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Bilayer nickelate thin film superconductors discovered under ambient pressure enable vast possibilities for investigating electronic structures of the superconducting state. Here, we report angle-resolved photoemission spectroscopy (ARPES) measurements of heterointerfaces between 1, 2, and 3 unit-cell epitaxial La$_{2.85}$Pr$_{0.15}$Ni$_2$O$_7$ films and SrLaAlO$_4$ substates, through pure-oxygen in situ sample transportation. Evidence obtained using photons with distinct probing depths shows that conduction is localized primarily at the first unit cell near the interface. Scanning transmission electron microscopy (STEM), together with energy-dispersive X-ray spectroscopy (EDS) and electron energy loss spectroscopy (EELS), indicates that interfacial Sr diffusion and pronounced oxygen 2p hybridization gradient may collectively account for the interfacial confinement of conduction. Fermi surface maps reveal hole doping compared to non-superconducting ambient-pressure bulk crystals. Measurements of dispersive band structures suggest the contributions from both Ni $d_{x^2-y^2}$ and $d_{z^2}$ orbitals at the Fermi level. Density functional theory (DFT) + U calculations capture qualitative features of the ARPES results, consistent with a hole-doped scenario. These findings constrain theoretical models of the superconducting mechanism and suggest pathways for enhancing superconductivity in nickelates under ambient pressure., Comment: under review

Symmetry elegantly governs the fundamental properties and derived functionalities of condensed matter. For instance, realizing the superconducting diode effect (SDE) demands breaking space-inversion and time-reversal symmetries simultaneously. Although the SDE is widely observed in various platforms, its underlying mechanism remains debated, particularly regarding the role of vortices. Here, we systematically investigate the nonreciprocal transport in the chiral type-I superconductor NbGe2. Moreover, we induce type-II superconductivity with elevated superconducting critical temperature on the artificial surface by focused ion beam irradiation, enabling control over vortex dynamics in NbGe2 devices. Strikingly, we observe negligible diode efficiency (Q < 2%) at low magnetic fields, which rises significantly to Q ~ 50% at high magnetic fields, coinciding with an abrupt increase in vortex creep rate when the superconductivity of NbGe2 bulk is suppressed. These results unambiguously highlight the critical role of vortex dynamics in the SDE, in addition to the established symmetry rules.

Using a method based on normalized monthly variation rate, we studied resistivity data of seven observation stations before the events in the epicenter areas of two strong earthquakes. The relationship between variation of anisotropic apparent resistivity and the azimuth of the maximum principal stress is analyzed. The study shows that significant apparent resistivity variation occurs in the direction that is perpendicular to the azimuth of the maximum principal stress while only small fluctuation are recorded in the direction of the maximum principal stress. We surmise that the variation of anisotropic resistivity occurs in the late stage of the development of a strong earthquake, which can be observed in the epicenter area. If the density of the observation stations is increased and the direction of the observed resistivity is right, the epicenter of an earthquake location may be estimated by the observed resistivity anomaly.

Network control theory (NCT) has recently been utilized in neuroscience to facilitate our understanding of brain stimulation effects. A particularly useful branch of NCT is optimal control, which focuses on applying theoretical and computational principles of control theory to design optimal strategies to achieve specific goals in neural processes. However, most existing research focuses on optimally controlling brain network dynamics from the original state to a target state at a specific time point. In this paper, we present the first investigation of introducing optimal stochastic tracking control strategy to synchronize the dynamics of the brain network to a target dynamics rather than to a target state at a specific time point. We utilized fMRI data from healthy groups, and cases of stroke and post-stroke aphasia. For all participants, we utilized a gradient descent optimization method to estimate the parameters for the brain network dynamic system. We then utilized optimal stochastic tracking control techniques to drive original unhealthy dynamics by controlling a certain number of nodes to synchronize with target healthy dynamics. Results show that the energy associated with optimal stochastic tracking control is negatively correlated with the intrinsic average controllability of the brain network system, while the energy of the optimal state approaching control is significantly related to the target state value. For a 100-dimensional brain network system, controlling the five nodes with the lowest tracking energy can achieve relatively acceptable dynamics control effects. Our results suggest that stochastic tracking control is more aligned with the objective of brain stimulation interventions, and is closely related to the intrinsic characteristics of the brain network system, potentially representing a new direction for future brain network optimal control research., Comment: Supplementary materials can be found at: https://zjueducn-my.sharepoint.com/:b:/g/personal/dongkl_zju_edu_cn/EbG817wduDFIgqqS3zt2d4gB0OZXM9wt-v18Xr41zXS1Fg?e=XOGNwG

Zero-shot Text-To-Speech (TTS) synthesis shows great promise for personalized voice customization through voice cloning. However, current methods for achieving zero-shot TTS heavily rely on large model scales and extensive training datasets to ensure satisfactory performance and generalizability across various speakers. This raises concerns regarding both deployment costs and data security. In this paper, we present a lightweight and stable zero-shot TTS system. We introduce a novel TTS architecture designed to effectively model linguistic content and various speaker attributes from source speech and prompt speech, respectively. Furthermore, we present a two-stage self-distillation framework that constructs parallel data pairs for effectively disentangling linguistic content and speakers from the perspective of training data. Extensive experiments show that our system exhibits excellent performance and superior stability on the zero-shot TTS tasks. Moreover, it shows markedly superior computational efficiency, with RTFs of 0.13 and 0.012 on the CPU and GPU, respectively., Comment: 5 pages,4 figures

We introduce MiniMax-01 series, including MiniMax-Text-01 and MiniMax-VL-01, which are comparable to top-tier models while offering superior capabilities in processing longer contexts. The core lies in lightning attention and its efficient scaling. To maximize computational capacity, we integrate it with Mixture of Experts (MoE), creating a model with 32 experts and 456 billion total parameters, of which 45.9 billion are activated for each token. We develop an optimized parallel strategy and highly efficient computation-communication overlap techniques for MoE and lightning attention. This approach enables us to conduct efficient training and inference on models with hundreds of billions of parameters across contexts spanning millions of tokens. The context window of MiniMax-Text-01 can reach up to 1 million tokens during training and extrapolate to 4 million tokens during inference at an affordable cost. Our vision-language model, MiniMax-VL-01 is built through continued training with 512 billion vision-language tokens. Experiments on both standard and in-house benchmarks show that our models match the performance of state-of-the-art models like GPT-4o and Claude-3.5-Sonnet while offering 20-32 times longer context window. We publicly release MiniMax-01 at https://github.com/MiniMax-AI., Comment: A technical report from MiniMax. The authors are listed in alphabetical order. We open-sourced our MiniMax-01 at https://github.com/MiniMax-AI

We investigate the lateral displacements for ballistic electron beams in a two-dimensional electron gas modulated by metallic ferromagnetic (FM) stripes with parallel and antiparallel (AP) magnetization configurations. It is shown that the displacements are negative as well as positive, which can be controlled by adjusting the electric potential induced by the applied voltage and the magnetic field strength of FM stripes. Based on these phenomena, we propose an efficient way to realize a spin beam splitter, which can completely separate spin-up and spin-down electron beams in the AP configuration by their corresponding spatial positions., Comment: 5 pages, 4 figures

Reranker models aim to re-rank the passages based on the semantics similarity between the given query and passages, which have recently received more attention due to the wide application of the Retrieval-Augmented Generation. Most previous methods apply pointwise encoding, meaning that it can only encode the context of the query for each passage input into the model. However, for the reranker model, given a query, the comparison results between passages are even more important, which is called listwise encoding. Besides, previous models are trained using the cross-entropy loss function, which leads to issues of unsmooth gradient changes during training and low training efficiency. To address these issues, we propose a novel Listwise-encoded Contrastive text reRanker (ListConRanker). It can help the passage to be compared with other passages during the encoding process, and enhance the contrastive information between positive examples and between positive and negative examples. At the same time, we use the circle loss to train the model to increase the flexibility of gradients and solve the problem of training efficiency. Experimental results show that ListConRanker achieves state-of-the-art performance on the reranking benchmark of Chinese Massive Text Embedding Benchmark, including the cMedQA1.0, cMedQA2.0, MMarcoReranking, and T2Reranking datasets., Comment: 11 pages, 4 figures

We demonstrate a switchable electron shell structure in a bilayer graphene quantum dot by manipulating the trigonal warping effect upon electrical gating. Under a small perpendicular electric field, the lowest s-shell is sequentially filled with two spin-up and two spin-down electrons of opposite valleys. When increasing the electric field, an additional three-fold minivalley degeneracy is generated so that the s-shell can be filled with 12 electrons with the first/last 6 electrons having the same spin polarization. The switched spin filling sequence demonstrates the possibility of using the trigonal warping effect to electrically access and manipulate the spin degree of freedom in bilayer graphene., Comment: Phys. Rev. Lett. (2025) in press

The diffusion transformer (DiT) architecture has attracted significant attention in image generation, achieving better fidelity, performance, and diversity. However, most existing DiT - based image generation methods focus on global - aware synthesis, and regional prompt control has been less explored. In this paper, we propose a coarse - to - fine generation pipeline for regional prompt - following generation. Specifically, we first utilize the powerful large language model (LLM) to generate both high - level descriptions of the image (such as content, topic, and objects) and low - level descriptions (such as details and style). Then, we explore the influence of cross - attention layers at different depths. We find that deeper layers are always responsible for high - level content control, while shallow layers handle low - level content control. Various prompts are injected into the proposed regional cross - attention control for coarse - to - fine generation. By using the proposed pipeline, we enhance the controllability of DiT - based image generation. Extensive quantitative and qualitative results show that our pipeline can improve the performance of the generated images., Comment: Accepted by ICASSP 2025, Github: https://github.com/ZhenXiong-dl/ICASSP2025-RCAC

The deployment of neural networks in vehicle platforms and wearable Artificial Intelligence-of-Things (AIOT) scenarios has become a research area that has attracted much attention. With the continuous evolution of deep learning technology, many image classification models are committed to improving recognition accuracy, but this is often accompanied by problems such as large model resource usage, complex structure, and high power consumption, which makes it challenging to deploy on resource-constrained platforms. Herein, we propose an ultra-lightweight binary neural network (BNN) model designed for hardware deployment, and conduct image classification research based on the German Traffic Sign Recognition Benchmark (GTSRB) dataset. In addition, we also verify it on the Chinese Traffic Sign (CTS) and Belgian Traffic Sign (BTS) datasets. The proposed model shows excellent recognition performance with an accuracy of up to 97.64%, making it one of the best performing BNN models in the GTSRB dataset. Compared with the full-precision model, the accuracy loss is controlled within 1%, and the parameter storage overhead of the model is only 10% of that of the full-precision model. More importantly, our network model only relies on logical operations and low-bit width fixed-point addition and subtraction operations during the inference phase, which greatly simplifies the design complexity of the processing element (PE). Our research shows the great potential of BNN in the hardware deployment of computer vision models, especially in the field of computer vision tasks related to autonomous driving.

Whether the mass of supermassive black hole ($M_\mathrm{BH}$) is directly linked to the quasar radio luminosity remains a long-debated issue, and understanding the role of $M_\mathrm{BH}$ in the evolution of quasars is pivotal to unveiling the mechanism of AGN feedback. In this work, based on a two-component Bayesian model, we examine how $M_\mathrm{BH}$ affects the radio emission from quasars, separating the contributions from host galaxy star formation (SF) and AGN activity. By modelling the radio flux density distribution of Sloan Digital Sky Survey (SDSS) quasars from the LOFAR Two-metre Sky Survey Data Release 2, we find no correlation between $M_\mathrm{BH}$ and SF rate (SFR) at any mass for quasars at a given redshift and bolometric luminosity. The same holds for AGN activity across most $M_\mathrm{BH}$ values; however, quasars with the top 20\% most massive SMBHs are 2 to 3 times more likely to host strong radio jets than those with lower-mass SMBHs at similar redshift and luminosity. We suggest defining radio quasar populations by their AGN and SF contributions instead of radio loudness; our new definition unifies previously divergent observational results on the role of $M_\mathrm{BH}$ in quasar radio emissions. We further demonstrate that this radio enhancement in quasars with the 20\% most massive SMBHs affects only the $\sim5\%$ most radio bright quasars at a given redshift and bolometric luminosity. We discuss possible physical origins of this radio excess in the most massive and radio-bright quasar population, which remains an interest for future study., Comment: 18 pages, 13 figures, accepted for publication in MNRAS

We present a method to construct high-order polynomial approximate invariants (AI) for non-integrable Hamiltonian dynamical systems, and apply it to modern ring-based particle accelerators. Taking advantage of a special property of one-turn transformation maps in the form of a square matrix, AIs can be constructed order-by-order iteratively. Evaluating AI with simulation data, we observe that AI's fluctuation is actually a measure of chaos. Through minimizing the fluctuations with control knobs in accelerators, the stable region of long-term motions could be enlarged., Comment: 4 pages, 6 figures

The cancellation of high-frequency laser phase noise using feedforward techniques, as opposed to feedback methods, has achieved significant advancements in recent years. However, directly applying existing feedforward techniques to laser systems based on nonlinear conversion still faces substantial challenges. Here, we propose and demonstrate a feedforward scheme that suppresses phase noise in frequency-doubled light by utilizing phase noise information of its fundamental pump. This scheme is enabled by the fact that the phase jitter of the frequency-doubled light is simply twice that of the pump, except for a first-order low-pass filtering effect introduced by the SHG enhancement cavity. Testing this method on a 420-nm frequency-doubled laser system, we realize a 25-dB suppression of the servo noise bump near 1 MHz on the 420-nm light, and an average suppression of 30 dB for strong injected noise ranging from 100 kHz to 20 MHz. This scheme shows promising potential for applications requiring blue or ultraviolet light with minimal high-frequency phase noise, such as precision control of atoms and molecules.

The anomalous Hall effect (AHE) occurs in magnetic systems and also unexpectedly in non-magnetic materials adjacent to magnetic insulators via the heterointerface interactions. However, the AHE in heterostructures induced by magnetic proximity effect remains quite weak, restricting their practical device applications. Here, we report a large intrinsic AHE with a resistivity of 114 n{\Omega} cm at 5 K in noncoplanar magnetic heterostructures of Cr5Te6/Pt. This is the record-high AHE value among all the magnetic insulators/heavy metal heterostructures. A reversal of the AHE signal occurs due to the reconstruction of Berry curvature at the Fermi level, which is verified by the first-principles calculations. Topological spin textures at the interface are directly visualized via high-magnetic-field magnetic force microscopy, which accounts for the large AHE, as confirmed by the atomic simulations. These findings open a new avenue for exploring the large AHE in heterointerfaces and facilitate the potential applications in topological spintronic devices., Comment: 23 pages, 15 figures

Achieving high-fidelity 3D reconstruction from monocular video remains challenging due to the inherent limitations of traditional methods like Structure-from-Motion (SfM) and monocular SLAM in accurately capturing scene details. While differentiable rendering techniques such as Neural Radiance Fields (NeRF) address some of these challenges, their high computational costs make them unsuitable for real-time applications. Additionally, existing 3D Gaussian Splatting (3DGS) methods often focus on photometric consistency, neglecting geometric accuracy and failing to exploit SLAM's dynamic depth and pose updates for scene refinement. We propose a framework integrating dense SLAM with 3DGS for real-time, high-fidelity dense reconstruction. Our approach introduces SLAM-Informed Adaptive Densification, which dynamically updates and densifies the Gaussian model by leveraging dense point clouds from SLAM. Additionally, we incorporate Geometry-Guided Optimization, which combines edge-aware geometric constraints and photometric consistency to jointly optimize the appearance and geometry of the 3DGS scene representation, enabling detailed and accurate SLAM mapping reconstruction. Experiments on the Replica and TUM-RGBD datasets demonstrate the effectiveness of our approach, achieving state-of-the-art results among monocular systems. Specifically, our method achieves a PSNR of 36.864, SSIM of 0.985, and LPIPS of 0.040 on Replica, representing improvements of 10.7%, 6.4%, and 49.4%, respectively, over the previous SOTA. On TUM-RGBD, our method outperforms the closest baseline by 10.2%, 6.6%, and 34.7% in the same metrics. These results highlight the potential of our framework in bridging the gap between photometric and geometric dense 3D scene representations, paving the way for practical and efficient monocular dense reconstruction.

The proliferation of mobile devices and online interactions have been threatened by different cyberattacks, where phishing attacks and malicious Uniform Resource Locators (URLs) pose significant risks to user security. Traditional phishing URL detection methods primarily rely on URL string-based features, which attackers often manipulate to evade detection. To address these limitations, we propose a novel graph-based machine learning model for phishing URL detection, integrating both URL structure and network-level features such as IP addresses and authoritative name servers. Our approach leverages Loopy Belief Propagation (LBP) with an enhanced convergence strategy to enable effective message passing and stable classification in the presence of complex graph structures. Additionally, we introduce a refined edge potential mechanism that dynamically adapts based on entity similarity and label relationships to further improve classification accuracy. Comprehensive experiments on real-world datasets demonstrate our model's effectiveness by achieving F1 score of up to 98.77\%. This robust and reproducible method advances phishing detection capabilities, offering enhanced reliability and valuable insights in the field of cybersecurity.

The recent discovery of ambient-pressure superconductivity in thin-film bilayer nickelates opens new possibilities for investigating electronic structures in this new class of high-transition temperature $T_C$ superconductors. Here, we construct a realistic multi-orbital Hubbard model for the thin-film system, by integrating ab initio calculations with scanning transmission electron microscopy (STEM) measurements, which reveal a higher-symmetry lattice. The interaction parameters are calculated with the constrained random phase approximation (cRPA). Density functional theory (DFT) plus cluster dynamical mean-field theory (CDMFT) calculations, with cRPA calculated on-site Coulomb repulsive $U$ and experimentally measured electron filling $n$, quantitatively reproduces Fermi surfaces from angle-resolved photoemission spectroscopy (ARPES) experiments. The distinct Fermi surface topology from simple DFT+$U$ results features the indispensable role of correlation effects. Based upon the correlated electronic structures, A modified random-phase-approximation (RPA) approach yields a pronounced $s^{\pm}$-wave pairing instability, due to the strong spin fluctuations originated from Fermi surface nesting between bands with predominantly $d_{z^{2}}$ characters. Our findings highlight the quantitative effectiveness of the DFT+cRPA+CDMFT approach that precisely determines correlated electronic structure parameters without fine-tuning. The revealed intermediate correlation effect may explain the same order-of-magnitude onset $T_C$ observed both in pressured bulk and strained thin film bilayer nickelates., Comment: 4 figures and 3 tables in main manuscript. 9 figures and 2 tables in supplementary material

Deep Reinforcement Learning (DRL) has been extensively used to address portfolio optimization problems. The DRL agents acquire knowledge and make decisions through unsupervised interactions with their environment without requiring explicit knowledge of the joint dynamics of portfolio assets. Among these DRL algorithms, the combination of actor-critic algorithms and deep function approximators is the most widely used DRL algorithm. Here, we find that training the DRL agent using the actor-critic algorithm and deep function approximators may lead to scenarios where the improvement in the DRL agent's risk-adjusted profitability is not significant. We propose that such situations primarily arise from the following two problems: sparsity in positive reward and the curse of dimensionality. These limitations prevent DRL agents from comprehensively learning asset price change patterns in the training environment. As a result, the DRL agents cannot explore the dynamic portfolio optimization policy to improve the risk-adjusted profitability in the training process. To address these problems, we propose a novel multi-agent Hierarchical Deep Reinforcement Learning (HDRL) algorithmic framework in this research. Under this framework, the agents work together as a learning system for portfolio optimization. Specifically, by designing an auxiliary agent that works together with the executive agent for optimal policy exploration, the learning system can focus on exploring the policy with higher risk-adjusted return in the action space with positive return and low variance. In this way, we can overcome the issue of the curse of dimensionality and improve the training efficiency in the positive reward sparse environment.

A nonzero electric dipole moment of a tauon, $d_\tau$, signals CP violation and provides an important probe for new physics. We study methods to measure $d_\tau$ at low energy $e^+ e^-$ colliders through the processes $e^+e^- \to \gamma^*, \psi(2S) \to \tau^+\tau^-$ with $\tau^\pm$ decays into a charged hadron and a tau neutrino. We point out that, with measuring energies of the charged hadron, Im$(d_\tau)$ can be measured. On the other hand, selecting events of $\tau$ decays after traveling more than the detector resolution distance, Re$(d_\tau)$ can also be determined. We find that the precision at Super Tau-Charm Facility (STCF) running at the center energy of $m_{\psi (2S)}$ for 10 year data accumulation, the precision of Im$(d_\tau)$ and Re$(d_\tau)$ are found to be 3.5 and 11 in unit of $10^{-18}~e\,\text{cm}$, respectively. The sensitivity for $d_\tau$ measurement precision at the STCF can be reached its optimum at a central energy of $6.3~\text{GeV}$, achieving a precision of $1.3$ for Im$(d_\tau)$ and $2.9$ for Re$(d_\tau)$ in unit of $ 10^{-18}~e\,\text{cm}$., Comment: 4 pages, 2 figures

The widespread adoption of facial recognition (FR) models raises serious concerns about their potential misuse, motivating the development of anti-facial recognition (AFR) to protect user facial privacy. In this paper, we argue that the static FR strategy, predominantly adopted in prior literature for evaluating AFR efficacy, cannot faithfully characterize the actual capabilities of determined trackers who aim to track a specific target identity. In particular, we introduce \emph{\ourAttack}, a dynamic FR strategy where the model's gallery database is iteratively updated with newly recognized target identity images. Surprisingly, such a simple approach renders all the existing AFR protections ineffective. To mitigate the privacy threats posed by DynTracker, we advocate for explicitly promoting diversity in the AFR-protected images. We hypothesize that the lack of diversity is the primary cause of the failure of existing AFR methods. Specifically, we develop \emph{DivTrackee}, a novel method for crafting diverse AFR protections that builds upon a text-guided image generation framework and diversity-promoting adversarial losses. Through comprehensive experiments on various facial image benchmarks and feature extractors, we demonstrate DynTracker's strength in breaking existing AFR methods and the superiority of DivTrackee in preventing user facial images from being identified by dynamic FR strategies. We believe our work can act as an important initial step towards developing more effective AFR methods for protecting user facial privacy against determined trackers.

Iron telluride-selenium superconducting materials, known for their non-toxicity, ease of preparation, simple structure, and high upper critical fields, have attracted much research interest in practical application. In this work, we conducted electrical transport measurements, magneto-optical imaging, and AC magnetic susceptibility measurements on FeTe$_{0.5}$Se$_{0.5}$ superconducting long tapes fabricated via reel-to-reel pulsed laser deposition. Our transport measurements revealed a high critical current density that remains relatively stable even with increasing external magnetic fields, reaching over $1\times 10^5$ A/cm$^2$ at 8 K and 9 T. The calculated pinning force density indicates that normal point pinning is the primary mechanism in these tapes. The magneto-optical images demonstrated that the tapes show homogeneous superconductivity and uniform distribution of critical current density. The AC magnetic susceptibility measurements also confirmed their strong flux pinning nature of withstanding high magnetic field. Based on these characteristics, FeTe$_{0.5}$Se$_{0.5}$ superconducting tapes show promising prospects for applications under high magnetic fields., Comment: 20 pages, 14 figures

Globally distributed software development has been a mainstream paradigm in developing modern software systems. We have witnessed a fast-growing population of software developers from areas where English is not a native language in the last several decades. Given that English is still the de facto working language in most global software engineering teams, we need to gain more knowledge about the experiences of developers who are non-native English speakers. We conducted an empirical study to fill this research gap. In this study, we interviewed 27 Chinese developers in commercial software development and open source global software development teams and applied Bourdieu's capital-field-habitus framework in an abductive data analysis process. Our study reveals four types of capital (language, social, symbolic, and economic) involved in their experiences and examines the interrelations among them. We found that non-native speakers' insufficient language capital played an essential role in prohibiting them from accessing and accumulating other capital, thus reproducing the sustained and systematic disadvantaged positions of non-native English speakers in GSD teams. We further discussed the theoretical and practical implications of the study.

Using large language models (LLMs) to generate source code from natural language prompts is a popular and promising idea with a wide range of applications. One of its limitations is that the generated code can be faulty at times, often in a subtle way, despite being presented to the user as correct. In this paper, we explore ways in which formal methods can assist with increasing the quality of code generated by an LLM. Instead of emitting code in a target language directly, we propose that the user guides the LLM to first generate an opaque intermediate representation, in the verification-aware language Dafny, that can be automatically validated for correctness against agreed on specifications. The correct Dafny program is then compiled to the target language and returned to the user. All user-system interactions throughout the procedure occur via natural language; Dafny code is never exposed. We describe our current prototype and report on its performance on the HumanEval Python code generation benchmarks.

Weak antilocalization (WAL), an increase in the electrical conductivity at low temperatures associated with the suppression of electron localization due to quantum interference effects, is often observed in topological materials. In this study, we report the observation of WAL in topologically nontrivial metal PbTaSe$_{2}$ at low temperatures. In the pristine sample, we identified the presence of WAL, which is attributed to the topologically protected backscattering. In order to investigate the influence of disorder on the WAL, we successively introduced controlled amounts of disorder by H$^{+}$-irradiation. As disorder increases, the dip-like magnetoresistance caused by WAL changes to a linear magnetoresistance(MR), and eventually to a quadratic MR as the electronic system becomes highly localized. This research unveils the significance of disorder in shaping the quantum transport characteristics of topological materials., Comment: 7 pages. 4 figures

Causal inference has recently gained notable attention across various fields like biology, healthcare, and environmental science, especially within explainable artificial intelligence (xAI) systems, for uncovering the causal relationships among multiple variables and outcomes. Yet, it has not been fully recognized and deployed in the manufacturing systems. In this paper, we introduce an explainable, scalable, and flexible federated Bayesian learning framework, \texttt{xFBCI}, designed to explore causality through treatment effect estimation in distributed manufacturing systems. By leveraging federated Bayesian learning, we efficiently estimate posterior of local parameters to derive the propensity score for each client without accessing local private data. These scores are then used to estimate the treatment effect using propensity score matching (PSM). Through simulations on various datasets and a real-world Electrohydrodynamic (EHD) printing data, we demonstrate that our approach outperforms standard Bayesian causal inference methods and several state-of-the-art federated learning benchmarks., Comment: 26 pages

A simultaneously transmitting and reflecting reconfigurable intelligent surface (STAR-RIS) aided communication system is investigated. A robust joint beamforming design problem under the imperfect channel state information (CSI) is formulated to maximize the weighted sum of the Jain's fairness index and the normalized system sum rate. To solve this non-convex problem, an alternating optimization (AO) algorithm is proposed, which leverages the S-Procedure, successive convex approximation (SCA), and semidefinite relaxation (SDR). Simulation results demonstrate that with proposed algorithm: 1) various trade-offs between sum rate and user fairness can be achieved; 2) a larger trade-off region can be achieved by adopting STAR-RIS compared to conventional RIS; and 3) the performance degradation caused by imperfect CSI is less than 7% with our proposed robust beamforming approach., Comment: 5 pages, 4 figures

Many renewable energy resources are integrated into power systems via grid-following (GFL) inverters which rely on a phase-locked loop (PLL) for grid synchronization. During severe grid faults, GFL inverters are vulnerable to transient instability, often leading to disconnection from the grid. This paper aims to elucidate the interaction mechanisms and define the stability boundaries of systems of two inverters, including GFL, grid-forming (GFM), or grid-supporting (GSP) inverters. First, the generalized large-signal expression for the two-inverter system under various inverter combinations is derived, revealing that no energy function exists for systems containing GFL inverters. This implies that the traditional direct method cannot be applied to such systems. To overcome these challenges, a manifold method is employed to precisely determine the domain of attraction (DOA) of the system, and the transient stability margin is assessed by a new metric termed the critical clearing radius (CCR). A case study of the two-inverter system under various inverter combinations is conducted to explore large-signal interactions across different scenarios. Manifold analysis and simulation results reveal that GSP inverters using PLL for grid synchronization exhibit behavior similar to GFM inverters when the droop coefficients in the terminal voltage control loop (TVC) are sufficiently large. Compared to GFL inverters, GSP inverters incorporating a TVC significantly enhances the transient stability of other inverters. In the STATCOM case, the optimal placement of the STATCOM, realized by GSP or GFM inverters, is identified to be at the midpoint of a transmission line. All findings in this paper are validated through electromagnetic transient (EMT) simulations

Heart disease remains a significant threat to human health. As a non-invasive diagnostic tool, the electrocardiogram (ECG) is one of the most widely used methods for cardiac screening. However, the scarcity of high-quality ECG data, driven by privacy concerns and limited medical resources, creates a pressing need for effective ECG signal generation. Existing approaches for generating ECG signals typically rely on small training datasets, lack comprehensive evaluation frameworks, and overlook potential applications beyond data augmentation. To address these challenges, we propose DiffuSETS, a novel framework capable of generating ECG signals with high semantic alignment and fidelity. DiffuSETS accepts various modalities of clinical text reports and patient-specific information as inputs, enabling the creation of clinically meaningful ECG signals. Additionally, to address the lack of standardized evaluation in ECG generation, we introduce a comprehensive benchmarking methodology to assess the effectiveness of generative models in this domain. Our model achieve excellent results in tests, proving its superiority in the task of ECG generation. Furthermore, we showcase its potential to mitigate data scarcity while exploring novel applications in cardiology education and medical knowledge discovery, highlighting the broader impact of our work.

Symmetry is crucial for gaining insights into the fundamental properties of physical systems, bringing possibilities in studying exotic phenomena such as quantum phase transitions and ground state entanglement. Here, we experimentally simulate a highly controllable extended quantum Rabi model, capable of tuning into the ultra-strong or deep coupling regime, in a spin-motion-coupled trapped ion. We observe that the phonon driven by such a model with parity symmetry preserved (broken) would experience double (single) excitation in the ultra-strong coupling regime. Quantum phenomena such as strong ground state entanglement and quantum superposition in systems occur with parity symmetry, and these phenomena disappear following the symmetry breaking. We also find sensitive responses for the two-level system entropy and phonon Wigner function in the deep coupling regime, depending on the parameter across the symmetry transition point. This work offers the prospect of exploring symmetry-controlled quantum phenomena and their applications in high-precision quantum technologies., Comment: 11 pages, 6 figures

Abstract: The ability to arbitrarily and flexibly control the polarization of light, including both the state of polarization (SoP) and the degree of polarization (DoP), is highly important for quantum optics, polarization imaging, and coherent optical communications. Although metasurfaces have shown promise in polarization control, the few studies focusing on the DoP often lack flexibility in manipulation. Here, we propose a novel approach using a disordered metasurface to flexibly convert natural light into partially polarized light, enabling independent and flexible control over all Stokes parameters. The metasurface is composed of two types of meta-atoms, uniformly distributed with specific quantity ratios, decoupling the design parameters in the process of polarization control, and allowing a one-to-one correspondence between metasurface and polarization spaces. The azimuthal and elevation angles of the SoP on the Poincar\'e sphere are independently controlled by the meta-atom rotation and size, while the DoP is governed by the quantity ratio. A developed algorithm determines the disordered metasurface arrangement, with theoretical calculations showing an average error of less than 3{\deg} for both the azimuthal and elevation angles and a control accuracy of \pm 0.05 for the DoP., Comment: 31 pages, 9 figures

With the rapid advancements in large language model (LLM) technology and the emergence of bioinformatics-specific language models (BioLMs), there is a growing need for a comprehensive analysis of the current landscape, computational characteristics, and diverse applications. This survey aims to address this need by providing a thorough review of BioLMs, focusing on their evolution, classification, and distinguishing features, alongside a detailed examination of training methodologies, datasets, and evaluation frameworks. We explore the wide-ranging applications of BioLMs in critical areas such as disease diagnosis, drug discovery, and vaccine development, highlighting their impact and transformative potential in bioinformatics. We identify key challenges and limitations inherent in BioLMs, including data privacy and security concerns, interpretability issues, biases in training data and model outputs, and domain adaptation complexities. Finally, we highlight emerging trends and future directions, offering valuable insights to guide researchers and clinicians toward advancing BioLMs for increasingly sophisticated biological and clinical applications., Comment: 64 pages, 1 figure

This paper introduce LongViTU, a large-scale (~121k QA pairs, ~900h videos), automatically generated dataset for long-form video understanding. We developed a systematic approach that organizes videos into a hierarchical tree structure and incorporates self-revision mechanisms to ensure high-quality QA pairs. Each QA pair in LongViTU features: 1) long-term context (average certificate length of 4.6 minutes); 2) rich knowledge and condensed reasoning (commonsense, causality, planning, etc.); and 3) explicit timestamp labels for relevant events. LongViTU also serves as a benchmark for instruction following in long-form and streaming video understanding. We evaluate the open-source state-of-the-art long video understanding model, LongVU, and the commercial model, Gemini-1.5-Pro, on our benchmark. They achieve GPT-4 scores of 49.9 and 52.3, respectively, underscoring the substantial challenge posed by our benchmark. Further supervised fine-tuning (SFT) on LongVU led to performance improvements of 12.0% on our benchmark, 2.2% on the in-distribution (ID) benchmark EgoSchema, 1.0%, 2.2% and 1.2% on the out-of-distribution (OOD) benchmarks VideoMME (Long), WorldQA and OpenEQA, respectively. These outcomes demonstrate LongViTU's high data quality and robust OOD generalizability.

Few-shot class-incremental learning (FSCIL) involves learning new classes from limited data while retaining prior knowledge, and often results in catastrophic forgetting. Existing methods either freeze backbone networks to preserve knowledge, which limits adaptability, or rely on additional modules or prompts, introducing inference overhead. To this end, we propose Continuous Knowledge-Preserving Decomposition for FSCIL (CKPD-FSCIL), a framework that decomposes a model's weights into two parts: one that compacts existing knowledge (knowledge-sensitive components) and another that carries redundant capacity to accommodate new abilities (redundant-capacity components). The decomposition is guided by a covariance matrix from replay samples, ensuring principal components align with classification abilities. During adaptation, we freeze the knowledge-sensitive components and only adapt the redundant-capacity components, fostering plasticity while minimizing interference without changing the architecture or increasing overhead. Additionally, CKPD introduces an adaptive layer selection strategy to identify layers with redundant capacity, dynamically allocating adapters. Experiments on multiple benchmarks show that CKPD-FSCIL outperforms state-of-the-art methods., Comment: Code: https://github.com/xiaojieli0903/CKPD-FSCIL

Studying the immunity of topological superconductors against non-local disorder is one of the key issues in both fundamental researches and potential applications. Here, we demonstrate that the non-Hermiticity can enhance the robustness of topological edge states against non-local disorder. To illustrate that, we consider a one-dimensional (1D) generalized Kitaev model with the asymmetric hopping in the presence of disorder. It is shown that the region supporting Majorana zero modes (MZMs) against non-local disorder will be enlarged by the non-Hermiticity. Through both the numerical and analytical analyses, we show that non-Hermiticity can stabilize the topological superconducting (SC) phase against higher disorder strength. Our studies would offer new insights into the interplay between non-Hermiticity and topology.

In this paper, a multiscale boundary condition for the discrete unified gas kinetic scheme (DUGKS) is developed for gas flows in all flow regimes. Based on the discrete Maxwell boundary condition (DMBC), this study addresses the limitations of the original DMBC used in DUGKS. Specifically, it is found that the DMBC produces spurious velocity slip and temperature jump, which are proportional to the mesh size and the momentum accommodation coefficient. The proposed multiscale DMBC is implemented by ensuring that the reflected original distribution function excludes collision effects. Theoretical analyses and numerous numerical tests show that the multiscale DMBC can achieve exactly the non-slip and non-jump conditions in the continuum limit and accurately captures non-equilibrium phenomena across a wide range of Knudsen numbers. The results demonstrate that the DUGKS with the multiscale DMBC can work properly for wall boundary conditions in all flow regimes with a fixed discretization in both space and time, without limitations on the thickness of the Knudsen layer and relaxation time., Comment: 38 pages, 16 figures

The recent discovery of superconductivity in the bilayer Ruddlesden-Popper nickelate La3Ni2O7 under high pressure has generated much interest in the superconducting pairing mechanism of nickelates. Various theoretical approaches have been applied to the study of superconductivity in La3Ni2O7, but lead to a number of contradicting results. We argue that different superconducting states in La3Ni2O7 are in close competition and at the same time particularly sensitive to the choice of interaction parameters as well as changes of the electronic structure through pressure. Our study uses a multi-orbital Hubbard model, incorporating all Ni 3d and O 2p states. We analyze the superconducting pairing mechanism of La3Ni2O7 within the random phase approximation and find a transition between d-wave and sign-changing s-wave pairing states as a function of pressure and interaction parameters, which is driven by spin-fluctuations with different wave vectors. Our work paves the way to understanding seemingly contradictory theoretical results within a unified framework.

Quantum digital signatures (QDS), based on the principles of quantum mechanics, provide information-theoretic security, ensuring the integrity, authenticity, and non-repudiation of data transmission. With present QDS protocols, measurement-device-independent QDS (MDI-QDS) can resist all attacks on detections, yet it suffers from finite-size effect. In this work, we present and compare three parameter estimation models for finite-size analysis of two-decoy MDI-QDS. The first model is a commonly used model in previous schemes, and we propose two new models to improve the performance. Subsequently, we perform numerical simulations to evaluate the performance of the three models. The results demonstrate that the proposed methods are less affected by finite-size effect, thereby effectively enhancing the signature rate. This work contributes to the practical development of QDS.

We search for the leptonic decay $D^+\to e^+\nu_{e}$ using an $e^+e^-$ collision data sample with an integrated luminosity of 20.3~fb$^{-1}$ collected with the BESIII detector at the center-of-mass energy of 3.773~GeV. No significant signal is observed and an upper limit on the branching fraction of $D^+\to e^+\nu_{e}$ is set as $9.7 \times 10^{-7}$, at the 90\% confidence level. Our upper limit is an order of magnitude smaller than the previous limit for this decay mode.

The calculation of the volume enclosed by curved surfaces discretized into triangular elements, and a cube is of great importance in different domains, such as computer graphics and multiphase flow simulations. We propose a robust algorithm, the Front2VOF (F2V) algorithm, to address this problem. The F2V algorithm consists of two main steps. First, it identifies the polygons within the cube by segmenting the triangular elements on the surface, retaining only the portions inside the cube boundaries. Second, it computes the volume enclosed by these polygons in combination with the cube faces. To validate the algorithm's accuracy and robustness, we tested it using a range of synthetic configurations with known analytical solutions.

Noise is a major obstacle in current quantum computing, and Machine Learning for Quantum Error Mitigation (ML-QEM) promises to address this challenge, enhancing computational accuracy while reducing the sampling overheads of standard QEM methods. Yet, existing models lack physical interpretability and rely heavily on extensive datasets, hindering their scalability in large-scale quantum circuits. To tackle these issues, we introduce the Neural Noise Accumulation Surrogate (NNAS), a physics-inspired neural network for ML-QEM that incorporates the structural characteristics of quantum noise accumulation within multi-layer circuits, endowing the model with physical interpretability. Experimental results demonstrate that NNAS outperforms current methods across a spectrum of metrics, including error mitigation capability, quantum resource consumption, and training dataset size. Notably, for deeper circuits where QEM methods typically struggle, NNAS achieves a remarkable reduction of over half in errors. NNAS also demands substantially fewer training data, reducing dataset reliance by at least an order of magnitude, due to its ability to rapidly capture noise accumulation patterns across circuit layers. This work pioneers the integration of quantum process-derived structural characteristics into neural network architectures, broadly enhancing QEM's performance and applicability, and establishes an integrative paradigm that extends to various quantum-inspired neural network architectures., Comment: Please feel free to contact He-Liang Huang or Haoyi Zhou for any suggestions or comments

We study the electromagnetic Dalitz decay $J/\psi \to e^+e^- \pi^0$ using $(10087 \pm 44) \times 10^6$ $J/\psi$ events collected by the \bes detector. The di-electron-invariant-mass dependent transition form factor of this decay is explored for the first time. A significant resonant structure corresponding to the $\rho/\omega$ resonance is observed, which cannot be described by existing theoretical models, due to contributions from the isospin-conserving $J/\psi \to \rho \pi^0$ and isospin-volating $J/\psi \to \omega \pi^0$ decays. The observed $\rho$--$\omega$ interference is consistent with that of the pion form factor but features a relatively narrow $\rho$ peak. By taking into account the contribution of this resonant structure, the branching fraction of $J/\psi \to e^+e^- \pi^0$ in the full $e^+e^-$ invariant mass spectrum range is also measured for the first time to be $(8.06 \pm 0.31 (\rm{stat}) \pm 0.38 (\rm{syst}))\times 10^{-7}$, which is two times larger than the prediction of the Vector Meson Dominance model due to the observed resonant contribution of $\rho/\omega$ resonances., Comment: 9 pages, 4 figures, Submitted to Phys. Rev. Lett

With the prevalence of Multimodal Large Language Models(MLLMs), autonomous driving has encountered new opportunities and challenges. In particular, multi-modal video understanding is critical to interactively analyze what will happen in the procedure of autonomous driving. However, videos in such a dynamical scene that often contains complex spatial-temporal movements, which restricts the generalization capacity of the existing MLLMs in this field. To bridge the gap, we propose a novel Hierarchical Mamba Adaptation (H-MBA) framework to fit the complicated motion changes in autonomous driving videos. Specifically, our H-MBA consists of two distinct modules, including Context Mamba (C-Mamba) and Query Mamba (Q-Mamba). First, C-Mamba contains various types of structure state space models, which can effectively capture multi-granularity video context for different temporal resolutions. Second, Q-Mamba flexibly transforms the current frame as the learnable query, and attentively selects multi-granularity video context into query. Consequently, it can adaptively integrate all the video contexts of multi-scale temporal resolutions to enhance video understanding. Via a plug-and-play paradigm in MLLMs, our H-MBA shows the remarkable performance on multi-modal video tasks in autonomous driving, e.g., for risk object detection, it outperforms the previous SOTA method with 5.5% mIoU improvement., Comment: 7 pages, 4 figures

ChartQA presents significant challenges due to the complex distribution of chart elements and the implicit patterns embedded within the underlying data. In this chapter, we have developed a joint multimodal scene graph for charts, explicitly representing the relationships between chart elements and their associated patterns. Our proposed multimodal scene graph consists of two components: a visual graph and a textual graph, each designed to capture the structural and semantic information within the chart. To unify representations across these different modalities, we introduce a multimodal graph contrastive learning approach that learns unified representations by maximizing similarity between nodes representing the same object across multimodal graphs. The learned graph representations can be seamlessly incorporated into a transformer decoder as a soft prompt. Additionally, given the growing need for Multimodal Large Language Models (MLLMs) in zero-shot scenarios, we have designed Chain-of-Thought (CoT) prompts for MLLMs to reduce hallucinations. We tested both methods on public benchmarks such as ChartQA, OpenCQA, and ChartX, demonstrating improved performance and validating the effectiveness of our proposed methods.

In daily domestic settings, frequently used objects like cups often have unfixed positions and multiple instances within the same category, and their carriers frequently change as well. As a result, it becomes challenging for a robot to efficiently navigate to a specific instance. To tackle this challenge, the robot must capture and update scene changes and plans continuously. However, current object navigation approaches primarily focus on the semantic level and lack the ability to dynamically update scene representation. In contrast, this paper captures the relationships between frequently used objects and their static carriers. It constructs an open-vocabulary Carrier-Relationship Scene Graph (CRSG) and updates the carrying status during robot navigation to reflect the dynamic changes of the scene. Based on the CRSG, we further propose an instance navigation strategy that models the navigation process as a Markov Decision Process. At each step, decisions are informed by the Large Language Model's commonsense knowledge and visual-language feature similarity. We designed a series of long-sequence navigation tasks for frequently used everyday items in the Habitat simulator. The results demonstrate that by updating the CRSG, the robot can efficiently navigate to moved targets. Additionally, we deployed our algorithm on a real robot and validated its practical effectiveness. The project page can be found here: https://OpenIN-nav.github.io., Comment: arXiv admin note: substantial text overlap with arXiv:2409.18743

In this manuscript, we consider the problem of learning a flow or diffusion-based generative model parametrized by a two-layer auto-encoder, trained with online stochastic gradient descent, on a high-dimensional target density with an underlying low-dimensional manifold structure. We derive a tight asymptotic characterization of low-dimensional projections of the distribution of samples generated by the learned model, ascertaining in particular its dependence on the number of training samples. Building on this analysis, we discuss how mode collapse can arise, and lead to model collapse when the generative model is re-trained on generated synthetic data.

Traditional equation-driven hydrological models often struggle to accurately predict streamflow in challenging regional Earth systems like the Tibetan Plateau, while hybrid and existing algorithm-driven models face difficulties in interpreting hydrological behaviors. This work introduces HydroTrace, an algorithm-driven, data-agnostic model that substantially outperforms these approaches, achieving a Nash-Sutcliffe Efficiency of 98% and demonstrating strong generalization on unseen data. Moreover, HydroTrace leverages advanced attention mechanisms to capture spatial-temporal variations and feature-specific impacts, enabling the quantification and spatial resolution of streamflow partitioning as well as the interpretation of hydrological behaviors such as glacier-snow-streamflow interactions and monsoon dynamics. Additionally, a large language model (LLM)-based application allows users to easily understand and apply HydroTrace's insights for practical purposes. These advancements position HydroTrace as a transformative tool in hydrological and broader Earth system modeling, offering enhanced prediction accuracy and interpretability.

Version control systems are commonly used to manage open-source software, in which each commit may introduce new vulnerabilities or fix existing ones. Researchers have developed various tools for detecting vulnerabilities in code commits, but their performance is limited by factors such as neglecting descriptive data and challenges in accurately identifying vulnerability introductions. To overcome these limitations, we propose CommitShield, which combines the code analysis capabilities of static analysis tools with the natural language and code understanding capabilities of large language models (LLMs) to enhance the accuracy of vulnerability introduction and fix detection by generating precise descriptions and obtaining rich patch contexts. We evaluate CommitShield using the newly constructed vulnerability repair dataset, CommitVulFix, and a cleaned vulnerability introduction dataset. Experimental results indicate that CommitShield improves recall by 76%-87% over state-of-the-art methods in the vulnerability fix detection task, and its F1-score improves by 15%-27% in the vulnerability introduction detection task.

It is envisioned that the sixth generation (6G) and beyond 6G (B6G) wireless communication networks will enable global coverage in space, air, ground, and sea. In this case, both base stations and users can be mobile and will tend to move continuously in three-dimensional (3D) space. Therefore, obtaining channel state information (CSI) in 3D continuous-space is crucial for the design and performance evaluation of future 6G and B6G wireless systems. On the other hand, new 6G technologies such as integrated sensing and communications (ISAC) will also require prior knowledge of CSI in 3D continuous-space. In this paper, a 3D continuous-space electromagnetic channel model is proposed for tri-polarized multi-user communications, taking into account scatterers and spherical wavefronts. Scattered fields are calculated using the method of moments (MoM) with high accuracy. Spherical wave functions are utilized to decompose the dyadic Green's functions that connect the transmitted source currents and the received electric fields. Simulation results demonstrate that transmit power, apertures, scatterers, and sample intervals have significant impacts on statistical properties and channel capacities, providing insights into the performance of continuous-space electromagnetic channel models and the design of future wireless systems.

Urban segregation refers to the physical and social division of people, often driving inequalities within cities and exacerbating socioeconomic and racial tensions. While most studies focus on residential spaces, they often neglect segregation across "activity spaces" where people work, socialize, and engage in leisure. Human mobility data offers new opportunities to analyze broader segregation patterns, encompassing both residential and activity spaces, but challenges existing methods in capturing the complexity and local nuances of urban segregation. This work introduces InclusiViz, a novel visual analytics system for multi-level analysis of urban segregation, facilitating the development of targeted, data-driven interventions. Specifically, we developed a deep learning model to predict mobility patterns across social groups using environmental features, augmented with explainable AI to reveal how these features influence segregation. The system integrates innovative visualizations that allow users to explore segregation patterns from broad overviews to fine-grained detail and evaluate urban planning interventions with real-time feedback. We conducted a quantitative evaluation to validate the model's accuracy and efficiency. Two case studies and expert interviews with social scientists and urban analysts demonstrated the system's effectiveness, highlighting its potential to guide urban planning toward more inclusive cities., Comment: Accepted to IEEE TVCG (Journal Track of PacificVis 2025)