arXiv subjects

Yiwen Li

Publications and source records attributed to Yiwen Li.

At least 19 recordsLinked to original sources

arXiv2026

SEPO: Evidence-Grounded Prompt Optimization via Structural Editing

Existing API-only prompt optimisers are often described as interpretable, but in practice, this usually means only post-hoc inspectability: each iteration still rewrites the prompt as one opaque string, leaving a trace of full-prompt diffs rather than localisable, machine-readable edits. This paper introduces SEPO (Structural, Evidence-grounded Prompt Optimization), a multi-trajectory prompt optimiser centred on edit-effect lineage feedback. Rather than treating each iteration as an isolated whole-prompt rewrite, SEPO locally edits stable, typed units in a two-layer prompt schema, links the target and realised structural operations of each edit to the examples it newly fixes or breaks, and carries this edit-effect record forward to guide later architect calls on the same search branch. This makes prompt optimisation addressable, attributable, and actionable. Across a 14-task held-out suite, SEPO improves over the strongest baseline, GEPA, by 3.1 pp on Llama-3.1-8B-Instruct and 2.2 pp on Qwen3-8B, reaching 61.9% and 73.3% macro accuracy. SEPO also lies on both the optimisation-time and test-time Pareto frontiers, spending 2.9M optimisation tokens versus 4.1M for GEPA and producing prompts over 5x shorter.

cs.AI↗

arXiv2026

REC-CBM: Rubric-Aware Error-Correction Concept Bottleneck Models for Trustworthy Open-Ended Grading

Open-ended grading is central to equitable and personalized education, yet manual grading remains time-consuming and costly, underscoring the need for automated grading systems. Although recent neural and large language model (LLM) based systems have demonstrated superior performance, they are typically black-box models whose scoring processes and rationales are difficult for educators to verify and trust. Concept bottleneck models (CBMs) have emerged as a promising approach by routing predictions through human-interpretable concepts, providing a mechanistic guarantee of transparency. However, standard CBMs are not tailored to open-ended grading: they do not explicitly model fine-grained rubric dimensions, inadequately capture the ordinal semantics of scoring scales, and neglect inherent reliability issues in human concept annotations. To address these limitations, we propose REC-CBM, a rubric-aware error-correction concept bottleneck model for trustworthy open-ended grading. REC-CBM introduces a rubric-aware concept encoder that learns concept-specific representations over responses and an ordinal pairwise calibration objective that preserves ranking structure among rubric dimensions. It further incorporates a latent concept error-correction module that denoises concept predictions before final grade prediction while preserving interpretability. Comprehensive experiments on publicly available datasets show that REC-CBM consistently improves grading performance and produces more faithful concept-level reasoning than both state-of-the-art baselines. Further analyses validate the contribution of each component and demonstrate the applicability in realistic educational settings. Overall, this work provides a practical, interpretable grading solution that enables educators to inspect, intervene in, and trust automated decisions, advancing more transparent and trustworthy education.

cs.CY↗

arXiv2026

Select Smarter, Not More: Prompt-Aware Evaluation Scheduling with Submodular Guarantees

Automatic prompt optimization (APO) hinges on the quality of its evaluation signal, yet scoring every prompt candidate on the full training set is prohibitively expensive. Existing methods either fix a single evaluation subset before optimization begins (principled but prompt-agnostic) or adapt it heuristically during optimization (flexible but unstable and lacking formal guarantees). We observe that APO naturally maps to an online adaptive testing problem: prompts are examinees, training examples are test items, and the scheduler should select items that best discriminate among the strongest candidates. This insight motivates Prompt-Aware Online Evaluation Scheduling (POES), which integrates an IRT-based discrimination utility, a facility-location coverage term, and switching-cost-aware warm-start swaps into a unified objective that is provably monotone submodular, yielding a (1-1/e) greedy guarantee for cold starts and bounded drift for warm-start updates. An adaptive controller modulates the exploration-exploitation balance based on optimization progress. Across 36 tasks spanning three benchmark families, POES achieves the highest overall average accuracy (6.2 percent improvement over the best baseline) with negligible token overhead (approximately 4 percent) at the same evaluation budget. Moreover, principled selection at k = 20 examples matches or exceeds the performance of naive evaluation at k = 30-50, reducing token consumption by 35-60 percent, showing that selecting smarter is more effective than selecting more. Our results demonstrate that evaluation scheduling is a first-class component of APO, not an implementation detail.

cs.AI↗

arXiv2025

AG-Fusion: adaptive gated multimodal fusion for 3d object detection in complex scenes

Multimodal camera-LiDAR fusion technology has found extensive application in 3D object detection, demonstrating encouraging performance. However, existing methods exhibit significant performance degradation in challenging scenarios characterized by sensor degradation or environmental disturbances. We propose a novel Adaptive Gated Fusion (AG-Fusion) approach that selectively integrates cross-modal knowledge by identifying reliable patterns for robust detection in complex scenes. Specifically, we first project features from each modality into a unified BEV space and enhance them using a window-based attention mechanism. Subsequently, an adaptive gated fusion module based on cross-modal attention is designed to integrate these features into reliable BEV representations robust to challenging environments. Furthermore, we construct a new dataset named Excavator3D (E3D) focusing on challenging excavator operation scenarios to benchmark performance in complex conditions. Our method not only achieves competitive performance on the standard KITTI dataset with 93.92% accuracy, but also significantly outperforms the baseline by 24.88% on the challenging E3D dataset, demonstrating superior robustness to unreliable modal information in complex industrial scenes.

cs.CV↗

arXiv2025

CyberBOT: Towards Reliable Cybersecurity Education via Ontology-Grounded Retrieval Augmented Generation

Advancements in large language models (LLMs) have enabled the development of intelligent educational tools that support inquiry-based learning across technical domains. In cybersecurity education, where accuracy and safety are paramount, systems must go beyond surface-level relevance to provide information that is both trustworthy and domain-appropriate. To address this challenge, we introduce CyberBOT, a question-answering chatbot that leverages a retrieval-augmented generation (RAG) pipeline to incorporate contextual information from course-specific materials and validate responses using a domain-specific cybersecurity ontology. The ontology serves as a structured reasoning layer that constrains and verifies LLM-generated answers, reducing the risk of misleading or unsafe guidance. CyberBOT has been deployed in a large graduate-level course at Arizona State University (ASU), where more than one hundred students actively engage with the system through a dedicated web-based platform. Computational evaluations in lab environments highlight the potential capacity of CyberBOT, and a forthcoming field study will evaluate its pedagogical impact. By integrating structured domain reasoning with modern generative capabilities, CyberBOT illustrates a promising direction for developing reliable and curriculum-aligned AI applications in specialized educational contexts.

cs.AI↗

arXiv2025

Phase-based stimulated emission depletion (pSTED) magnetic particle imaging

Magnetic particle imaging (MPI) is an in vivo method to detect magnetic nanoparticles for cell tracking, vascular imaging, and molecular target imaging without ionizing radiation. Current magnetic particle imaging is accomplished by forming an field-free line (FFL) through a gradient selection field. By translating and rotating FFL under excitation and drive fields, the harmonic complex signal of a point source forms a Lorentzian-shape point spread function on the plane perpendicular to FFL. The Lorentzian PSF has a finite size and limited resolution due to the non-sharp Langevin function and weak selection field. This study proposes a donut-shaped focal spot by borrowing the stimulated emission depletion (STED) fluorescence microscopy principle. The influence of the gradient selection field on the relaxation time of magnetic particles determines the nonlinear phase shift of the harmonic complex signals, resulting in the formation of a donut-shaped focal spot. By subtracting the donut-shaped focal spot from the Lorentzian focal spot, the STED focal spot size was reduced by up to 4 times beyond the Langevin magnetization resolution barrier. In human brain FFL-based MPI scanner, the donut-shaped focal spot can be used to reconstruct images with super-resolution and super-sensitivity through the deconvoution of the STED focal spot and filtered backprojection algorithm.

physics.med-ph↗

arXiv2024

Density-wave like behavior in a new Kagome material Ce$_{2}$Ru$_{3}$Si

Kagome materials with inherent geometric frustration can produce many interesting physical properties, such as flat bands, quantum spin liquid, chiral magnetism, superconductivity and density-wave orders. Sometimes, the localized 4$f$ electrons from Ce atoms coupled with other conduction electrons would also give rise to the flat bands near the Fermi level, and results in the formation of heavy fermion. Thus, it is highly probable that kagome material incorporating Ce element will display nontrivial physical properties. In this study, we present a new Kagome material belonging to the trinary Laves phase, Ce$_{2}$Ru$_{3}$Si, in which kagome plane is formed by Ru atoms. Electrical transport and specific heat measurements reveal a density-wave like transition. A Curie-Weiss behavior is observed in low-temperature region. Meanwhile we also find a relatively large specific coefficient $γ_{n}(0)$. The calculated Wilson ratio $R_\mathrm{W}\propto{χ(0)/γ_{n}}$ is approximately 3.1, indicating a moderate electron correlation effect. Chemical doping of Ir at the Ru site rapidly suppresses this density-wave like transition, while Mo doping leads to a gradual decrease in transition temperature. Theoretical calculation indicates both the Ce-4$f$ and Ru-4$d$ electronic bands cross the Fermi level, forming a Mexican-hat-shape Fermi surface close to the Fermi energy, potentially accounting for the observed density-wave like transition. Our findings provide an useful platform for investigating how hybridization between 4$f$ and 4$d$ electrons influences the electronic transport, and the relationship between the density-wave transition and kagome structure.

cond-mat.str-el↗

arXiv2024

Enhanced Coupling of Superconductivity and Evolution of Gap Structure in CsV3Sb5 through Ta Doping

In this study, we present a detailed investigation of kagome superconductors CsV3Sb5 single crystal and its Ta-doped variant, Cs(V0.86Ta0.14)3Sb5, through specific heat measurements. Our results show a clear suppression of the charge density wave (CDW) and notable increase in the superconducting transition temperature (Tc) from 2.8 K to 4.6 K upon Ta doping. The electronic specific heat of the pristine CsV3Sb5 sample can be fitted with a model comprising an s-wave gap and a highly anisotropic extended s-wave gap, where the ratio 2Δ/k_B T_c is smaller than the weak coupling limit of 3.5. For the doped sample Cs(V0.86Ta0.14)3Sb5, it exhibits two isotropic s-wave gaps, yielding the larger gap of 2Δ/k_B T_c=5.04, which indicates a significant enhancement in superconducting coupling. This evolution is attributed to the increased density of states (DOS) near the Fermi level released through the suppression of the CDW gap. Our results demonstrate enhanced superconducting coupling and variation of gap structure in CsV3Sb5 due to Ta doping.

cond-mat.supr-con↗

arXiv2024

Superconductivity up to 14.2 K in MnB$_4$ under pressure

The discovery of superconductivity in 3$d$-transition metal compounds with strong magnetism is interesting but rare. Especially for Mn-based compounds, there exist only very limited materials that show superconductivity. Here, we report the discovery of superconductivity up to 14.2 K in a Mn-based material MnB$_4$. By applying high pressures, we found the continuous suppression of a weak insulating behavior and the occurrence of superconductivity after about 30 GPa. With further increasing pressure, $T_\text{c}$ is gradually enhanced and reaches the maximum value of about 14.2 K at 150 GPa with a Fermi-Liquid behavior in the normal states. The synchrotron X-ray diffraction data reveal the unchanged monoclinic (S.G: $P2_1/c$) symmetry but an unusual crossover of the lattice parameters $b$ and $c$. Theoretical calculations based on the electron-phonon coupling picture reveal a very low $T_\text{c}$ (less than 1 K), manifesting an exotic pairing mechanism beyond the Bardeen-Cooper-Schrieffer (BCS) theory. Our findings show a promising way to explore high $T_\text{c}$ superconductivity by combining the 3d-transition metal magnetic elements and light elements.

cond-mat.supr-con↗

arXiv2024

Semi-weakly-supervised neural network training for medical image registration

For training registration networks, weak supervision from segmented corresponding regions-of-interest (ROIs) have been proven effective for (a) supplementing unsupervised methods, and (b) being used independently in registration tasks in which unsupervised losses are unavailable or ineffective. This correspondence-informing supervision entails cost in annotation that requires significant specialised effort. This paper describes a semi-weakly-supervised registration pipeline that improves the model performance, when only a small corresponding-ROI-labelled dataset is available, by exploiting unlabelled image pairs. We examine two types of augmentation methods by perturbation on network weights and image resampling, such that consistency-based unsupervised losses can be applied on unlabelled data. The novel WarpDDF and RegCut approaches are proposed to allow commutative perturbation between an image pair and the predicted spatial transformation (i.e. respective input and output of registration networks), distinct from existing perturbation methods for classification or segmentation. Experiments using 589 male pelvic MR images, labelled with eight anatomical ROIs, show the improvement in registration performance and the ablated contributions from the individual strategies. Furthermore, this study attempts to construct one of the first computational atlases for pelvic structures, enabled by registering inter-subject MRs, and quantifies the significant differences due to the proposed semi-weak supervision with a discussion on the potential clinical use of example atlas-derived statistics.

eess.IV↗

arXiv2024

Multiband superconductivity and a deep gap minimum from the specific heat in KCa$_2$(Fe$_{1-x}$Ni$_x$)$_4$As$_4$F$_2$ ($x$ = 0, 0.05, 0.13)

Specific heat can explore low-energy quasiparticle excitations of superconductors, so it is a powerful tool for bulk measurement on the superconducting gap structure and pairing symmetry. Here, we report an in-depth investigation on the specific heat of the multiband superconductors KCa$_2$(Fe$_{1-x}$Ni$_x$)$_4$As$_4$F$_2$ ($x$ = 0, 0.05, 0.13) single crystals and the overdoped non-superconducting one with $x$ = 0.17. Clear specific heat anomalies can be observed at the superconducting transition temperature of 33.6 K and 28.8 K for the samples with $x$ = 0 and $x$ = 0.05, respectively. For the two samples, the magnetic field induced specific heat coefficient $Δγ(H)$ in the low-temperature limit increases rapidly below 2 T, then it rises slowly above 2 T. Using the non-superconducting sample with $x$ = 0.17 as a reference, the specific heat of phonon background for various superconducting samples can be obtained and subtracted, which allows us to extract the electronic specific heat of the superconducting samples. Through comparative analyses, it is found that the energy gap structure including two $s$-wave gaps and an extended $s$-wave gap with large anisotropy can reasonably describe the electronic specific heat data. According to these results, we suggest that at least one anisotropic superconducting gap with a deep gap minimum should exist in this multiband system. With the doping of Ni, the superconducting transition temperature of the sample decreases along with the decrease of the large $s$-wave gap, but the extended $s$-wave gap increases due to the enlarged electron pockets via adding more electrons. Despite these changes, the general properties of the gap structure remain unchanged versus doping Ni.

cond-mat.supr-con↗

arXiv2023

A Recycling Training Strategy for Medical Image Segmentation with Diffusion Denoising Models

Denoising diffusion models have found applications in image segmentation by generating segmented masks conditioned on images. Existing studies predominantly focus on adjusting model architecture or improving inference, such as test-time sampling strategies. In this work, we focus on improving the training strategy and propose a novel recycling method. During each training step, a segmentation mask is first predicted given an image and a random noise. This predicted mask, which replaces the conventional ground truth mask, is used for denoising task during training. This approach can be interpreted as aligning the training strategy with inference by eliminating the dependence on ground truth masks for generating noisy samples. Our proposed method significantly outperforms standard diffusion training, self-conditioning, and existing recycling strategies across multiple medical imaging data sets: muscle ultrasound, abdominal CT, prostate MR, and brain MR. This holds for two widely adopted sampling strategies: denoising diffusion probabilistic model and denoising diffusion implicit model. Importantly, existing diffusion models often display a declining or unstable performance during inference, whereas our novel recycling consistently enhances or maintains performance. We show that, under a fair comparison with the same network architectures and computing budget, the proposed recycling-based diffusion models achieved on-par performance with non-diffusion-based supervised training. By ensembling the proposed diffusion and the non-diffusion models, significant improvements to the non-diffusion models have been observed across all applications, demonstrating the value of this novel training method. This paper summarizes these quantitative results and discusses their values, with a fully reproducible JAX-based implementation, released at https://github.com/mathpluscode/ImgX-DiffSeg.

eess.IV↗

arXiv2023

Unconventional superconductivity in Sc$_2$Ir$_{4-x}$Si$_x$ by spin-orbit coupling driven flat band

The kagome lattice is very attractive as it can host many novel quantum states, such as the charge density wave, superconductivity, quantum spin liquid, etc. Meanwhile, iridates often exhibit a strong spin-orbit coupling (SOC) effect due to the large atomic mass of 5$d$ elements, which has important implications for both the energy bands and the pairing symmetry of superconductors. For the Laves phase superconductor Sc$_2$Ir$_4$ with a kagome lattice, by doping Si to the Ir sites, we observed a nonmonotonic and two-dome like doping dependence of the superconducting transition temperature $T_{\rm c}$, which is typically found in many unconventional superconducting systems. Interestingly, for some samples, especially Sc$_2$Ir$_{3.5}$Si$_{0.5}$ with the optimal $T_{\rm c}$, after the suppression of superconductivity, the normal-state resistivity exhibits a semiconducting behavior; meanwhile, the specific heat coefficient shows an upturn which follows the relation $C/T\propto{\rm ln}(T_0/T)$ at low temperatures. Around the optimal doping, the resistance measurements exhibit strong superconducting fluctuations. And the superconductivity related specific heat can be fitted by the model of a $d$-wave gap after subtracting the normal-state background. These strongly suggest unconventional superconductivity and correlation effect in the samples, which is mainly induced by a flat band near the Fermi level when considering the SOC, as supported by the first-principles calculations. Our results reveal a new unconventional superconducting system Sc$_2$Ir$_{4-x}$Si$_x$ with strong correlation effects induced by the flat band in the kagome system with strong SOC.

cond-mat.supr-con↗

arXiv2023

Prototypical few-shot segmentation for cross-institution male pelvic structures with spatial registration

The prowess that makes few-shot learning desirable in medical image analysis is the efficient use of the support image data, which are labelled to classify or segment new classes, a task that otherwise requires substantially more training images and expert annotations. This work describes a fully 3D prototypical few-shot segmentation algorithm, such that the trained networks can be effectively adapted to clinically interesting structures that are absent in training, using only a few labelled images from a different institute. First, to compensate for the widely recognised spatial variability between institutions in episodic adaptation of novel classes, a novel spatial registration mechanism is integrated into prototypical learning, consisting of a segmentation head and an spatial alignment module. Second, to assist the training with observed imperfect alignment, support mask conditioning module is proposed to further utilise the annotation available from the support images. Extensive experiments are presented in an application of segmenting eight anatomical structures important for interventional planning, using a data set of 589 pelvic T2-weighted MR images, acquired at seven institutes. The results demonstrate the efficacy in each of the 3D formulation, the spatial registration, and the support mask conditioning, all of which made positive contributions independently or collectively. Compared with the previously proposed 2D alternatives, the few-shot segmentation performance was improved with statistical significance, regardless whether the support data come from the same or different institutes.

eess.IV↗

arXiv2023

Importance of Aligning Training Strategy with Evaluation for Diffusion Models in 3D Multiclass Segmentation

Recently, denoising diffusion probabilistic models (DDPM) have been applied to image segmentation by generating segmentation masks conditioned on images, while the applications were mainly limited to 2D networks without exploiting potential benefits from the 3D formulation. In this work, we studied the DDPM-based segmentation model for 3D multiclass segmentation on two large multiclass data sets (prostate MR and abdominal CT). We observed that the difference between training and test methods led to inferior performance for existing DDPM methods. To mitigate the inconsistency, we proposed a recycling method which generated corrupted masks based on the model's prediction at a previous time step instead of using ground truth. The proposed method achieved statistically significantly improved performance compared to existing DDPMs, independent of a number of other techniques for reducing train-test discrepancy, including performing mask prediction, using Dice loss, and reducing the number of diffusion time steps during training. The performance of diffusion models was also competitive and visually similar to non-diffusion-based U-net, within the same compute budget. The JAX-based diffusion framework has been released at https://github.com/mathpluscode/ImgX-DiffSeg.

eess.IV↗

arXiv2023

Planckian Dissipation and non-Ginzburg-Landau Type Upper Critical Field in Bi2201

Resistivity and Hall effect measurements have been carried out on a micro-fabricated bridge of Bi2201 single crystal at low temperatures down to 0.4 K under high magnetic fields. When superconductivity is crashed by a high magnetic field, the recovered "normal state" resistivity still shows a linear temperature dependence in low temperature region. Combining with the effective mass and the charge carrier density, we get a linear scattering rate $1/τ= αk_{B} T/\hbar$ with $0.77<α<1.16$, which gives a strong evidence of the Planckian dissipation. Furthermore, our results reveal a new type of temperature dependence of upper critical field, $H_{c2}(T)=H^*\sqrt{(1-t)/(t+0.154)}$, which is totally different from the expectation of the Ginzburg-Landau theory, and suggests uncondensed Cooper pairs above $H_{c2}(T)$ line.

cond-mat.supr-con↗

arXiv2022

MONAI: An open-source framework for deep learning in healthcare

Artificial Intelligence (AI) is having a tremendous impact across most areas of science. Applications of AI in healthcare have the potential to improve our ability to detect, diagnose, prognose, and intervene on human disease. For AI models to be used clinically, they need to be made safe, reproducible and robust, and the underlying software framework must be aware of the particularities (e.g. geometry, physiology, physics) of medical data being processed. This work introduces MONAI, a freely available, community-supported, and consortium-led PyTorch-based framework for deep learning in healthcare. MONAI extends PyTorch to support medical data, with a particular focus on imaging, and provide purpose-specific AI model architectures, transformations and utilities that streamline the development and deployment of medical AI models. MONAI follows best practices for software-development, providing an easy-to-use, robust, well-documented, and well-tested software framework. MONAI preserves the simple, additive, and compositional approach of its underlying PyTorch libraries. MONAI is being used by and receiving contributions from research, clinical and industrial teams from around the world, who are pursuing applications spanning nearly every aspect of healthcare.

cs.LG↗

arXiv2022

Comparative studies on unconventional superconductivity in Cr$_3$Ru compounds with bcc and A15 structures

Chromium (Cr) is a transition metal element with 3$d$ orbital electrons. In most compounds containing Cr, due to the correlation effect, twofold features of localization and itinerancy are expected. The localization gives rise to a magnetic moment, while the latter exhibits as the effective coherent weight for conductivity. Here we report the physical properties of Cr$_3$Ru compounds with bcc or A15 phases by using multiple experimental tools. The resistivity measurements show sharp superconducting transitions at $T_{\rm c}$ = 2.77 K and $T_{\rm c}$ = 3.37 K for the bcc and A15 structures. A high residual resistivity exists for both samples with the mean-free-path about 2 nm. Magnetization measurements also show rather narrow transitions, with a clear hump structure at high temperatures around 150 K, which may be ascribed to the remaining antiferromagnetic spin fluctuations. The specific heat coefficient reveals a clear jump at $T_{\rm c}$. We find that s-wave gaps can be adopted to fit the low temperature specific heat data yielding ratios of about $2Δ/k_{\scriptscriptstyle B}T_{\rm c}$ $\approx$ 3.6, indicating a moderate pairing strength. Interestingly, the Wilson ratios are 3.81 and 3.62 for the bcc and A15 phases, suggesting a moderate correlation effect of conducting electron in the normal state. Besides, for samples with A15 structure, another specific heat anomaly occurs at about 0.85 K and is sensitive to magnetic fields. By applying high pressures, both system exhibit an enhancement of $T_{\rm c}$ with a rate of about 0.019 K/GPa and 0.013 K/GPa for the bcc and A15 phases. We also conduct tunneling spectrum measurements and found that the coherence peaks are strongly smeared out. The strong suppression to the coherence peaks may be ascribed to the strong scattering. Our combinatory results point to an unconventional superconductivity in these Cr based compounds.

cond-mat.supr-con↗