arXiv subjects

Jiarui Li

Publications and source records attributed to Jiarui Li.

At least 37 records · Page 2Linked to original sources

arXiv2026

APLSuite: An Integrated Suite for CD4+ T Cell Epitope Prediction via Antigen Processing Likelihood

Computational epitope prediction is a critical tool for exploring and understanding CD4+ T cell-mediated immune responses, a key aspect of adaptive immunity. While existing computational methods primarily focus on supervised learning approaches, they often overlook the essential role of antigen processing in determining binding specificity. To address this limitation, our group developed Antigen Processing Likelihood (APL), an algorithm that integrates crystallographic B-factor, solvent accessible surface area (SASA), hydrogen exchange protection factors (COREX), and sequence entropy. In this paper we introduce APLSuite, a comprehensive and lightweight software suite designed to streamline APL-based epitope prediction. APLSuite integrates distributed RESTful API services, a Python client for data aggregation and processing, a data science tool for efficient epitope computation, and a user-friendly graphical user interface for non-coding users. It provides a seamless and efficient pipeline for APL calculation and epitope prediction that can be finished in minutes with GPU-acceleration, which has not been implemented by existed tools. This flexible and extensible software suite is deployable on desktop and cloud environments, offering both guided and customizable workflows to meet diverse research needs in immunology research and immunotherapy development. (The project page for this work is available at: https://tulane-mettu-landry-lab.github.io/blogs/APLSuite/)

q-bio.BM↗

arXiv2026

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 $(π/2,π/2)$ 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↗

arXiv2026

Room-temperature multistage metastability in a moiré superstructure

Metastability is fundamental not only to phase ordering and transitions, but also to a broad range of modern technologies, from memory devices to metallic glasses. In condensed-matter physics, charge density waves (CDWs) offer versatile platforms for accessing metastable states due to their sensitivity to external stimuli. However, most metastable CDW states are stabilized only at low temperatures, limiting their practical utility. In this study, we report the observation of electrically driven, room-temperature, nonvolatile metastable states in the bulk form of EuTe$_4$, a recently discovered compound that hosts an innate moiré superlattice characterized by the stacking of incommensurate monolayer and bilayer CDWs. Systematic transport measurements reveal discrete resistivity plateaus and strong electric-field sensitivity, with a large number of metastable states readily induced across a wide temperature window within a giant hysteresis loop, making them well-suited for high-temperature, multi-bit memory applications. By integrating photoemission spectroscopy, diffraction, and in-situ transport measurements, we uncover that these metastable states do not stem from conventional mechanisms such as the emergence of new ordered phases or changes in incommensurate periodicity. Instead, they are characterized by a suppression of the original CDW amplitude and a reduction in correlation length, pointing to a unique electric-field-induced switching of out-of-plane CDW phases in the moiré superstructure. Our findings not only provide critical insights into metastable phenomena in moiré systems with stacked electronic orders but also establish EuTe$_4$ as a promising platform for developing room-temperature, multi-bit memory devices.

cond-mat.str-el↗

arXiv2026

Certificate-Driven Closed-Loop Multi-Agent Path Finding with Inheritable Factorization

Multi-agent coordination in automated warehouses and logistics is commonly modeled as the Multi-Agent Path Finding (MAPF) problem. Closed-loop MAPF algorithms improve scalability by planning only the next movement and replanning online, but this finite-horizon viewpoint can be shortsighted and makes it difficult to preserve global guarantees and exploit compositional structure. This issue is especially visible in Anytime Closed-Loop Conflict-Based Search (ACCBS), which applies Conflict-Based Search (CBS) over dynamically extended finite horizons but, under finite computational budgets, may terminate with short active prefixes in dense instances. We introduce certificate trajectories and their associated fleet budget as a general mechanism for filtering closed-loop updates. A certificate provides a conflict-free fallback plan and a monotone upper bound on the remaining cost; accepting only certificate-improving updates yields completeness. The same budget information induces a budget-limited factorization that enables global, inheritable decomposition across timesteps. Instantiating the framework on ACCBS yields Certificate-Driven Conflict-Based Search (CDCBS). Experiments on benchmark maps show that CDCBS achieves more consistent solution quality than ACCBS, particularly in dense settings, while the proposed factorization reduces effective group size.

cs.RO↗

arXiv2026

A superconducting half-dome in bilayer nickelates

Understanding how superconductivity emerges and collapses in correlated electron systems remains a central challenge in condensed matter physics. As a recently discovered member of the high temperature superconductor family, bilayer nickelates provide a new opportunity for examining this problem. Their pronounced sensitivity to oxygen stoichiometry, while posing challenges for stabilizing superconductivity, simultaneously offers an effective control parameter for tuning electronic phases. Here we report a superconducting half-dome in compressively strained bilayer nickelate thin films as a function of continuous tuning of oxygen stoichiometry. Starting from an optimally superconducting state, increasing oxygen stoichiometry gradually suppresses superconductivity toward a metallic phase, whereas decreasing oxygen stoichiometry drives a granular superconductor-to-insulator transition while leaving the superconducting onset intact. This half-dome structure can be understood to arise from the contrasting roles played by interstitial oxygen versus oxygen vacancies - namely the dominance of doping versus scattering. Notably, the half-dome emerges consistently across samples with different rare-earth combinations, with or without alkaline-earth doping, revealing a general feature of the bilayer nickelate phase diagram.

cond-mat.supr-con↗

arXiv2026

Why Does It Look There? Structured Explanations for Image Classification

Deep learning models achieve remarkable predictive performance, yet their black-box nature limits transparency and trustworthiness. Although numerous explainable artificial intelligence (XAI) methods have been proposed, they primarily provide saliency maps or concepts (i.e., unstructured interpretability). Existing approaches often rely on auxiliary models (\eg, GPT, CLIP) to describe model behavior, thereby compromising faithfulness to the original models. We propose Interpretability to Explainability (I2X), a framework that builds structured explanations directly from unstructured interpretability by quantifying progress at selected checkpoints during training using prototypes extracted from post-hoc XAI methods (e.g., GradCAM). I2X answers the question of "why does it look there" by providing a structured view of both intra- and inter-class decision making during training. Experiments on MNIST and CIFAR10 demonstrate effectiveness of I2X to reveal prototype-based inference process of various image classification models. Moreover, we demonstrate that I2X can be used to improve predictions across different model architectures and datasets: we can identify uncertain prototypes recognized by I2X and then use targeted perturbation of samples that allows fine-tuning to ultimately improve accuracy. Thus, I2X not only faithfully explains model behavior but also provides a practical approach to guide optimization toward desired targets.

cs.CV↗

arXiv2026

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↗

arXiv2026

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↗

arXiv2026

Biaxial Strain Control of Helimagnetism via Chemical Expansion in Thin Film SrFeO3

We demonstrate control of helimagnetic order in biaxially strained SrFeO3 thin films using neutron diffraction and resonant soft x-ray scattering. SrFeO3, a negative charge-transfer oxide, exhibits a complex magnetic phase diagram that includes multi-q spin structures. Tensile epitaxial strain produces a pronounced shortening of the helimagnetic ordering length and a tilting of the magnetic ordering vector. We interpret this behavior in terms of chemical expansion: lattice dilation under tensile strain lowers the energetic cost of oxygen vacancies, leading to an expanded unit cell that modifies Fe-O hybridization and enhances superexchange relative to double exchange. These results reveal how epitaxial strain can indirectly tune helimagnetism through defect-driven chemical expansion, highlighting the strong coupling between lattice, chemistry, and magnetic order in transition-metal oxides. Our findings establish chemical expansion as an effective mechanism for engineering complex magnetic textures in oxide thin films, with implications for spintronic, magnonic, and quantum information applications.

cond-mat.mtrl-sci↗

arXiv2026

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

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

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

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

Superconductivity and normal-state transport in compressively strained La$_2$PrNi$_2$O$_7$ thin films

The discovery of superconductivity under high pressure in Ruddlesden-Popper phases of bulk nickelates has sparked great interest in stabilizing ambient pressure superconductivity in thin-film form using epitaxial strain. Recently, signs of superconductivity have been observed in compressively strained bilayer nickelate thin films with an onset temperature exceeding 40 K, albeit with broad and two-step-like transitions. Here, we report intrinsic superconductivity and normal-state transport properties in compressively strained La$_2$PrNi$_2$O$_7$ thin films, achieved through a combination of isovalent Pr substitution, growth optimization, and precision ozone annealing. The superconducting onset occurs above 48 K, with zero resistance reached above 30 K, and the critical current density at 1.4 K is 100-fold larger than previous reports. The normal-state resistivity exhibits quadratic temperature dependence indicative of Fermi liquid behaviour, and other phenomenological similarities to transport in overdoped cuprates suggest parallels in their emergent properties.

cond-mat.supr-con↗

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↗