arXiv subjects

Ningning Zhao

Publications and source records attributed to Ningning Zhao.

11 recordsLinked to original sources

arXiv2026

A Unified Framework to Elicit Structured Feedback for Interpretable Multi-Trait Essay Scoring

Multi-trait Automated Essay Scoring (AES) requires rubric-grounded reasoning across interdependent traits, rather than isolated score prediction. Existing feedback-enhanced methods often decouple feedback from scoring or assess traits independently, weakening score--feedback consistency and rubric alignment. We propose HiFTS, a unified autoregressive framework that generates hierarchical CoT feedback before predicting trait-level and holistic scores. HiFTS distills rubric-grounded hierarchical CoT feedback from a teacher LLM and trains student models to jointly generate feedback and scores. HiFTS further applies Group Relative Policy Optimization with a composite reward balancing score agreement, calibration, feedback quality, and structural validity. At inference, a lightweight global prior provides holistic guidance to reduce drift during long-form reasoning. We also introduce CFMS-34, a Chinese multi-trait AES dataset with 951 essays annotated with holistic scores and 34 rubric-based traits. Experiments on CFMS-34 and ASAP++ show that HiFTS achieves strong holistic and trait-level scoring while producing coherent, rubric-aligned feedback.

cs.CL↗

arXiv2025

Harnessing Chain-of-Thought Reasoning in Multimodal Large Language Models for Face Anti-Spoofing

Face Anti-Spoofing (FAS) typically depends on a single visual modality when defending against presentation attacks such as print attacks, screen replays, and 3D masks, resulting in limited generalization across devices, environments, and attack types. Meanwhile, Multimodal Large Language Models (MLLMs) have recently achieved breakthroughs in image-text understanding and semantic reasoning, suggesting that integrating visual and linguistic co-inference into FAS can substantially improve both robustness and interpretability. However, the lack of a high-quality vision-language multimodal dataset has been a critical bottleneck. To address this, we introduce FaceCoT (Face Chain-of-Thought), the first large-scale Visual Question Answering (VQA) dataset tailored for FAS. FaceCoT covers 14 spoofing attack types and enriches model learning with high-quality CoT VQA annotations. Meanwhile, we develop a caption model refined via reinforcement learning to expand the dataset and enhance annotation quality. Furthermore, we introduce a CoT-Enhanced Progressive Learning (CEPL) strategy to better leverage the CoT data and boost model performance on FAS tasks. Extensive experiments demonstrate that models trained with FaceCoT and CEPL outperform state-of-the-art methods on multiple benchmark datasets.

cs.CV↗

arXiv2023

Kagome surface states and weak electronic correlation in vanadium-kagome metals

RV6Sn6 (R = Y and lanthanides) with two-dimensional vanadium-kagome surface states is an ideal platform to investigate kagome physics and manipulate the kagome features to realize novel phenomena. Utilizing the micron-scale spatially resolved angle-resolved photoemission spectroscopy and first-principles calculations, we report a systematical study of the electronic structures of RV6Sn6 (R = Gd, Tb, and Lu) on the two cleaved surfaces, i.e., the V- and RSn1-terminated (001) surfaces. The calculated bands without any renormalization match well with the main ARPES dispersive features, indicating the weak electronic correlation in this system. We observe 'W'-like kagome surface states around the Brillouin zone corners showing R-element-dependent intensities, which is probably due to various coupling strengths between V and RSn1 layers. Our finding suggests an avenue for tuning electronic states by interlayer coupling based on two-dimensional kagome lattices.

cond-mat.mtrl-sci↗

arXiv2021

ForgeryNet -- Face Forgery Analysis Challenge 2021: Methods and Results

The rapid progress of photorealistic synthesis techniques has reached a critical point where the boundary between real and manipulated images starts to blur. Recently, a mega-scale deep face forgery dataset, ForgeryNet which comprised of 2.9 million images and 221,247 videos has been released. It is by far the largest publicly available in terms of data-scale, manipulations (7 image-level approaches, 8 video-level approaches), perturbations (36 independent and more mixed perturbations), and annotations (6.3 million classification labels, 2.9 million manipulated area annotations, and 221,247 temporal forgery segment labels). This paper reports methods and results in the ForgeryNet - Face Forgery Analysis Challenge 2021, which employs the ForgeryNet benchmark. The model evaluation is conducted offline on the private test set. A total of 186 participants registered for the competition, and 11 teams made valid submissions. We will analyze the top-ranked solutions and present some discussion on future work directions.

cs.CV↗

arXiv2021

Quantum transport evidence of topological band structures of kagome superconductor CsV3Sb5

We report the transport properties of kagome superconductor CsV$_{3}$Sb$_{5}$ single crystals at magnetic field up to 32 T. The Shubnikov de Haas (SdH) oscillations emerge at low temperature and four frequencies of $F_α=$ 27 T, $F_β=$ 73 T, $F_ε=$ 727 T, and $F_η=$ 786 T with relatively small cyclotron masses are observed. For $F_β$ and $F_ε$, the Berry phases are close to $π$, providing a clear evidence of nontrivial topological band structures of CsV$_{3}$Sb$_{5}$. Furthermore, the consistence between theoretical calculations and experimental results implies that these frequencies can be assigned to the Fermi surfaces locating near the boundary of Brillouin zone and confirms that the structure with an inverse Star of David distortion could be the most stable structure at charge density wave state. These results will shed light on the nature of correlated topological physics in kagome material CsV$_{3}$Sb$_{5}$.

cond-mat.supr-con↗

arXiv2021

Pressure-induced Superconductivity in Noncentrosymmetric Weyl Semimetals LaAlX (X = Si and Ge)

In topological materials, Dirac fermions can split into two Weyl fermions with opposite chiralities due to the breaking of space inversion symmetry, while in non-centrosymmetric superconductors, novel superconducting electron pairing mechanisms arise because of the antisymmetric spin-orbit coupling. In this work, we report the pressure-introduced superconductivity in a typical noncentrosymmetric Weyl semimetal LaAlX (X=Si and Ge). Superconductivity was observed at around 65 GPa without structural phase transition. A typical dome-shape phase diagram is obtained with the maximum Tc of 2.5 K (2.1 K) for LaAlSi (LaAlGe). Furthermore, the application of pressure does not destroy the nontrivial band topology of LaAlSi up to 80.4 GPa, making such materials as potential candidates for realizing topological superconductivity. Our discovery of superconductivity in LaAlX (X=Si and Ge) will provide critical insight in noncentrosymmetric superconductors and stimulate further study on superconductivity in Weyl semimetals.

cond-mat.supr-con↗

arXiv2021

Charge-density-wave-induced bands renormalization and energy gaps in a kagome superconductor RbV3Sb5

Recently discovered Z2 topological kagome metals AV3Sb5 (A = K, Rb, and Cs) exhibit charge density wave (CDW) phases and novel superconducting paring states, providing a versatile platform for studying the interplay between electron correlation and quantum orders. Here we directly visualize CDW-induced bands renormalization and energy gaps in RbV3Sb5 using angle-resolved photoemission spectroscopy, pointing to the key role of tuning van Hove singularities to the Fermi energy in mechanisms of ordering phases. Near the CDW transition temperature, the bands around the Brillouin zone (BZ) boundary are shifted to high-binding energy, forming an "M"-shape band with singularities near the Fermi energy. The Fermi surfaces are partially gapped and the electronic states on the residual ones should be possibly dedicated to the superconductivity. Our findings are significant in understanding CDW formation and its associated superconductivity.

cond-mat.supr-con↗

arXiv2019

Fully Automated Pancreas Segmentation with Two-stage 3D Convolutional Neural Networks

Due to the fact that pancreas is an abdominal organ with very large variations in shape and size, automatic and accurate pancreas segmentation can be challenging for medical image analysis. In this work, we proposed a fully automated two stage framework for pancreas segmentation based on convolutional neural networks (CNN). In the first stage, a U-Net is trained for the down-sampled 3D volume segmentation. Then a candidate region covering the pancreas is extracted from the estimated labels. Motivated by the superior performance reported by renowned region based CNN, in the second stage, another 3D U-Net is trained on the candidate region generated in the first stage. We evaluated the performance of the proposed method on the NIH computed tomography (CT) dataset, and verified its superiority over other state-of-the-art 2D and 3D approaches for pancreas segmentation in terms of dice-sorensen coefficient (DSC) accuracy in testing. The mean DSC of the proposed method is 85.99%.

cs.CV↗

arXiv2019

Motion Compensated Dynamic MRI Reconstruction with Local Affine Optical Flow Estimation

This paper proposes a novel framework to reconstruct the dynamic magnetic resonance images (DMRI) with motion compensation (MC). Due to the inherent motion effects during DMRI acquisition, reconstruction of DMRI using motion estimation/compensation (ME/MC) has been studied under a compressed sensing (CS) scheme. In this paper, by embedding the intensity-based optical flow (OF) constraint into the traditional CS scheme, we are able to couple the DMRI reconstruction with motion field estimation. The formulated optimization problem is solved by a primal-dual algorithm with linesearch due to its efficiency when dealing with non-differentiable problems. With the estimated motion field, the DMRI reconstruction is refined through MC. By employing the multi-scale coarse-to-fine strategy, we are able to update the variables(temporal image sequences and motion vectors) and to refine the image reconstruction alternately. Moreover, the proposed framework is capable of handling a wide class of prior information (regularizations) for DMRI reconstruction, such as sparsity, low rank and total variation. Experiments on various DMRI data, ranging from in vivo lung to cardiac dataset, validate the reconstruction quality improvement using the proposed scheme in comparison to several state-of-the-art algorithms.

cs.CV↗

arXiv2016

Joint Segmentation and Deconvolution of Ultrasound Images Using a Hierarchical Bayesian Model based on Generalized Gaussian Priors

This paper proposes a joint segmentation and deconvolution Bayesian method for medical ultrasound (US) images. Contrary to piecewise homogeneous images, US images exhibit heavy characteristic speckle patterns correlated with the tissue structures. The generalized Gaussian distribution (GGD) has been shown to be one of the most relevant distributions for characterizing the speckle in US images. Thus, we propose a GGD-Potts model defined by a label map coupling US image segmentation and deconvolution. The Bayesian estimators of the unknown model parameters, including the US image, the label map and all the hyperparameters are difficult to be expressed in closed form. Thus, we investigate a Gibbs sampler to generate samples distributed according to the posterior of interest. These generated samples are finally used to compute the Bayesian estimators of the unknown parameters. The performance of the proposed Bayesian model is compared with existing approaches via several experiments conducted on realistic synthetic data and in vivo US images.

cs.CV↗

arXiv2016

Fast Single Image Super-Resolution

This paper addresses the problem of single image super-resolution (SR), which consists of recovering a high resolution image from its blurred, decimated and noisy version. The existing algorithms for single image SR use different strategies to handle the decimation and blurring operators. In addition to the traditional first-order gradient methods, recent techniques investigate splitting-based methods dividing the SR problem into up-sampling and deconvolution steps that can be easily solved. Instead of following this splitting strategy, we propose to deal with the decimation and blurring operators simultaneously by taking advantage of their particular properties in the frequency domain, leading to a new fast SR approach. Specifically, an analytical solution can be obtained and implemented efficiently for the Gaussian prior or any other regularization that can be formulated into an $\ell_2$-regularized quadratic model, i.e., an $\ell_2$-$\ell_2$ optimization problem. Furthermore, the flexibility of the proposed SR scheme is shown through the use of various priors/regularizations, ranging from generic image priors to learning-based approaches. In the case of non-Gaussian priors, we show how the analytical solution derived from the Gaussian case can be embedded intotraditional splitting frameworks, allowing the computation cost of existing algorithms to be decreased significantly. Simulation results conducted on several images with different priors illustrate the effectiveness of our fast SR approach compared with the existing techniques.

cs.CV↗