arXiv subjects

Jie Su

Publications and source records attributed to Jie Su.

At least 19 recordsLinked to original sources

arXiv2026

An adaptive parameter optimization method for astronomical image alignment using Bayesian optimization. I. A hierarchical search strategy for FWHM and SNR

The alignment and stacking of astronomical images are fundamental steps for detecting faint objects and performing high?precision astrometry. In traditional alignment workflows, the extraction of source lists is critically dependent on key parameters such as the Full Width at Half Maximum (FWHM) and the Signal-to-Noise Ratio (SNR) threshold. These parameters are often selected manually through an inefficient trial-error process that lacks objectivity and does not guarantee optimal results. We present an adaptive method for optimizing astronomical image alignment parameters based on Bayesian Optimization (BO). We frame the parameter search as an optimization problem, with an objective function designed to maximize the number of successfully matched source pairs. By employing a hierarchical search strategy, we perform an efficient global search for FWHM and SNR to automatically determine the optimal combination for a given observational dataset. Experimental results demonstrate that our method effectively handles image data with varying seeing conditions and back?ground noise levels. It rapidly converges to a robust set of alignment parameters, achieving sub-pixel accuracy and significantly improving the automation level and success rate of the alignment process. This work may provide a useful basis for developing large-scale, automated astronomical data processing pipelines

astro-ph.IM↗

arXiv2026

CockpitHAT: Dependency-Graph-Driven Hierarchical Attribution for Embodied Multi-Agent Cockpits

LLM multi-agent systems suffer from Correctness Collapse, where high task-level accuracy conceals severe process-level failures. This is especially hazardous in safety-critical embodied settings such as automotive cockpits, where lexically correct utterances may trigger dangerous physical operations. Existing attribution methods rely on text traces alone, missing dependency structure, multi-channel evidence, and safety-aware evaluation. We introduce CockpitHAT, a hierarchical attribution framework that replaces positional windows with dependency-distance thresholds from interaction DAGs, integrates multi-channel evidence via an embodied adapter, and applies a safety-uplift to high-risk failures during confidence-weighted analyst consensus. We further release CockpitBench, a benchmark of 212 annotated failure traces spanning dialogue, vehicle-state, environmental, and memory channels, each labeled with ISO 26262 ASIL severity via three-expert consensus. On the public Who&When benchmark, CockpitHAT achieves agent-level / step-exact accuracies of 77.9% / 37.8% on the Hand-Crafted split and 86.5% / 46.0% on the Algorithm-Generated split, surpassing the text-only SOTA ECHO by up to 17.6 / 16.7 points. On CockpitBench, it attains 78.3% agent-level and 38.2% step-exact accuracy. These results establish dependency-aware, multi-channel, risk-calibrated attribution as an effective paradigm for reliable failure diagnosis in real-world embodied LLM multi-agent systems.

cs.AI↗

arXiv2026

Divisive Normalization Shapes Low-Rank Slow Manifolds for Continuous Working Memory

The ability to robustly maintain and update continuous variables is a hallmark of working memory. While classical continuous attractor networks suffer from severe fine-tuning fragility, standard artificial recurrent neural networks (RNNs) like GRUs and LSTMs typically fail to stably learn continuous manifolds, instead shattering the state space into discretized point attractors. To bridge this gap, we draw inspiration from divisive normalization, a canonical neural computation widely observed across cortical circuits, and propose the Recurrent Divisive Normalization Network (RDNN), a minimal and algebraically isolated model of dynamic division. Through dynamical systems analysis on canonical working memory tasks, we demonstrate that this biophysical constraint allows the network to converge to robust, high-fidelity slow manifolds. Furthermore, we analyze the gradient dynamics of divisive normalization during Backpropagation Through Time (BPTT), showing that it introduces an activity-dependent local gradient scaling. This scaling dampens parameter updates in highly active regimes, which empirically aligns with a significant self-compression of the network's effective rank, confining the recurrent dynamics to a tight, low-dimensional subspace while avoiding the optimization pathologies associated with explicit low-rank factorization. Finally, ablations demonstrate that while subtractive inhibition can maintain static memories, divisive normalization is mathematically essential to prevent manifold shattering under time-varying inputs. Our findings identify divisive normalization not merely as a biological artifact, but as a critical computational mechanism for learning high-fidelity continuous representations.

q-bio.NC↗

arXiv2026

Mechanism-Dependent Descriptors Enable Predictive Design of Oxygen Capacity in Perovskite Oxides

Perovskite oxides can reversibly accommodate substantial changes in oxygen stoichiometry, making them attractive for clean-energy technologies including chemical looping and oxygen storage. Despite extensive efforts to optimize their redox properties, predictive descriptors capable of assessing oxygen capacity across diverse compositions remain under development. Here, we combine experiments and first-principles calculations to establish composition and oxygen-capacity relationships in the model perovskite series LnxSr1-xCoO3. We confirm that increasing Sr2+ content promotes the formation of high-valence Co4+, expanding the cationic redox reservoir available during oxygen release and thereby enhancing oxygen capacity. In this regime, oxygen-vacancy formation energy captures the observed trend because oxygen release is primarily compensated by Co4+/Co3+/Co2+ redox. Across the rare-earth series, however, oxygen capacity decreases from La to Lu despite progressively lower oxygen-vacancy formation energies. We reveal that this counterintuitive behavior originates from an alternative charge-compensation pathway, in which lattice oxygen is partially oxidized to O1- -like species during oxygen removal. Heavy rare-earth compositions (Tb-Lu) preferentially stabilize these oxygen-hole species through distinct local bonding environments, with charge compensation involving both oxidized lattice oxygen and reduced rare-earth and cobalt cations, thereby suppressing net oxygen release despite favorable vacancy thermodynamics. We further identify average metal-oxygen bond strength, quantified by integrated crystal orbital Hamilton population, as a physically meaningful descriptor for oxygen capacity when anionic redox becomes dominant.

cond-mat.mtrl-sci↗

arXiv2026

Self-organized MT Direction Maps Emerge from Spatiotemporal Contrastive Optimization

The spatial and functional organization of the primate visual cortex is a fundamental problem in neuroscience. While recent computational frameworks like the Topographic Deep Artificial Neural Network (TDANN) have successfully modeled spatial organization in the ventral stream, the computational origins of the dorsal stream's distinct topographies, such as direction-selective maps in the middle temporal (MT) area, remain largely unresolved. In this work, we present a spatiotemporal TDANN to investigate whether MT topography is governed by the same universal principles. By training a 3D ResNet on naturalistic videos via a Momentum Contrast (MoCo) self-supervised paradigm alongside a biologically inspired spatial loss, we demonstrate the spontaneous emergence of brain-like direction maps and topological pinwheel structures. Crucially, we reveal that MT tuning properties, characterized by strong direction selectivity paired with a residual axial component, arise from a strict optimization trade-off between task-driven discriminative pressure and spatial regularization. The model's representations quantitatively match in vivo macaque MT physiological baselines, including direction selectivity index, circular variance, and pinwheel density. These findings unify the computational origins of the ventral and dorsal streams, establishing a general mechanism for cortical self-organization.

q-bio.NC↗

arXiv2026

CosmicWeb-21cm array: A New Radio Observation Array Design for 21cm Cosmology

This paper presents the CosmicWeb-21cm array, a novel radio interferometer designed to overcome the key challenges in 21 cm cosmology. Its core innovations include: (1) a multi-scale nested geometry combining a hexagonal core with logarithmic spiral arms for excellent UV coverage and calibration robustness; (2) an intelligent non-uniform frequency sampling strategy that adapts resolution to foreground and signal characteristics, reducing data volume while preserving information; and (3) a machine-learning-enhanced, physics-informed processing pipeline that achieves 99.7\% foreground removal efficiency; (4) a dual-polarization crossed dipole integrated with a dielectric lens and cryogenically cooled LNA, achieving stable beam patterns and low noise temperature ($<35$ K) across 50-250 MHz. These co-designed advances enable high sensitivity mapping of the Epoch of Reionization, dark energy constraints and cosmic-web structure.

astro-ph.IM↗

arXiv2025

Space-time adaptive methods for parabolic evolution equations

We present a family of integral equation-based solvers for the heat equation, reaction-diffusion systems, the unsteady Stokes equation and the incompressible Navier-Stokes equations in two space dimensions. Our emphasis is on the development of methods that can efficiently follow complex solution features in space-time by refinement and coarsening at each time step on an adaptive quadtree. For simplicity, we focus on problems posed in a square domain with periodic boundary conditions. The performance and robustness of the methods are illustrated with several numerical examples.

math.NA↗

arXiv2025

A Unified Cortical Circuit Model with Divisive Normalization and Self-Excitation for Robust Representation and Memory Maintenance

Robust information representation and its persistent maintenance are fundamental for higher cognitive functions. Existing models employ distinct neural mechanisms to separately address noise-resistant processing or information maintenance, yet a unified framework integrating both operations remains elusive -- a critical gap in understanding cortical computation. Here, we introduce a recurrent neural circuit that combines divisive normalization with self-excitation to achieve both robust encoding and stable retention of normalized inputs. Mathematical analysis shows that, for suitable parameter regimes, the system forms a continuous attractor with two key properties: (1) input-proportional stabilization during stimulus presentation; and (2) self-sustained memory states persisting after stimulus offset. We demonstrate the model's versatility in two canonical tasks: (a) noise-robust encoding in a random-dot kinematogram (RDK) paradigm; and (b) approximate Bayesian belief updating in a probabilistic Wisconsin Card Sorting Test (pWCST). This work establishes a unified mathematical framework that bridges noise suppression, working memory, and approximate Bayesian inference within a single cortical microcircuit, offering fresh insights into the brain's canonical computation and guiding the design of biologically plausible artificial neural architectures.

q-bio.NC↗

arXiv2025

Breaking Peierls theorem in polyacetylene chains via topological design

Peierls theorem postulates that a one-dimensional (1D) metallic chain must undergo a metal-to-insulator transition via lattice distortion, resulting in bond length alternation (BLA) within the chain. The validity of this theorem has been repeatedly proven in practice, as evidenced by the absence of a metallic phase in low-dimensional atomic lattices and electronic crystals, including conjugated polymers, artificial 1D quantum nanowires, and anisotropic inorganic crystals. Overcoming this transition enables realizing long-sought organic quantum phases of matter, including 1D synthetic organic metals and even high-temperature organic superconductors. Herein, we demonstrate that the Peierls transition can be globally suppressed by employing lattice topology engineering of classic trans-polyacetylene chains connected to open-shell nanographene terminals. The appropriate topology connection enables an effective interplay between the zero-energy modes (ZMs) of terminal and the finite odd-membered polyacetylene (OPA) chains. This creates a critical topology-defined highest occupied molecular orbital (HOMO) that compensates for bond density variations, thereby suppressing BLA and reestablishing their quasi-1D metallic character. Moreover, it also causes the formation of an unconventional boundary-free resonance state, being delocalized over the entire chain with non-decaying spectral weight, distinguishing them from traditional solitons observed in polyacetylene. Our finding sets the stage for pioneering the suppression of material instability and the creation of synthetic organic quantum materials with unconventional quantum phases previously prohibited by the Peierls transition.

cond-mat.mes-hall↗

arXiv2025

Precise Asteroseismology of the High-amplitude Delta Scuti Star EH Librae, an AE UMa Analogue in the Hertzsprung Gap

A subclass of intermediate mass variables Delta Scuti stars, known as High-amplitude Delta Scuti (HADS) stars, exhibits pronounced radial pulsations with high amplitudes. The ground-based and space-based observations of the HADS star EH Lib are used to help making asteroseismological analysis of this pulsating star. Following the reduction of the light curves, the frequency analysis reveals the fundamental frequency as $f_0=11.3105$ c day$^{-1}$ and two more significant frequencies $f_1$ and $f_2$, in addition to the harmonics of $f_0$ and a linear combination. The period change rate is determined as $(1/P_0)(dP_0/dt)=(5.4\pm0.5)\times10^{-9}$ yr$^{-1}$ derived from an O-C diagram, which is constructed from 342 times of maximum light spanning over 70 years. Using these observational constraints, along with the metallicity reported in the literature, we construct theoretical models using the stellar evolution code MESA and calculate the theoretical frequencies of the eigen modes using the oscillation code GYRE. The appropriate models are selected by matching both $f_0$ and $(1/P_0)(dP_0/dt)$ within their respective uncertainties. The results indicate that the observed period change of EH Lib can be attributed to stellar evolutionary effects. The stellar parameters of EH Lib are derived as: the mass of $1.715\pm0.065$ M$_{\odot}$, the luminosity of log $(L/L_{\odot})=1.38\pm0.06$, and the age of $(1.14\pm0.13)\times10^{9}$ years. EH Lib is classified as a single-mode HADS star, locating currently in the Hertzsprung gap, with a helium core and a hydrogen-burning shell. This work expands the asteroseismological sample of HADS stars and establishes a foundation for future investigations into their commonalities and specific properties, thereby advancing our understanding of these variables.

astro-ph.SR↗

arXiv2025

Attention in Diffusion Model: A Survey

Attention mechanisms have become a foundational component in diffusion models, significantly influencing their capacity across a wide range of generative and discriminative tasks. This paper presents a comprehensive survey of attention within diffusion models, systematically analysing its roles, design patterns, and operations across different modalities and tasks. We propose a unified taxonomy that categorises attention-related modifications into parts according to the structural components they affect, offering a clear lens through which to understand their functional diversity. In addition to reviewing architectural innovations, we examine how attention mechanisms contribute to performance improvements in diverse applications. We also identify current limitations and underexplored areas, and outline potential directions for future research. Our study provides valuable insights into the evolving landscape of diffusion models, with a particular focus on the integrative and ubiquitous role of attention.

cs.LG↗

arXiv2025

MFH: A Multi-faceted Heuristic Algorithm Selection Approach for Software Verification

Currently, many verification algorithms are available to improve the reliability of software systems. Selecting the appropriate verification algorithm typically demands domain expertise and non-trivial manpower. An automated algorithm selector is thus desired. However, existing selectors, either depend on machine-learned strategies or manually designed heuristics, encounter issues such as reliance on high-quality samples with algorithm labels and limited scalability. In this paper, an automated algorithm selection approach, namely MFH, is proposed for software verification. Our approach leverages the heuristics that verifiers producing correct results typically implement certain appropriate algorithms, and the supported algorithms by these verifiers indirectly reflect which ones are potentially applicable. Specifically, MFH embeds the code property graph (CPG) of a semantic-preserving transformed program to enhance the robustness of the prediction model. Furthermore, our approach decomposes the selection task into the sub-tasks of predicting potentially applicable algorithms and matching the most appropriate verifiers. Additionally, MFH also introduces a feedback loop on incorrect predictions to improve model prediction accuracy. We evaluate MFH on 20 verifiers and over 15,000 verification tasks. Experimental results demonstrate the effectiveness of MFH, achieving a prediction accuracy of 91.47% even without ground truth algorithm labels provided during the training phase. Moreover, the prediction accuracy decreases only by 0.84% when introducing 10 new verifiers, indicating the strong scalability of the proposed approach.

cs.SE↗

arXiv2025

Uncovering the underlying mechanisms of phase transitions in chiral active particles

Chiral active matter widely exists in nature and emerges rich dynamical behaviors. Among these, chiral active particles (CAPs) with alignment effects show novel collective motions such as orderly rotating droplets and distinct phase transitions under different chirality degrees. However, the underlying dynamical and thermodynamical mechanisms of the phase transitions in the CAP system are not quite clear. Here, by combining the nonequilibrium physics with the coarse-grained mapping method, we quantified the potential landscape and the flux field to reflect global driving forces of the CAP system, characterizing the number and location of the steady states. Moreover, we revealed that mean flux and entropy production rate are respectively the dynamical and thermodynamical origins for the nonequilibrium phase transition, further providing a practical tool to confirm the continuity of the phase transition and the phase boundary. Our findings may inspire the design of experimental CAPs and present a new approach for investigating phase transition behaviors in other complex active systems.

cond-mat.soft↗

arXiv2025

Pulsation Properties of Blazhko and Non-Blazhko RRab Stars

In this study, we conduct a comparative analysis of the properties of Blazhko and non-Blazhko RRab stars. We identified 1054 non-Blazhko and 785 Blazhko RRab stars in the photometric data observed by K2 mission, which, combined with those 37 stars observed in the original Kepler field, constituted our study sample. Using the Fourier Decomposition method, we calculated the pulsation parameters, including phase differences and amplitude ratios, for these RRab stars, revealing significant discrepancies in the pulsation parameters between Blazhko and non-Blazhko RRab stars. However, distinguishing between Blazhko and Non-Blazhko RRab stars based on Fourier parameters remains challenging due to the significant overlap in their distributions. By cross-matching our sample with the LRS of LAMOST DR12, we identified 147 Blazhko and 111 non-Blazhko RRab stars, which exhibit similar metallicity distributions. Furthermore, cross-matching with Gaia DR3 data yielded 766 Blazhko and 950 non-Blazhko RRab stars, showing differences in color indices but not in absolute magnitudes. Our findings suggested the Blazhko effect is linked to pulsation parameters and colors, rather than metallicities or absolute magnitude.

astro-ph.SR↗

arXiv2025

Sustainable intensification of small-scale aquaculture systems depends on the local context and characteristics of producers

Aquaculture has been the fastest growing food production sector globally due to its potential to improve food security, stimulate economic growth, and reduce poverty. Its rapid development has been linked to sustainability challenges, many of which are still unresolved and poorly understood. Small-scale producers account for an increasing fraction of aquacultural output. At the same time, many of these producers experience poverty, food insecurity, and rely on unimproved production practices. We develop a stylized mathematical model to explore the effects of ecological, social, and economic factors on the dynamics of a small-scale pond aquaculture system. Using analytical and numerical methods, we explore the stability, asymptotic dynamics, and bifurcations of the model. Depending on the characteristics of the system, the model exhibits one of three distinct configurations: monostability with a global poverty trap in a nutrient-dominated or fish-dominated system; bistability with poverty trap and well-being attractors; multistability with poverty trap and two well-being attractors with different characteristics. The model results show that intensification can be sustainable only if it takes into account the local social-ecological context. In addition, the heterogeneity of small-scale aquaculture producers matters, as the effects of intensification can be unevenly distributed among them. Finally, more is not always better because too high nutrient input or productivity can lead to a suboptimal attractor or system collapse.

q-bio.PE↗

arXiv2024

Revealing Physical Mechanisms of Pattern Formation and Switching in Ecosystems via Nonequilibrium Landscape and Flux

Spatial patterns are widely observed in numerous nonequilibrium natural systems, often undergoing complex transitions and bifurcations, thereby exhibiting significant importance in many physical and biological systems such as embryonic development, ecosystem desertification, and turbulence. However, how spatial pattern formation emerges and how the spatial pattern switches are not fully understood. Here, we developed a landscape-flux field theory via the spatial mode expansion method to uncover the underlying physical mechanism of the pattern formation and switching. We identified the landscape and flux field as the driving force for spatial dynamics and applied this theory to the critical transitions between spatial vegetation patterns in semi-arid ecosystems, revealing that the nonequilibrium flux drives the switchings of spatial patterns. We uncovered how the pattern switching emerges through the optimal pathways and how fast this occurs via the speed of pattern switching. Furthermore, both the averaged flux and the entropy production rate exhibit peaks near pattern switching boundaries, revealing dynamical and thermodynamical origins for pattern transitions, and further offering early warning signals for anticipating spatial pattern switching. Our work thus reveals physical mechanisms on spatial pattern-switching in semi-arid ecosystems and, more generally, introduces a useful approach for quantifying spatial pattern switching in nonequilibrium systems, which further offers practical applications such as early warning signals for critical transitions of spatial patterns.

physics.bio-ph↗

arXiv2024

Mechanism of the Nonequilibrium Phase Transition in Self-Propelled Particles with Alignment

Self-propelled particles with alignment, displaying ordered collective motions such as swarming, can be investigated by the well-known Vicsek model. However, challenges still remain regarding the nature of the associated phase transition. Here, we use the landscape-flux approach combined with the coarse-grained mapping method to reveal the underlying mechanism of the continuous or discontinuous order-disorder nonequilibrium phase transition in Vicsek model systems featuring diverse noise characteristics. It is found that the nonequilibrium flux inside the landscape in the density-alignment degree phase space always rotates counterclockwise, and tends to delocalize or destabilize the point attractor states, providing the dynamical driving force for altering the landscape shape and the system state. Furthermore, the variations in the averaged flux and entropy production rate exhibit pronounced differences across various noise types. This not only helps to reveal the dynamical and thermodynamical mechanisms of the order-disorder transition but also offers a useful tool to recognize the continuity of the transition. Our findings present a novel perspective for exploring nonequilibrium phase transition behaviors and other collective motions in various complex systems.

cond-mat.stat-mech↗

arXiv2024

Thermalization and Mpemba-like patterns in effective temperature dynamics of strongly coupled dissipative quantum chaotic systems

Anomalous thermalization, particularly the crossings of temperature trajectories from different initial states termed Mpemba crossings (MPCs), have intrigued scientists for decades. While recent studies in quantum systems suggest that initial conditions play a decisive role in its emergence, they offer limited insight into MPCs in complex, highly nonequilibrium systems. In this study, we investigate temperature dynamics in the strongly coupled, quantum chaotic Sachdev-Ye-Kitaev (SYK) model, which is dual to the low-energy dynamics of 2D dilaton gravity. Our findings reveal a dynamically driven nonequilibrium mechanism underlying MPCs during rapid thermalization, with implications for gravitational systems. We explore quench dynamics in SYK systems under three conditions: coupling to a single SYK thermal bath, coupling to two thermal baths at different temperatures, and dissipative SYKs modeled by the Lindblad equation. We find that strong system-bath coupling induces oscillating effective temperatures and trajectory crossings in transient states due to nonequilibrium statistics, phenomena absent in quasi-static thermodynamics and Lindbladian SYKs. These MPCs highlight a unique feature of anomalous thermalization of strongly coupled quantum chaotic systems driven far from equilibrium. Besides, the results also provide qualitative insights into the nonequilibrium thermodynamics of black holes strongly interacting with their environment, such as primordial black holes in the early universe.

quant-ph↗