arXiv subjects

Jiarui Li

Publications and source records attributed to Jiarui Li.

At least 37 records · Page 2Linked to original sources

arXiv2025

TCLeaf-Net: a transformer-convolution framework with global-local attention for robust in-field lesion-level plant leaf disease detection

Timely and accurate detection of foliar diseases is vital for safeguarding crop growth and reducing yield losses. Yet, in real-field conditions, cluttered backgrounds, domain shifts, and limited lesion-level datasets hinder robust modeling. To address these challenges, we release Daylily-Leaf, a paired lesion-level dataset comprising 1,746 RGB images and 7,839 lesions captured under both ideal and in-field conditions, and propose TCLeaf-Net, a transformer-convolution hybrid detector optimized for real-field use. TCLeaf-Net is designed to tackle three major challenges. To mitigate interference from complex backgrounds, the transformer-convolution module (TCM) couples global context with locality-preserving convolution to suppress non-leaf regions. To reduce information loss during downsampling, the raw-scale feature recalling and sampling (RSFRS) block combines bilinear resampling and convolution to preserve fine spatial detail. To handle variations in lesion scale and feature shifts, the deformable alignment block with FPN (DFPN) employs offset-based alignment and multi-receptive-field perception to strengthen multi-scale fusion. Experimental results show that on the in-field split of the Daylily-Leaf dataset, TCLeaf-Net improves mAP@50 by 5.4 percentage points over the baseline model, reaching 78.2\%, while reducing computation by 7.5 GFLOPs and GPU memory usage by 8.7\%. Moreover, the model outperforms recent YOLO and RT-DETR series in both precision and recall, and demonstrates strong performance on the PlantDoc, Tomato-Leaf, and Rice-Leaf datasets, validating its robustness and generalizability to other plant disease detection scenarios.

cs.CV↗

arXiv2025

Collective Magnetic Excitations in a Photo-excited Electron-doped Cuprate Superconductor

Elucidating the microscopic behavior of cuprates under ultrafast photoexcitation offers critical insights into their highly correlated out-of-equilibrium states. Although quasiparticle dynamics have been investigated extensively, the behavior of collective magnetic excitations remains comparatively unexplored. Here, we use time-resolved resonant inelastic X-ray scattering (trRIXS) at the Cu $L_3$-edge to track the collective magnetic excitations (paramagnons) in an optimally electron-doped cuprate driven out-of-equilibrium by a femtosecond pump laser pulse. Upon pumping, we observed an anti-Stokes signal associated with paramagnon generation, which modifies the paramagnon dispersion near the zone center, although the bandwidth remained unchanged. Moreover, the spectral weight exhibits a momentum-dependent variation across the Brillouin zone. The light-driven boost of the paramagnon population and the resulting spectral-weight transfer could provide new leverage to manipulate the properties of cuprates.

cond-mat.mtrl-sci↗

arXiv2025

FICO: Finite-Horizon Closed-Loop Factorization for Unified Multi-Agent Path Finding

Multi-Agent Path Finding is a fundamental problem in robotics and AI, yet most existing formulations treat planning and execution separately and address variants of the problem in an ad hoc manner. This paper presents a system-level framework for MAPF that integrates planning and execution, generalizes across variants, and explicitly models uncertainties. At its core is the MAPF system, a formal model that casts MAPF as a control design problem encompassing classical and uncertainty-aware formulations. To solve it, we introduce Finite-Horizon Closed-Loop Factorization (FICO), a factorization-based algorithm inspired by receding-horizon control that exploits compositional structure for efficient closed-loop operation. FICO enables real-time responses -- commencing execution within milliseconds -- while scaling to thousands of agents and adapting seamlessly to execution-time uncertainties. Extensive case studies demonstrate that it reduces computation time by up to two orders of magnitude compared with open-loop baselines, while delivering significantly higher throughput under stochastic delays and agent arrivals. These results establish a principled foundation for analyzing and advancing MAPF through system-level modeling, factorization, and closed-loop design.

cs.RO↗

arXiv2025

Reducing the strain required for ambient-pressure superconductivity in bilayer nickelates

The remarkable discovery of high temperature superconductivity in bulk bilayer nickelates under high pressure has prompted the conjecture that epitaxial compressive strain might mimic essential aspects of hydrostatic pressure. The successful realization of superconductivity in films on SrLaAlO4 (001) (SLAO) supports this correspondence, yet it remains unclear whether the rich pressure-temperature phase diagram of bilayer nickelates can be systematically mapped (and studied at ambient pressure) as a function of epitaxial strain. To this end, experimental access near the elusive edge of the superconducting phase boundary would provide invaluable insight into the nature of the superconducting state and the ground state from which it emerges. It would also offer a benchmark for theoretical models. Here we report superconducting bilayer nickelates grown on LaAlO3 (001) (LAO), where the compressive strain required for ambient-pressure superconductivity is nearly halved to -1.2%. These films exhibit a superconducting onset above 10 K and reach zero resistance at 3 K, with normal-state transport properties differing from those of films grown on SLAO. Our results offer a new opportunity to probe emergent phenomena near the superconducting phase boundary in the strain-temperature phase diagram of bilayer nickelates.

cond-mat.supr-con↗

arXiv2025

Revealing the Potential of Learnable Perturbation Ensemble Forecast Model for Tropical Cyclone Prediction

Tropical cyclones (TCs) are highly destructive and inherently uncertain weather systems. Ensemble forecasting helps quantify these uncertainties, yet traditional systems are constrained by high computational costs and limited capability to fully represent atmospheric nonlinearity. FuXi-ENS introduces a learnable perturbation scheme for ensemble generation, representing a novel AI-based forecasting paradigm. Here, we systematically compare FuXi-ENS with ECMWF-ENS using all 90 global TCs in 2018, examining their performance in TC-related physical variables, track and intensity forecasts, and the associated dynamical and thermodynamical fields. FuXi-ENS demonstrates clear advantages in predicting TC-related physical variables, and achieves more accurate track forecasts with reduced ensemble spread, though it still underestimates intensity relative to observations. Further dynamical and thermodynamical analyses reveal that FuXi-ENS better captures large-scale circulation, with moisture turbulent energy more tightly concentrated around the TC warm core, whereas ECMWF-ENS exhibits a more dispersed distribution. These findings highlight the potential of learnable perturbations to improve TC forecasting skill and provide valuable insights for advancing AI-based ensemble prediction of extreme weather events that have significant societal impacts.

cs.LG↗

arXiv2025

GRIDAI: Generating and Repairing Intrusion Detection Rules via Collaboration among Multiple LLM-based Agents

Rule-based network intrusion detection systems play a crucial role in the real-time detection of Web attacks. However, most existing works primarily focus on automatically generating detection rules for new attacks, often overlooking the relationships between new attacks and existing rules, which leads to significant redundancy within the ever-expanding ruleset. To address this issue, we propose GRIDAI, a novel end-to-end framework for the automated Generation and Repair of Intrusion Detection rules through collaboration among multiple LLM-based agents. Unlike traditional methods, GRIDAI first assesses the nature of incoming attack samples. If the sample represents a new attack type, it is used to generate a new rule. Otherwise, the sample is identified as a variant of an attack already covered by an existing rule and used to repair the rule by updating the corresponding signature, thereby enhancing its generalization capability. Additionally, to mitigate syntactic and semantic errors in rules caused by LLM hallucinations, we incorporate a tool-based real-time validation mechanism and a representative attack sample maintained for each rule, enabling fully automated rule generation and repair. Comprehensive experiments were conducted on a public dataset containing seven types of attacks and a private dataset with 43 attack types. The results demonstrate that GRIDAI accurately identifies the relationships between new attack samples and existing rules, efficiently generates and repairs rules to handle new attacks and variants, and effectively mitigates the impact of LLM hallucinations.

cs.CR↗

arXiv2025

TCR-EML: Explainable Model Layers for TCR-pMHC Prediction

T cell receptor (TCR) recognition of peptide-MHC (pMHC) complexes is a central component of adaptive immunity, with implications for vaccine design, cancer immunotherapy, and autoimmune disease. While recent advances in machine learning have improved prediction of TCR-pMHC binding, the most effective approaches are black-box transformer models that cannot provide a rationale for predictions. Post-hoc explanation methods can provide insight with respect to the input but do not explicitly model biochemical mechanisms (e.g. known binding regions), as in TCR-pMHC binding. ``Explain-by-design'' models (i.e., with architectural components that can be examined directly after training) have been explored in other domains, but have not been used for TCR-pMHC binding. We propose explainable model layers (TCR-EML) that can be incorporated into protein-language model backbones for TCR-pMHC modeling. Our approach uses prototype layers for amino acid residue contacts drawn from known TCR-pMHC binding mechanisms, enabling high-quality explanations for predicted TCR-pMHC binding. Experiments of our proposed method on large-scale datasets demonstrate competitive predictive accuracy and generalization, and evaluation on the TCR-XAI benchmark demonstrates improved explainability compared with existing approaches.

q-bio.QM↗

arXiv2025

Origin of Spin Stripes in Bilayer Nickelate La$_3$Ni$_2$O$_7$

The bilayer nickelate La$_3$Ni$_2$O$_7$ has recently emerged as a high-temperature superconductor with unusual spin stripe order in its ambient pressure phase. We propose a microscopic Hamiltonian that faithfully reflects the crystalline symmetry of this system, with the primary aim of addressing its unconventional magnetism at ambient pressure. Using state-of-the-art density matrix renormalization group calculations, we show that $(\pi/2,\pi/2)$ spin stripe order arises in our model at sizable Hund's coupling $J_H$ from a hidden quasi-one-dimensionality and persists over a range of electron concentrations. In the more symmetric high-pressure regime, our model exhibits enhanced interlayer pairing tendencies when the interlayer antiferromagnetic coupling $J_{\bot}$ becomes sufficiently large. Our results provide a microscopic origin of the diagonal spin stripes and identify Hund's coupling $J_H$ and interlayer coupling $J_{\bot}$ as key ingredients governing magnetic order and pairing tendencies in La$_3$Ni$_2$O$_7$.

cond-mat.supr-con↗

arXiv2025

Rational Multi-Modal Transformers for TCR-pMHC Prediction

T cell receptor (TCR) recognition of peptide-MHC (pMHC) complexes is fundamental to adaptive immunity and central to the development of T cell-based immunotherapies. While transformer-based models have shown promise in predicting TCR-pMHC interactions, most lack a systematic and explainable approach to architecture design. We present an approach that uses a new post-hoc explainability method to inform the construction of a novel encoder-decoder transformer model. By identifying the most informative combinations of TCR and epitope sequence inputs, we optimize cross-attention strategies, incorporate auxiliary training objectives, and introduce a novel early-stopping criterion based on explanation quality. Our framework achieves state-of-the-art predictive performance while simultaneously improving explainability, robustness, and generalization. This work establishes a principled, explanation-driven strategy for modeling TCR-pMHC binding and offers mechanistic insights into sequence-level binding behavior through the lens of deep learning.

cs.CE↗

arXiv2025

$i$MIND: Insightful Multi-subject Invariant Neural Decoding

Decoding visual signals holds the tantalizing potential to unravel the complexities of cognition and perception. While recent studies have focused on reconstructing visual stimuli from neural recordings to bridge brain activity with visual imagery, existing methods offer limited insights into the underlying mechanisms of visual processing in the brain. To mitigate this gap, we present an \textit{i}nsightful \textbf{M}ulti-subject \textbf{I}nvariant \textbf{N}eural \textbf{D}ecoding ($i$MIND) model, which employs a novel dual-decoding framework--both biometric and semantic decoding--to offer neural interpretability in a data-driven manner and deepen our understanding of brain-based visual functionalities. Our $i$MIND model operates through three core steps: establishing a shared neural representation space across subjects using a ViT-based masked autoencoder, disentangling neural features into complementary subject-specific and object-specific components, and performing dual decoding to support both biometric and semantic classification tasks. Experimental results demonstrate that $i$MIND achieves state-of-the-art decoding performance with minimal scalability limitations. Furthermore, $i$MIND empirically generates voxel-object activation fingerprints that reveal object-specific neural patterns and enable investigation of subject-specific variations in attention to identical stimuli. These findings provide a foundation for more interpretable and generalizable subject-invariant neural decoding, advancing our understanding of the voxel semantic selectivity as well as the neural vision processing dynamics.

cs.CE↗

arXiv2025

Optical Measurement of Mass Density of Biological Samples

Mass density is a vital property for improved biophysical understanding of and within biological samples. It is increasingly attracting active investigation, but still lacks reliable, non-contact techniques to accurately characterize it in biological systems. Contrary to popular belief, refractive index information alone is insufficient to determine a sample's mass density, as we demonstrate here theoretically and experimentally. Instead, we measured the nonlinear gain of stimulated Brillouin scattering to provide additional information for mass density estimation. This all-optical method reduces the estimation error tenfold, offering a more accurate and universal technique for mass density measurements.

cond-mat.soft↗

arXiv2025

USAD: End-to-End Human Activity Recognition via Diffusion Model with Spatiotemporal Attention

The primary objective of human activity recognition (HAR) is to infer ongoing human actions from sensor data, a task that finds broad applications in health monitoring, safety protection, and sports analysis. Despite proliferating research, HAR still faces key challenges, including the scarcity of labeled samples for rare activities, insufficient extraction of high-level features, and suboptimal model performance on lightweight devices. To address these issues, this paper proposes a comprehensive optimization approach centered on multi-attention interaction mechanisms. First, an unsupervised, statistics-guided diffusion model is employed to perform data augmentation, thereby alleviating the problems of labeled data scarcity and severe class imbalance. Second, a multi-branch spatio-temporal interaction network is designed, which captures multi-scale features of sequential data through parallel residual branches with 3*3, 5*5, and 7*7 convolutional kernels. Simultaneously, temporal attention mechanisms are incorporated to identify critical time points, while spatial attention enhances inter-sensor interactions. A cross-branch feature fusion unit is further introduced to improve the overall feature representation capability. Finally, an adaptive multi-loss function fusion strategy is integrated, allowing for dynamic adjustment of loss weights and overall model optimization. Experimental results on three public datasets, WISDM, PAMAP2, and OPPORTUNITY, demonstrate that the proposed unsupervised data augmentation spatio-temporal attention diffusion network (USAD) achieves accuracies of 98.84%, 93.81%, and 80.92% respectively, significantly outperforming existing approaches. Furthermore, practical deployment on embedded devices verifies the efficiency and feasibility of the proposed method.

cs.CV↗

arXiv2025

Quantifying Cross-Attention Interaction in Transformers for Interpreting TCR-pMHC Binding

CD8+ "killer" T cells and CD4+ "helper" T cells play a central role in the adaptive immune system by recognizing antigens presented by Major Histocompatibility Complex (pMHC) molecules via T Cell Receptors (TCRs). Modeling binding between T cells and the pMHC complex is fundamental to understanding basic mechanisms of human immune response as well as in developing therapies. While transformer-based models such as TULIP have achieved impressive performance in this domain, their black-box nature precludes interpretability and thus limits a deeper mechanistic understanding of T cell response. Most existing post-hoc explainable AI (XAI) methods are confined to encoder-only, co-attention, or model-specific architectures and cannot handle encoder-decoder transformers used in TCR-pMHC modeling. To address this gap, we propose Quantifying Cross-Attention Interaction (QCAI), a new post-hoc method designed to interpret the cross-attention mechanisms in transformer decoders. Quantitative evaluation is a challenge for XAI methods; we have compiled TCR-XAI, a benchmark consisting of 274 experimentally determined TCR-pMHC structures to serve as ground truth for binding. Using these structures we compute physical distances between relevant amino acid residues in the TCR-pMHC interaction region and evaluate how well our method and others estimate the importance of residues in this region across the dataset. We show that QCAI achieves state-of-the-art performance on both interpretability and prediction accuracy under the TCR-XAI benchmark.

cs.CE↗

arXiv2025

Understanding How University Guidelines Address Privacy and Security Issues of Generative AI in Academic Settings

Generative artificial intelligence (GenAI) is transforming the educational landscape by augmenting learning paradigms. However, state-of-the-art GenAI systems driving this transformation are predominantly developed and controlled by a small number of private companies; there is little clarity about their data retention practices and limited user control over inputs and outputs. In the context of education, end-users lack the awareness of how to safely adopt GenAI in learning. This raises significant concerns, particularly when proprietary or personally identifiable educational information may be shared with external GenAI platforms. In response to these concerns, universities are developing their own usage guidelines and policies to balance innovation with academic integrity, privacy, and security. Our research seeks to understand these emerging guidelines, with a particular focus on the privacy and security implications of integrating GenAI tools into academic environments - an area that has received little attention to date. We conducted an in-depth qualitative analysis of GenAI-usage guidelines from 43 universities across 12 countries. Our findings reveal several key challenges, including barriers faced by universities in deploying privacy measures and adopting existing security frameworks. These insights lay the groundwork for designing more robust, privacy-aware GenAI guidelines for higher education.

cs.HC↗

arXiv2025

Multi-Agent Path Finding via Finite-Horizon Hierarchical Factorization

We present a novel algorithm for large-scale Multi-Agent Path Finding (MAPF) that enables fast, scalable planning in dynamic environments such as automated warehouses. Our approach introduces finite-horizon hierarchical factorization, a framework that plans one step at a time in a receding-horizon fashion. Robots first compute individual plans in parallel, and then dynamically group based on spatio-temporal conflicts and reachability. The framework accounts for conflict resolution, and for immediate execution and concurrent planning, significantly reducing response time compared to offline algorithms. Experimental results on benchmark maps demonstrate that our method achieves up to 60% reduction in time-to-first-action while consistently delivering high-quality solutions, outperforming state-of-the-art offline baselines across a range of problem sizes and planning horizons.

cs.RO↗

arXiv2025

Electronic structure of compressively strained thin film La$_2$PrNi$_2$O$_7$

The discovery of superconductivity in the bulk nickelates under high pressure is a major advance in physics. The recent observation of superconductivity at ambient pressure in compressively strained bilayer nickelate thin films has now enabled direct characterization of the superconducting phase through angle resolved photoemission spectroscopy (ARPES). Here we present an in-situ ARPES study of compressively strained La$_2$PrNi$_2$O$_7$ films grown by oxide molecular beam epitaxy, and the ozone treated counterparts with an onset T$_c$ of 40 K, supplemented with results from pulsed laser deposition films with similar T$_c$. We resolve a systematic strain-driven electronic band shift with respect to that of bulk crystals, in qualitative agreement with density functional theory (DFT) calculations. However, the strongly renormalized flat 3$d_{z2}$ band shifts a factor of 5-10 smaller than anticipated by DFT. Furthermore, it stays ~70 meV below the Fermi level, contradicting the expectation that superconductivity results from the high density of states of this band at the Fermi level. We also observed a non-trivial k$_z$ dispersion of the cuprate-like 3$d_{x2-y2}$ band. Combined with results from both X-ray diffraction and DFT, we suggest that the strained films are under ~5 GPa effective pressure, considerably larger than the na\"ive expectation from the DFT relaxed structure. Finally, the ~70 meV energy position is intriguingly close to the collective mode coupling more prominently seen in thin films, in the energy range of both oxygen related phonons and the maximum of the spin excitation spectrum.

cond-mat.supr-con↗

arXiv2025

Process Optimization and Deployment for Sensor-Based Human Activity Recognition Based on Deep Learning

Sensor-based human activity recognition is a key technology for many human-centered intelligent applications. However, this research is still in its infancy and faces many unresolved challenges. To address these, we propose a comprehensive optimization process approach centered on multi-attention interaction. We first utilize unsupervised statistical feature-guided diffusion models for highly adaptive data enhancement, and introduce a novel network architecture-Multi-branch Spatiotemporal Interaction Network, which uses multi-branch features at different levels to effectively Sequential ), which uses multi-branch features at different levels to effectively Sequential spatio-temporal interaction to enhance the ability to mine advanced latent features. In addition, we adopt a multi-loss function fusion strategy in the training phase to dynamically adjust the fusion weights between batches to optimize the training results. Finally, we also conducted actual deployment on embedded devices to extensively test the practical feasibility of the proposed method in existing work. We conduct extensive testing on three public datasets, including ablation studies, comparisons of related work, and embedded deployments.

eess.SP↗

arXiv2025

Doping dependence of 2-spinon excitations in the doped 1D cuprate Ba$_2$CuO$_{3+{\delta}}$

Recent photoemission experiments on the quasi-one-dimensional Ba-based cuprates suggest that doped holes experience an attractive potential not captured using the simple Hubbard model. This observation has garnered significant attention due to its potential relevance to Cooper pair formation in high-$T_c$ cuprate superconductors. To scrutinize this assertion, we examined signatures of such an attractive potential in doped 1D cuprates Ba$_2$CuO$_{3+\delta}$ by measuring the dispersion of the 2-spinon excitations using Cu $L_3$-edge resonant inelastic X-ray scattering (RIXS). Upon doping, the 2-spinon excitations appear to weaken, with a shift of the minimal position corresponding to the nesting vector of the Fermi points, $q_F$. Notably, we find that the energy scale of the 2-spinons near the Brillouin zone boundary is substantially softened compared to that predicted by the Hubbard model in one-dimension. Such a discrepancy implies missing ingredients, which lends support for the presence of an additional attractive potential between holes.

cond-mat.str-el↗