased by approximately 3.3%, demonstrating the effectiveness of our approach.

The emergence of bulk carbon ferromagnetism is long-expected over years. At nanoscale, carbon ferromagnetism was detected by analyzing the magnetic edge states via scanning tunneling microscopy(STM), and its origin can be explained by local redistribution of electron wave function. In larger scale, carbon ferromagnetism can be created by deliberately producing defects in graphite, and detected by macroscopic technical magnetization. Meanwhile, it becomes crucial to determine that the detected magnetization is originated from carbon rather than from magnetic impurities. One solution is X-ray magnetic circular dichroism (XMCD). Nonetheless, a reproducible, full section of XMCD spectrum across C-1s absorption energy has not appeared yet, which should be decisive for assuring the indisputable existence of bulk carbon ferromagnetism. Besides, the lack of direct observation on the atomic structure of the ferromagnetic carbon leaves the structural origin of its ferromagnetism still in mist. In this work, for detecting bulk carbon ferromagnetism, we managed to grow all-carbon film consisting of vertically aligned graphene multi-edge (VGME), which wove into a three-dimensional hyperfine-porous network. Magnetization (M-H) curves and XMCD spectra co-confirmed bulk carbon ferromagnetism of VGME at room temperature, with the average unit magnetic momentum of ~0.0006 miuB/atom. The influence of magnetic impurities on magnetization was excluded by both absorption spectra and inductively coupled plasma mass spectrometry measurements. The spin transfer behavior also verified the long-range and robust feature of the bulk carbon ferromagnetism. Our work provides direct evidence of elementary resolved bulk carbon ferromagnetism at room temperature and clarifies its origin from pi-electrons at graphene edges.

Motivated by the recently developed classical spin model for Migdal-Eliashberg theory, we develop new numerical and analytical methods based on this spin-chain representation and apply these methods to the Bogoliuov-Tomachov-Morel-Anderson pairing potential, which incorporates the phonon-mediated attraction and Coulomb repulsion. We show that the Monte Carlo method with heat bath updates can efficiently obtain the gap functions even for the situations challenging for the iterative solvers, suggesting an unprecedented robust approach for solving the full nonlinear Migdal-Eliashberg theory. Moreover, we derive the renormalization of all the couplings by tracing out the high-frequency spins in the partition function. The derived analytical renormalization equations produce the well-known $\mu^*$ effect for the Bogoliuov-Tomachov-Morel-Anderson pairing potential and can be generalized to other superconductivity problems. We further point out that several interesting features (e.g., sign changing in the frequency-dependent gap function) can be intuitively understood using the classical spin-chain representation for Migdal-Eliasherg theory. Our results show the advantage of using the spin-chain representation for solving Migdal-Eliashberg theory and provide new ways for tackling general superconductivity problems., Comment: 13 pages, 8 figures. Published version

We investigate the doubly bottom state $T^-_{bb}$ composed of two bottom mesons with $J^P=1^+$. The potentials are obtained using the one-boson exchange model. With the heavy quark flavor symmetry, all the parameters in the model are determined by fitting the experimental data of doubly charmed state $T_{cc}^{+}$ from our previous work. Our analysis indicates that the isospin symmetry is well-preserved. We identify a deeply bound $T^-_{bb}$ state with quantum numbers $I(J^{P})=0(1^+)$, in contrast to the loosely bound $T_{cc}^{+}$. Additionally, we discover a resonant $T^-_{bb}$ state with $I(J^{P})=1(1^+)$. Furthermore, our investigation into the $\bar{B}\bar{B}^*$-$\bar{B}^*\bar{B}^*$ coupled channel effect reveals its important impact. The binding energy of the bound $I(J^{P})=0(1^+)$ states becomes deeper, while the resonant $T^-_{bb}$ state with $I(J^{P})=1(1^+)$ remains nearly unchanged., Comment: 24 pages, 7 figures, add discussions on the pole trajectory of the heavy meson pairs and a I=1 DD* resonance, accepted for publication in PRD

Large language models (LLMs) have demonstrated exceptional performance in various natural language processing tasks, yet their efficacy in more challenging and domain-specific tasks remains largely unexplored. This paper presents FinEval, a benchmark specifically designed for the financial domain knowledge in the LLMs. FinEval is a collection of high-quality multiple-choice questions covering Finance, Economy, Accounting, and Certificate. It includes 4,661 questions spanning 34 different academic subjects. To ensure a comprehensive model performance evaluation, FinEval employs a range of prompt types, including zero-shot and few-shot prompts, as well as answer-only and chain-of-thought prompts. Evaluating state-of-the-art Chinese and English LLMs on FinEval, the results show that only GPT-4 achieved an accuracy close to 70% in different prompt settings, indicating significant growth potential for LLMs in the financial domain knowledge. Our work offers a more comprehensive financial knowledge evaluation benchmark, utilizing data of mock exams and covering a wide range of evaluated LLMs.

Motivated by the recently discovered incompressible insulating phase in the bilayer graphene exciton experiment [arXiv:2306.16995], we study using bosonization two Coulomb-coupled spinless quantum wires and examine the possibility of realizing the similar phenomenology in one dimension. We explore the possible phases as functions of $k_{F}$'s and interactions. We show that an incompressible insulating phase can arise for two lightly doped electron-hole quantum wires (i.e., $k_{F1}=-k_{F2}$ and small $|k_{F1}|$) due to strong interwire interactions. Such an insulating phase forms a parity-even wire-antisymmetric charge density wave without interwire phase coherence, which melts to a phase allowing for a perfect negative drag upon heating. The finite-temperature response is qualitatively consistent with the ``exciton solid'' phenomenology in the bilayer graphene exciton experiment., Comment: 11 pages, 4 figures. Published version

Quantum kinetically constrained models can exhibit a wealth of dynamical phenomena ranging from anomalous transport to Hilbert-space fragmentation (HSF). We study a class of one-dimensional particle number conserving systems where particle hoppings are subjected to an East-like constraint, akin to facilitated spin models in classical glasses. While such a kinetic constraint leads to HSF, we find that the degree of fragmentation exhibits a sharp transition as the average particle density is varied. Below a critical density, the system transitions from being weakly fragmented where most of the initial states thermalize diffusively, to strongly fragmented where the dynamics are frozen and the system fails to thermalize. Remarkably, the East model allows for both efficient numerical simulations and analytic solutions of various diagnostics of the phase transition, from which we obtain a set of exact critical exponents. We find that the freezing transition in particle-conserving East models belongs to the same universality class as dipole-conserving fracton systems. Our results provide a tractable minimal model for filling-induced freezing transitions associated with HSF, which can be readily tested in state-of-the-art quantum platforms., Comment: 9 pages, 8 figures

We study free fermion systems under adaptive quantum dynamics consisting of unitary gates and projective measurements followed by corrective unitary operations. We further introduce a classical flag for each site, allowing for an active or inactive status which determines whether or not the unitary gates are allowed to apply. In this dynamics, the individual quantum trajectories exhibit a measurement-induced entanglement transition from critical to area-law scaling above a critical measurement rate, similar to previously studied models of free fermions under continuous monitoring. Furthermore, we find that the corrective unitary operations can steer the system into a state characterized by charge-density-wave order. Consequently, an additional phase transition occurs, which can be observed at both the level of the quantum trajectory and the quantum channel. We establish that the entanglement transition and the steering transition are fundamentally distinct. The latter transition belongs to the parity-conserving (PC) universality class, arising from the interplay between the inherent fermionic parity and classical labelling. We demonstrate both the entanglement and the steering transitions via efficient numerical simulations of free fermion systems, which confirm the PC universality class of the latter., Comment: 10 pages, 9 figures; [v2] Updated references

We have investigated the internal structure of the open- and hidden-charmed ($DD^*$/$\bar DD^*$) molecules in the unified framework. We first fit the experimental lineshape of the $T^+_{cc}$ state and extract the $DD^*$ interaction, from which the $T^+_{cc}$ is assumed to arise solely. Then we obtain the $D\bar{D}^*$ interaction by charge conjugation. Our results show that the $D\bar{D}^*$ interaction is attractive but insufficient to form $X(3872)$ as a bound state. Instead, its formation requires the crucial involvement of the coupled channel effect between the $D\bar D^*$ and $c\bar c$ components, although the $c\bar c$ accounts for approximately $1\%$ only. Besides $X(3872)$, we have obtained a higher-energy state around $3957.9$ MeV with a width of $16.7$ MeV, which may be a potential candidate for the $X(3940)$. In $J^{PC}=1^{+-}$ sector, we have found two states related to the iso-scalar $\tilde X(3872)$ and $h_c(2P)$, respectively. Our combined study provides valuable insights into the nature of these $DD^*$/$D\bar D^*$ exotic states., Comment: 10 pages, 5 figures, version accepted for publication in Science Bulletin

Brain network analysis is a useful approach to studying human brain disorders because it can distinguish patients from healthy people by detecting abnormal connections. Due to the complementary information from multiple modal neuroimages, multimodal fusion technology has a lot of potential for improving prediction performance. However, effective fusion of multimodal medical images to achieve complementarity is still a challenging problem. In this paper, a novel hierarchical structural-functional connectivity fusing (HSCF) model is proposed to construct brain structural-functional connectivity matrices and predict abnormal brain connections based on functional magnetic resonance imaging (fMRI) and diffusion tensor imaging (DTI). Specifically, the prior knowledge is incorporated into the separators for disentangling each modality of information by the graph convolutional networks (GCN). And a disentangled cosine distance loss is devised to ensure the disentanglement's effectiveness. Moreover, the hierarchical representation fusion module is designed to effectively maximize the combination of relevant and effective features between modalities, which makes the generated structural-functional connectivity more robust and discriminative in the cognitive disease analysis. Results from a wide range of tests performed on the public Alzheimer's Disease Neuroimaging Initiative (ADNI) database show that the proposed model performs better than competing approaches in terms of classification evaluation. In general, the proposed HSCF model is a promising model for generating brain structural-functional connectivities and identifying abnormal brain connections as cognitive disease progresses., Comment: 4 figures

Motivated by the recent studies of intrinsic local moments and Kondo-driven phases in magic-angle twisted bilayer graphene, we investigate the renormalization of Kondo coupling ($J_K$) and the competing Hund's rule interaction ($J$) in the low-energy limit. Specifically, we consider a surrogate single-impurity generalized Kondo model and employ the poor man's scaling approach. The scale-dependent $J_K$ and $J$ are derived analytically within the one-loop poor man's scaling approach, and the Kondo temperature ($T_K$) and the characteristic Hund's rule coupling ($J^*$, defined by the renormalized value of $J$ at some small finite energy scale) are estimated over a wide range of filling factors. We find that $T_K$ depends strongly on the filling factors as well as the value of $J_K$. Slightly doping away from integer fillings and/or increasing $J_K$ may substantially enhance $T_K$ in the parameter regime relevant to experiments. $J^*$ is always reduced from the bare value of $J$, but the filling factor dependence is not as significant as it is for $T_K$. Our results suggest that it is essential to incorporate the renormalization of $J_K$ and $J$ in the many-body calculations, and Kondo screening should occur for a wide range of fractional fillings in magic-angle twisted bilayer graphene, implying the existence of Kondo-driven correlated metallic phases. We also point out that the observation of distinct phases at integer fillings in different samples may be due to the variation of $J_K$ in addition to disorder and strain in the experiments., Comment: 22 pages, 10 figures; published version

We propose a scheme that generates period-doubled responses via periodically driving certain Hamiltonians hosting quantum many-body scars, akin to recent experimental observations in driven Rydberg atom arrays. Our construction takes advantage of an su(2) spectrum generating algebra associated with the static quantum-scarred Hamiltonian, which enacts a $\pi$-rotation within the scar subspace after one period of time evolution with appropriately chosen driving parameters. This yields period-doubled (subharmonic) responses in local observables for any choice of initial state residing in the scar subspace. The quasienergy spectrum features atypical $\pi$-paired eigenstates embedded in an otherwise fully thermal spectrum.The protocol requires neither a large driving frequency nor a large driving amplitude, and is thus distinct from the prethermalization physics in previous investigations of the driven PXP model. We demonstrate our scheme using several spin-1/2 and spin-1 quantum scarred models possessing an exact su(2) spectrum generating algebra, as well as a symmetry-deformed PXP model, where the su(2) algebra is only approximate. Our results extend the class of models hosting quantum many-body scars that can be leveraged to yield time-crystalline behaviors under periodic driving., Comment: 4.5+5 pages, 3 figures

This paper proposes a Robust and Efficient Memory Network, referred to as REMN, for studying semi-supervised video object segmentation (VOS). Memory-based methods have recently achieved outstanding VOS performance by performing non-local pixel-wise matching between the query and memory. However, these methods have two limitations. 1) Non-local matching could cause distractor objects in the background to be incorrectly segmented. 2) Memory features with high temporal redundancy consume significant computing resources. For limitation 1, we introduce a local attention mechanism that tackles the background distraction by enhancing the features of foreground objects with the previous mask. For limitation 2, we first adaptively decide whether to update the memory features depending on the variation of foreground objects to reduce temporal redundancy. Second, we employ a dynamic memory bank, which uses a lightweight and differentiable soft modulation gate to decide how many memory features need to be removed in the temporal dimension. Experiments demonstrate that our REMN achieves state-of-the-art results on DAVIS 2017, with a $\mathcal{J\&F}$ score of 86.3% and on YouTube-VOS 2018, with a $\mathcal{G}$ over mean of 85.5%. Furthermore, our network shows a high inference speed of 25+ FPS and uses relatively few computing resources., Comment: Accepted by ICME 2023. 6 pages, 6 figures

We propose a mechanism for reaching pseudorandom quantum states, computationally indistinguishable from Haar random, with shallow log-n depth quantum circuits, where n is the number of qudits. We argue that $\log n$ depth 2-qubit-gate-based generic random quantum circuits that are claimed to provide a lower bound on the speed of information scrambling, cannot produce computationally pseudorandom quantum states. This conclusion is connected with the presence of polynomial (in $n$) tails in the stay probability of short Pauli strings that survive evolution through such shallow circuits. We show, however, that stay-probability-tails can be eliminated and pseudorandom quantum states can be accomplished with shallow $\log n$ depth circuits built from a special universal family of `inflationary' quantum (IQ) gates. We prove that IQ-gates cannot be implemented with 2-qubit gates, but can be realized either as a subset of 2-qudit-gates in $U(d^2)$ with $d\ge 3$ and $d$ prime, or as special 3-qubit gates.

In the field of semi-supervised medical image segmentation, the shortage of labeled data is the fundamental problem. How to effectively learn image features from unlabeled images to improve segmentation accuracy is the main research direction in this field. Traditional self-training methods can partially solve the problem of insufficient labeled data by generating pseudo labels for iterative training. However, noise generated due to the model's uncertainty during training directly affects the segmentation results. Therefore, we added sample-level and pixel-level uncertainty to stabilize the training process based on the self-training framework. Specifically, we saved several moments of the model during pre-training, and used the difference between their predictions on unlabeled samples as the sample-level uncertainty estimate for that sample. Then, we gradually add unlabeled samples from easy to hard during training. At the same time, we added a decoder with different upsampling methods to the segmentation network and used the difference between the outputs of the two decoders as pixel-level uncertainty. In short, we selectively retrained unlabeled samples and assigned pixel-level uncertainty to pseudo labels to optimize the self-training process. We compared the segmentation results of our model with five semi-supervised approaches on the public 2017 ACDC dataset and 2018 Prostate dataset. Our proposed method achieves better segmentation performance on both datasets under the same settings, demonstrating its effectiveness, robustness, and potential transferability to other medical image segmentation tasks. Keywords: Medical image segmentation, semi-supervised learning, self-training, uncertainty estimation

The information diffusion performance of GCN and its variant models is limited by the adjacency matrix, which can lower their performance. Therefore, we introduce a new framework for graph convolutional networks called Hybrid Diffusion-based Graph Convolutional Network (HD-GCN) to address the limitations of information diffusion caused by the adjacency matrix. In the HD-GCN framework, we initially utilize diffusion maps to facilitate the diffusion of information among nodes that are adjacent to each other in the feature space. This allows for the diffusion of information between similar points that may not have an adjacent relationship. Next, we utilize graph convolution to further propagate information among adjacent nodes after the diffusion maps, thereby enabling the spread of information among similar nodes that are adjacent in the graph. Finally, we employ the diffusion distances obtained through the use of diffusion maps to regularize and constrain the predicted labels of training nodes. This regularization method is then applied to the HD-GCN training, resulting in a smoother classification surface. The model proposed in this paper effectively overcomes the limitations of information diffusion imposed only by the adjacency matrix. HD-GCN utilizes hybrid diffusion by combining information diffusion between neighborhood nodes in the feature space and adjacent nodes in the adjacency matrix. This method allows for more comprehensive information propagation among nodes, resulting in improved model performance. We evaluated the performance of DM-GCN on three well-known citation network datasets and the results showed that the proposed framework is more effective than several graph-based semi-supervised learning methods., Comment: 16 pages, 4 figures

We demonstrate slow dynamics and constrained motion of domain walls in one-dimensional (1D) interacting bosons with double-well dispersion. In the symmetry-broken regime, the domain-wall motion is ``fractonlike'' -- a single domain wall cannot move freely, while two nearby domain walls can move collectively. Consequently, we find an Ohmic-like linear response and a vanishing superfluid stiffness, which are atypical for a Bose condensate in a 1D translation invariant closed quantum system. Near Lifshitz quantum critical point, we obtain superfluid stiffness $\rho_s\sim T$ and sound velocity $v_s\sim T^{1/2}$, showing similar unconventional low-temperature slow dynamics to the symmetry-broken regime. Particularly, the superfluid stiffness suggests an order by disorder effect as $\rho_s$ increases with temperature. Our results pave the way for studying fractons in ultracold atom experiments., Comment: 7+6 pages, 3+1 figures. Published version

LiDAR and camera fusion techniques are promising for achieving 3D object detection in autonomous driving. Most multi-modal 3D object detection frameworks integrate semantic knowledge from 2D images into 3D LiDAR point clouds to enhance detection accuracy. Nevertheless, the restricted resolution of 2D feature maps impedes accurate re-projection and often induces a pronounced boundary-blurring effect, which is primarily attributed to erroneous semantic segmentation. To well handle this limitation, we propose a general multi-modal fusion framework Multi-Sem Fusion (MSF) to fuse the semantic information from both the 2D image and 3D points scene parsing results. Specifically, we employ 2D/3D semantic segmentation methods to generate the parsing results for 2D images and 3D point clouds. The 2D semantic information is further reprojected into the 3D point clouds with calibration parameters. To handle the misalignment between the 2D and 3D parsing results, we propose an Adaptive Attention-based Fusion (AAF) module to fuse them by learning an adaptive fusion score. Then the point cloud with the fused semantic label is sent to the following 3D object detectors. Furthermore, we propose a Deep Feature Fusion (DFF) module to aggregate deep features at different levels to boost the final detection performance. The effectiveness of the framework has been verified on two public large-scale 3D object detection benchmarks by comparing them with different baselines. The experimental results show that the proposed fusion strategies can significantly improve the detection performance compared to the methods using only point clouds and the methods using only 2D semantic information. Most importantly, the proposed approach significantly outperforms other approaches and sets state-of-the-art results on the nuScenes testing benchmark.

We systematically study emergent Kondo lattice models from magic-angle twisted bilayer graphene using the topological heavy fermion representation. At the commensurate fillings, we demonstrate a series of symmetric strongly correlated metallic states driven by the hybridization between a triangular lattice of $SU(8)$ local moments and delocalized fermions. In particular, a (fragile) topological Dirac Kondo semimetal can be realized, providing a potential explanation for the symmetry-preserving correlated state at $\nu=0$. We further investigate the stability of the Dirac Kondo semimetal by constructing a quantum phase diagram showing the interplay between Kondo hybridization and magnetic correlation. The destruction of Kondo hybridization suggests that the magic-angle twisted bilayer graphene may be on the verge of a solid-state quantum simulator for novel magnetic orders on a triangular lattice. Experimental implications are also discussed., Comment: 6+10 pages, 3+4 figures. Published version

The intensely studied measurement-induced entanglement phase transition has become a hallmark of non-unitary quantum many-body dynamics. Usually, such a transition only shows up at the level of each individual quantum trajectory, and is absent for the density matrix averaged over measurement outcomes. In this work, we introduce a class of adaptive random circuit models with feedback that exhibit transitions in both settings. After each measurement, a unitary operation is either applied or not depending on the measurement outcome, which steers the averaged density matrix towards a unique state above a certain measurement threshold. Interestingly, the transition for the density matrix and the entanglement transition in the individual quantum trajectory in general happen at \textit{different} critical measurement rates. We demonstrate that the former transition belongs to the parity-conserving universality class by an explicit mapping to a classical branching-annihilating random walk process., Comment: 4.5 pages,6 figures; 5 pages of supplemental material. [v2] Figures compressed to reduce file size. [v3] Reference added

In 1936, Krull asked if the integral closure of a primary ideal is still primary. Fifty years later, Huneke partially answered this question by giving a primary polynomial ideal whose integral closure is not primary in a regular local ring of characteristic $p=2$. We provide counterexamples to Krull's question regarding polynomial rings with any characteristics. We also find that the Jacobian ideal $J$ of the polynomial $f = x^6 + y^6 + x^4 z t + z^3$ given by Brian\c{c}on and Speder in 1975 is a counterexample to Krull's question. Let $V_1$ be the hypersurface defined by $f = 0$ and $V_2$ be its singular locus. Brian\c{c}on and Speder proved that Whitney equisingularity does not imply Zariski equisingularity by showing that the pair $(V_1 \setminus V_2,\ V_2)$ satisfies Whitney's conditions around the origin but fails Zariski's equisingular conditions. We discover that the pair $(V_1 \setminus V_2,\ V_2)$ fails Whitney's conditions at the variety of the embedded prime of the integral closure $\bar{J}$, which means that $V_1$ is not Whitney regular along $V_2$. Moreover, we also show that Whitney stratification of this hypersurface is different from the stratification of isosingular sets given by Hauenstein and Wampler, which is related to Thom-Boardman singularity., Comment: 10 pages, 6 figures

In this paper, a probabilistic interpretation for the viscosity solution of a parabolic partial differential equation is obtained by virtue of the solution of a class of quadratic backward stochastic differential equations (BSDEs, for short). Furthermore, we prove the convergence and the large deviation principle for the solution of this class of quadratic BSDEs, which is associated with a family of Markov processes with the diffusion coefficients that tend to be zero.

Quantum many-body scar states are special eigenstates of nonintegrable models with distinctive entanglement features that give rise to infinitely long-lived coherent dynamics under quantum quenches from certain initial states. We elaborate on a construction of quantum many-body scar states in which they emerge from Einstein-Podolsky-Rosen (EPR) states in systems with two layers, wherein the two layers are maximally entangled. We apply this construction to spin systems as well as systems of itinerant fermions and bosons and demonstrate how symmetries can be harnessed to enhance its versatility. We show that several well-known examples of quantum many-body scars, including the tower of states in the spin-1 XY model and the $\eta$-pairing states in the Fermi-Hubbard model, can be understood within this formalism. We also demonstrate how an {\it infinite} tower of many-body scar states can emerge in bilayer Bose-Hubbard models with charge conservation., Comment: 19 pages, 8 figures

The nature of multiple samples to extract correlation information limits the applications of ghost imaging of moving objects. A novel multi-to-one neural network is proposed and the concept of "batch frame" is introduced to improve the serial imaging method. The neural network extracts more correlation information from a small number of samples, thus reducing the sampling ratio of the ghost imaging technique. We combine the correlation characteristics between images to propose a frame merging algorithm, which eliminates the dynamic blur of high-speed moving objects and further improves the reconstruction quality of moving object images at a low sampling ratio. The experimental results are consistent with the simulation results., Comment: 12 pages, 7 figures

Conservation laws and the associated hydrodynamic modes have important consequences on the growth of higher R\'enyi entropies in isolated quantum systems. It has been shown in various random unitary circuits and Hamiltonian systems that the dynamics of the R\'enyi entropies in the presence of a U(1) symmetry obey $S^{(n\geq 2)}(t) \propto t^{1/z}$, where $z$ is identified as the dynamical exponent characterizing transport of the conserved charges. Here, however, we demonstrate that this simple identification may not hold in certain quantum systems with kinetic constraints. In particular, we study two types of U(1)-symmetric quantum automaton circuits with XNOR and Fredkin constraints, respectively. We find numerically that while spin transport in both models is subdiffusive, the second R\'enyi entropy grows diffusively in the XNOR model, and superdiffusively in the Fredkin model. For systems with XNOR constraint, this distinction arises since the spin correlation function can be attributed to an emergent tracer dynamics of tagged particles, whereas the R\'enyi entropies are constrained by collective transport of the particles. Our results suggest that care must be taken when relating transport and entanglement entropy dynamics in generic quantum systems with conservation laws., Comment: 6 pages, 7 figures

In this paper, we study one-dimensional backward stochastic differential equation (BSDE, for short), whose coefficient $f$ is Lipschitz in $y$ but only continuous in $z$. In addition, if the terminal condition $\xi$ has bounded Malliavin derivative, we prove some uniqueness results for the BSDE with quadratic and linear growth in $z$, respectively.

The semi-inclusive electroproduction of exotic hadrons, including the $T_{cc}$, $P_{cs}$, and hidden-charm baryon-antibaryon states, is explored under the assumption that they are $S$-wave hadronic molecules of a pair of charmed hadrons. We employ the Monte Carlo event generator Pythia to produce the hadron pairs and then bind them together to form hadronic molecules. With the use of such a production mechanism, the semi-inclusive electroproduction rates are estimated at the order-of-magnitude level. Our results indicate that a larger number of $P_{cs}$ states and $\Lambda_c\bar{\Lambda}_c$ molecules can be produced at the proposed electron-ion colliders in China (EicC) and in the US (EIC). The results also suggest that the $T_{cc}$ states and other hidden-charm baryon-antibaryon states can be searched for at EIC. Besides, the potential 24-GeV upgrade of the Continuous Beam Accelerator Facility at the Thomas Jefferson National Accelerator Facility can play an important role in the search for the hidden-charm tetraquark and pentaquark states due to its high luminosity., Comment: 20 pages, 4 figures. Version to appear in PRD

Solid results from Transformers have made them prevailing architectures in various natural language and vision tasks. As a default component in Transformers, Layer Normalization (LN) normalizes activations within each token to boost the robustness. However, LN requires on-the-fly statistics calculation in inference as well as division and square root operations, leading to inefficiency on hardware. What is more, replacing LN with other hardware-efficient normalization schemes (e.g., Batch Normalization) results in inferior performance, even collapse in training. We find that this dilemma is caused by abnormal behaviors of activation statistics, including large fluctuations over iterations and extreme outliers across layers. To tackle these issues, we propose Unified Normalization (UN), which can speed up the inference by being fused with other linear operations and achieve comparable performance on par with LN. UN strives to boost performance by calibrating the activation and gradient statistics with a tailored fluctuation smoothing strategy. Meanwhile, an adaptive outlier filtration strategy is applied to avoid collapse in training whose effectiveness is theoretically proved and experimentally verified in this paper. We demonstrate that UN can be an efficient drop-in alternative to LN by conducting extensive experiments on language and vision tasks. Besides, we evaluate the efficiency of our method on GPU. Transformers equipped with UN enjoy about 31% inference speedup and nearly 18% memory reduction. Code will be released at https://github.com/hikvision-research/Unified-Normalization., Comment: ACM MM'22

The event generator BEEC [Z. Yang, X.G. Wu and X.Y. Wang, Comput. Phys. Commun. 184, 2848 (2013)] was devoted to the simulation of heavy quarkonium production at an unpolarized electron-positron collider. We upgraded it here by adding the generation of quarkonium with polarized electron and positron beams. In addition, the production of color-singlet 2S-wave states were included. Several future electron-positron colliders with high luminosity have been under discussion in the past decade. Especially, their possibility of producing polarized beams is important for providing more insights into the underlying physics. This upgraded version offers a useful tool for the feasibility study on quarkonium from the experimental side., Comment: 5 pages, 1 figure

We develop a systematic theory for excitons subject to Fermi-Hubbard physics in moir\'e twisted transition metal dichalcogenides (TMDs). Specifically, we consider excitons in moir\'e systems for which the valence band is in the Mott-insulating regime. These "Mott-moir\'e excitons", which are achievable in twisted TMD heterobilayers, are bound states of a magnetic polaron in the valence band and a free electron in the conduction band. We find significantly narrower exciton bandwidths in the presence of Hubbard physics, serving as a potential experimental signature of strong correlations. We also demonstrate the high tunability of Mott-moir\'e excitons through the dependence of their binding energies, diameters, and bandwidths on the moir\'e period. Our work provides guidelines for future exploration of strongly correlated excitons in twisted TMD heterobilayers.

Combining the quark model, the quark-pair-creation mechanism and $B^{(*)}\bar K$ interaction, we have investigated the near-threshold $P$-wave $B_s$ states in the framework of the Hamiltonian effective field theory. With the heavy quark flavor symmetry, all the parameters are determined in the $D_s$ sector by fitting the lattice data. The masses of the bottom-strange partners of the $D^{*}_{s0}(2317)$ and $D^{*}_{s1}(2460)$ are predicted to be $M_{B^{*}_{s0}}=5730.2_{-1.5}^{+2.4}$ MeV and $M_{B^{*}_{s1}}= 5769.6_{-1.6}^{+2.4}$ MeV, respectively, which are well consistent with the lattice QCD simulation. The two $P$-wave $B_s$ states are the mixtures of the bare $\bar b s$ core and $B^{(*)}\bar K$ component. Moreover, we find a crossing point between the energy levels with and without the interaction Hamiltonian in the finite volume spectrum in the $0^+$ case, which corresponds to a CDD (Castillejo-Dalitz-Dyson) zero in the $T$-matrix of the $B\bar K$ scattering. This CDD zero will help deepen the insights of the near-threshold states and can be examined by future lattice calculation., Comment: 20 pages, 4 figures. Version accepted by JHEP

In the semi-supervised learning field, Graph Convolution Network (GCN), as a variant model of GNN, has achieved promising results for non-Euclidean data by introducing convolution into GNN. However, GCN and its variant models fail to safely use the information of risk unlabeled data, which will degrade the performance of semi-supervised learning. Therefore, we propose a Safe GCN framework (Safe-GCN) to improve the learning performance. In the Safe-GCN, we design an iterative process to label the unlabeled data. In each iteration, a GCN and its supervised version(S-GCN) are learned to find the unlabeled data with high confidence. The high-confidence unlabeled data and their pseudo labels are then added to the label set. Finally, both added unlabeled data and labeled ones are used to train a S-GCN which can achieve the safe exploration of the risk unlabeled data and enable safe use of large numbers of unlabeled data. The performance of Safe-GCN is evaluated on three well-known citation network datasets and the obtained results demonstrate the effectiveness of the proposed framework over several graph-based semi-supervised learning methods.

Motivated by a recent experiment [arXiv:2205.05087], we investigate a possible mechanism that enhances superconductivity in hole-doped Bernal bilayer graphene due to a proximate WSe$_2$ monolayer. We show that the virtual tunneling between WSe$_2$ and Bernal bilayer graphene, which is known to induce Ising spin-orbit coupling, can generate an additional attraction between two holes, providing a potential explanation for enhancing superconductivity in Bernal bilayer graphene. Using microscopic interlayer tunneling, we derive the Ising spin-orbit coupling and the effective attraction as functions of the twist angle between Bernal bilayer graphene and the WSe$_2$ monolayer. Our theory provides an intuitive and physical explanation for the intertwined relation between Ising spin-orbit coupling and superconductivity enhancement, which should motivate future studies., Comment: 6+13 pages, 5+4 figures. Published version

Recently, graph neural networks (GNNs) have shown prominent performance in graph representation learning by leveraging knowledge from both graph structure and node features. However, most of them have two major limitations. First, GNNs can learn higher-order structural information by stacking more layers but can not deal with large depth due to the over-smoothing issue. Second, it is not easy to apply these methods on large graphs due to the expensive computation cost and high memory usage. In this paper, we present node-adaptive feature smoothing (NAFS), a simple non-parametric method that constructs node representations without parameter learning. NAFS first extracts the features of each node with its neighbors of different hops by feature smoothing, and then adaptively combines the smoothed features. Besides, the constructed node representation can further be enhanced by the ensemble of smoothed features extracted via different smoothing strategies. We conduct experiments on four benchmark datasets on two different application scenarios: node clustering and link prediction. Remarkably, NAFS with feature ensemble outperforms the state-of-the-art GNNs on these tasks and mitigates the aforementioned two limitations of most learning-based GNN counterparts., Comment: 17 pages, 8 figures