arXiv subjects

Qian Zhao

Publications and source records attributed to Qian Zhao.

At least 55 records · Page 3Linked to original sources

arXiv2025

Generative Latent Kernel Modeling for Blind Motion Deblurring

Deep prior-based approaches have demonstrated remarkable success in blind motion deblurring (BMD) recently. These methods, however, are often limited by the high non-convexity of the underlying optimization process in BMD, which leads to extreme sensitivity to the initial blur kernel. To address this issue, we propose a novel framework for BMD that leverages a deep generative model to encode the kernel prior and induce a better initialization for the blur kernel. Specifically, we pre-train a kernel generator based on a generative adversarial network (GAN) to aptly characterize the kernel's prior distribution, as well as a kernel initializer to provide a well-informed and high-quality starting point for kernel estimation. By combining these two components, we constrain the BMD solution within a compact latent kernel manifold, thus alleviating the aforementioned sensitivity for kernel initialization. Notably, the kernel generator and initializer are designed to be easily integrated with existing BMD methods in a plug-and-play manner, enhancing their overall performance. Furthermore, we extend our approach to tackle blind non-uniform motion deblurring without the need for additional priors, achieving state-of-the-art performance on challenging benchmark datasets. The source code is available at https://github.com/dch0319/GLKM-Deblur.

cs.CV↗

arXiv2025

Latent Imputation before Prediction: A New Computational Paradigm for De Novo Peptide Sequencing

De novo peptide sequencing is a fundamental computational technique for ascertaining amino acid sequences of peptides directly from tandem mass spectrometry data, eliminating the need for reference databases. Cutting-edge models usually encode the observed mass spectra into latent representations from which peptides are predicted autoregressively. However, the issue of missing fragmentation, attributable to factors such as suboptimal fragmentation efficiency and instrumental constraints, presents a formidable challenge in practical applications. To tackle this obstacle, we propose a novel computational paradigm called \underline{\textbf{L}}atent \underline{\textbf{I}}mputation before \underline{\textbf{P}}rediction (LIPNovo). LIPNovo is devised to compensate for missing fragmentation information within observed spectra before executing the final peptide prediction. Rather than generating raw missing data, LIPNovo performs imputation in the latent space, guided by the theoretical peak profile of the target peptide sequence. The imputation process is conceptualized as a set-prediction problem, utilizing a set of learnable peak queries to reason about the relationships among observed peaks and directly generate the latent representations of theoretical peaks through optimal bipartite matching. In this way, LIPNovo manages to supplement missing information during inference and thus boosts performance. Despite its simplicity, experiments on three benchmark datasets demonstrate that LIPNovo outperforms state-of-the-art methods by large margins. Code is available at \href{https://github.com/usr922/LIPNovo}{https://github.com/usr922/LIPNovo}.

cs.CE↗

arXiv2025

Robust and Computationally Efficient Trimmed L-Moments Estimation for Parametric Distributions

This paper proposes a robust and computationally efficient estimation framework for fitting parametric distributions based on trimmed L-moments. Trimmed L-moments extend classical L-moment theory by downweighting or excluding extreme order statistics, resulting in estimators that are less sensitive to outliers and heavy tails. We construct estimators for both location-scale and shape parameters using asymmetric trimming schemes tailored to different moments, and establish their asymptotic properties for inferential justification using the general structural theory of L-statistics, deriving simplified single-integration expressions to ensure numerical stability. State-of-the-art algorithms are developed to resolve the sign ambiguity in estimating the scale parameter for location-scale models and the tail index for the Frechet model. The proposed estimators offer improved efficiency over traditional robust alternatives for selected asymmetric trimming configurations, while retaining closed-form expressions for a wide range of common distributions, facilitating fast and stable computation. Simulation studies demonstrate strong finite-sample performance. An application to financial claim severity modeling highlights the practical relevance and flexibility of the approach.

stat.ME↗

arXiv2025

Generation of Relativistic Structured Spin-Polarized Lepton Beams

Relativistic structured spin-polarized (SSP) particle beams, characterized by polarization structures, are of critical importance in a wide range of applications, such as material properties investigation, imaging, and information storage. However, generation of relativistic SSP beams faces significant challenges. Here, we put forward a novel method for generating relativistic SSP lepton beams via employing a moderate-intensity terahertz (THz) wave. Building upon our foundational work on velocity-matched spin rotation in dielectric-lined waveguides [Phys. Rev. Lett. 134, 075001 (2025)], we present the first demonstration of spin-polarization mode matching - a novel mechanism that establishes a direct relation between waveguide modes and beam polarization states. This breakthrough enables precise spatial control over spin structures at relativistic energies, generating customizable spin-polarization configurations such as spider-like, azimuthal, and helical structures, etc. Such SSP beams have the potential to generate high-energy structured photon beams and open a new avenue for research on relativistic structured particle beams, especially in nuclear physics, high-energy physics, materials science and atomic physics.

physics.optics↗

arXiv2025

Singular Value Fine-tuning for Few-Shot Class-Incremental Learning

Class-Incremental Learning (CIL) aims to prevent catastrophic forgetting of previously learned classes while sequentially incorporating new ones. The more challenging Few-shot CIL (FSCIL) setting further complicates this by providing only a limited number of samples for each new class, increasing the risk of overfitting in addition to standard CIL challenges. While catastrophic forgetting has been extensively studied, overfitting in FSCIL, especially with large foundation models, has received less attention. To fill this gap, we propose the Singular Value Fine-tuning for FSCIL (SVFCL) and compared it with existing approaches for adapting foundation models to FSCIL, which primarily build on Parameter Efficient Fine-Tuning (PEFT) methods like prompt tuning and Low-Rank Adaptation (LoRA). Specifically, SVFCL applies singular value decomposition to the foundation model weights, keeping the singular vectors fixed while fine-tuning the singular values for each task, and then merging them. This simple yet effective approach not only alleviates the forgetting problem but also mitigates overfitting more effectively while significantly reducing trainable parameters. Extensive experiments on four benchmark datasets, along with visualizations and ablation studies, validate the effectiveness of SVFCL. The code will be made available.

cs.CV↗

arXiv2025

Feature Alignment with Equivariant Convolutions for Burst Image Super-Resolution

Burst image processing (BIP), which captures and integrates multiple frames into a single high-quality image, is widely used in consumer cameras. As a typical BIP task, Burst Image Super-Resolution (BISR) has achieved notable progress through deep learning in recent years. Existing BISR methods typically involve three key stages: alignment, upsampling, and fusion, often in varying orders and implementations. Among these stages, alignment is particularly critical for ensuring accurate feature matching and further reconstruction. However, existing methods often rely on techniques such as deformable convolutions and optical flow to realize alignment, which either focus only on local transformations or lack theoretical grounding, thereby limiting their performance. To alleviate these issues, we propose a novel framework for BISR, featuring an equivariant convolution-based alignment, ensuring consistent transformations between the image and feature domains. This enables the alignment transformation to be learned via explicit supervision in the image domain and easily applied in the feature domain in a theoretically sound way, effectively improving alignment accuracy. Additionally, we design an effective reconstruction module with advanced deep architectures for upsampling and fusion to obtain the final BISR result. Extensive experiments on BISR benchmarks show the superior performance of our approach in both quantitative metrics and visual quality.

cs.CV↗

arXiv2025

Exploring the Limit of Outcome Reward for Learning Mathematical Reasoning

Reasoning abilities, especially those for solving complex math problems, are crucial components of general intelligence. Recent advances by proprietary companies, such as o-series models of OpenAI, have made remarkable progress on reasoning tasks. However, the complete technical details remain unrevealed, and the techniques that are believed certainly to be adopted are only reinforcement learning (RL) and the long chain of thoughts. This paper proposes a new RL framework, termed OREAL, to pursue the performance limit that can be achieved through \textbf{O}utcome \textbf{RE}w\textbf{A}rd-based reinforcement \textbf{L}earning for mathematical reasoning tasks, where only binary outcome rewards are easily accessible. We theoretically prove that behavior cloning on positive trajectories from best-of-N (BoN) sampling is sufficient to learn the KL-regularized optimal policy in binary feedback environments. This formulation further implies that the rewards of negative samples should be reshaped to ensure the gradient consistency between positive and negative samples. To alleviate the long-existing difficulties brought by sparse rewards in RL, which are even exacerbated by the partial correctness of the long chain of thought for reasoning tasks, we further apply a token-level reward model to sample important tokens in reasoning trajectories for learning. With OREAL, for the first time, a 7B model can obtain 94.0 pass@1 accuracy on MATH-500 through RL, being on par with 32B models. OREAL-32B also surpasses previous 32B models trained by distillation with 95.0 pass@1 accuracy on MATH-500. Our investigation also indicates the importance of initial policy models and training queries for RL. Code, models, and data will be released to benefit future research\footnote{https://github.com/InternLM/OREAL}.

cs.CL↗

arXiv2025

Computational Protein Science in the Era of Large Language Models (LLMs)

Considering the significance of proteins, computational protein science has always been a critical scientific field, dedicated to revealing knowledge and developing applications within the protein sequence-structure-function paradigm. In the last few decades, Artificial Intelligence (AI) has made significant impacts in computational protein science, leading to notable successes in specific protein modeling tasks. However, those previous AI models still meet limitations, such as the difficulty in comprehending the semantics of protein sequences, and the inability to generalize across a wide range of protein modeling tasks. Recently, LLMs have emerged as a milestone in AI due to their unprecedented language processing & generalization capability. They can promote comprehensive progress in fields rather than solving individual tasks. As a result, researchers have actively introduced LLM techniques in computational protein science, developing protein Language Models (pLMs) that skillfully grasp the foundational knowledge of proteins and can be effectively generalized to solve a diversity of sequence-structure-function reasoning problems. While witnessing prosperous developments, it's necessary to present a systematic overview of computational protein science empowered by LLM techniques. First, we summarize existing pLMs into categories based on their mastered protein knowledge, i.e., underlying sequence patterns, explicit structural and functional information, and external scientific languages. Second, we introduce the utilization and adaptation of pLMs, highlighting their remarkable achievements in promoting protein structure prediction, protein function prediction, and protein design studies. Then, we describe the practical application of pLMs in antibody design, enzyme design, and drug discovery. Finally, we specifically discuss the promising future directions in this fast-growing field.

cs.CE↗

arXiv2024

Unraveling the magnetic and electronic complexity of intermetallic ErPd$_2$Si$_2$: Anisotropic thermal expansion, phase transitions, and twofold magnetotransport behavior

We present a comprehensive investigation into the physical properties of intermetallic ErPd$_2$Si$_2$, a compound renowned for its intriguing magnetic and electronic characteristics. We confirm the tetragonal crystal structure of ErPd$_2$Si$_2$ within the $I4/mmm$ space group. Notably, we observed anisotropic thermal expansion, with the lattice constant $a$ expanding and $c$ contracting between 15 K and 300 K. This behavior is attributed to lattice vibrations and electronic contributions. Heat capacity measurements revealed three distinct temperature regimes: $T_1 \sim 3.0$ K, $T_\textrm{N} \sim 4.20$ K, and $T_2 \sim 15.31$ K. These correspond to the disappearance of spin-density waves, the onset of an incommensurate antiferromagnetic (AFM) structure, and the crystal-field splitting and/or the presence of short-range spin fluctuations, respectively. Remarkably, the AFM phase transition anomaly was observed exclusively in low-field magnetization data (120 Oe) at $T_\textrm{N}$. A high magnetic field ($B =$ 3 T) effectively suppressed this anomaly, likely due to spin-flop and spin-flip transitions. Furthermore, the extracted effective PM moments closely matched the expected theoretical value, suggesting a dominant magnetic contribution from localized 4$f$ spins of Er. Additionally, significant differences in resistance ($R$) values at low temperatures under applied $B$ indicated a magnetoresistance (MR) effect with a minimum value of -4.36\%. Notably, the measured MR effect exhibited anisotropic behavior, where changes in the strength or direction of the applied $B$ induced variations in the MR effect. A twofold symmetry of $R$ was discerned at 3 T and 9 T, originating from the orientation of spin moments relative to the applied $B$. Intriguingly, above $T_\textrm{N}$, short-range spin fluctuations also displayed a preferred orientation along the $c$-axis due to single-ion anisotropy.

cond-mat.str-el↗

arXiv2024

DroughtSet: Understanding Drought Through Spatial-Temporal Learning

Drought is one of the most destructive and expensive natural disasters, severely impacting natural resources and risks by depleting water resources and diminishing agricultural yields. Under climate change, accurately predicting drought is critical for mitigating drought-induced risks. However, the intricate interplay among the physical and biological drivers that regulate droughts limits the predictability and understanding of drought, particularly at a subseasonal to seasonal (S2S) time scale. While deep learning has been demonstrated with potential in addressing climate forecasting challenges, its application to drought prediction has received relatively less attention. In this work, we propose a new dataset, DroughtSet, which integrates relevant predictive features and three drought indices from multiple remote sensing and reanalysis datasets across the contiguous United States (CONUS). DroughtSet specifically provides the machine learning community with a new real-world dataset to benchmark drought prediction models and more generally, time-series forecasting methods. Furthermore, we propose a spatial-temporal model SPDrought to predict and interpret S2S droughts. Our model learns from the spatial and temporal information of physical and biological features to predict three types of droughts simultaneously. Multiple strategies are employed to quantify the importance of physical and biological features for drought prediction. Our results provide insights for researchers to better understand the predictability and sensitivity of drought to biological and physical conditions. We aim to contribute to the climate field by proposing a new tool to predict and understand the occurrence of droughts and provide the AI community with a new benchmark to study deep learning applications in climate science.

cs.LG↗

arXiv2024

Ultrafast Spin Rotation of Relativistic Lepton Beams via Terahertz Wave in a Dielectric-Lined Waveguide

Spin rotation is central for the spin-manipulation of lepton beams which, in turn, plays an important role in investigation of the properties of spin-polarized lepton beams and the examination of spin-dependent interactions. However, realization of compact and ultrafast spin rotation of lepton beams, between longitudinal and transverse polarizations, still faces significant challenges. Here, we put forward a novel method for ultrafast (picosecond-timescale) spin rotation of a relativistic lepton beam via employing a moderate-intensity terahertz (THz) wave in a dielectric-lined waveguide (DLW). The lepton beam undergoes spin precession induced by the THz magnetic field. We find that optimizing the lepton velocity and THz phase velocity in the DLW can mitigate the impact of transverse Lorentz forces on the lepton beam and increase the precession frequency, thereby maintaining the beam quality and enhancing the efficiency of transverse-to-longitudinal spin rotation. The final polarization degree of the lepton beam exceeds $98\%$, and the energy spread can be improved significantly. Flexibility in adjusting the electromagnetic modes within the DLW adds further potential for spin-manipulation, and holds promise for advancing the development of spin-polarized particle beams, which have broad applications in materials science and atomic, nuclear, and high-energy physics.

physics.plasm-ph↗

arXiv2024

Graph Domain Adaptation with Dual-branch Encoder and Two-level Alignment for Whole Slide Image-based Survival Prediction

In recent years, histopathological whole slide image (WSI)- based survival analysis has attracted much attention in medical image analysis. In practice, WSIs usually come from different hospitals or laboratories, which can be seen as different domains, and thus may have significant differences in imaging equipment, processing procedures, and sample sources. These differences generally result in large gaps in distribution between different WSI domains, and thus the survival analysis models trained on one domain may fail to transfer to another. To address this issue, we propose a Dual-branch Encoder and Two-level Alignment (DETA) framework to explore both feature and category-level alignment between different WSI domains. Specifically, we first formulate the concerned problem as graph domain adaptation (GDA) by virtue the graph representation of WSIs. Then we construct a dual-branch graph encoder, including the message passing branch and the shortest path branch, to explicitly and implicitly extract semantic information from the graph-represented WSIs. To realize GDA, we propose a two-level alignment approach: at the category level, we develop a coupling technique by virtue of the dual-branch structure, leading to reduced divergence between the category distributions of the two domains; at the feature level, we introduce an adversarial perturbation strategy to better augment source domain feature, resulting in improved alignment in feature distribution. To the best of our knowledge, our work is the first attempt to alleviate the domain shift issue for WSI data analysis. Extensive experiments on four TCGA datasets have validated the effectiveness of our proposed DETA framework and demonstrated its superior performance in WSI-based survival analysis.

cs.CV↗

arXiv2024

Generation of Ultrarelativistic Vortex Leptons with Large Orbital Angular Momenta

Ultrarelativistic vortex leptons with intrinsic orbital angular momenta (OAM) have important applications in high energy particle physics, nuclear physics, astrophysics, etc. However, unfortunately, their generation still poses a great challenge. Here, we put forward a novel method for generating ultrarelativistic vortex positrons and electrons through nonlinear Breit-Wheeler (NBW) scattering of vortex $γ$ photons. For the first time, a complete angular momentum-resolved scattering theory has been formulated, introducing the angular momentum of laser photons and vortex particles into the conventional NBW scattering framework. We find that vortex positron (electron) can be produced when the outgoing electron (positron) is generated along the collision axis. By unveiling the angular momentum transfer mechanism, we clarify that OAM of the $γ$ photon and angular momenta of multiple laser photons are entirely transferred to the generated pairs, leading to the production of ultrarelativistic vortex positrons or electrons with large OAM. Furthermore, we find that the cone opening angle and superposition state of the vortex $γ$ photon, distinct characteristics aside from its intrinsic OAM, can be determined via the angular distribution of created pairs in NBW processes. Our method paves the way for investigating strong-field quantum electrodynamics processes concerning the generation and detection of vortex particle beams in intense lasers.

hep-ph↗

arXiv2024

Time-Consistent Portfolio Selection for Rank-Dependent Utilities in an Incomplete Market

We investigate the portfolio selection problem for an agent with rank-dependent utility in an incomplete financial market. For a constant-coefficient market and CRRA utilities, we characterize the deterministic strict equilibrium strategies. In the case of time-invariant probability weighting function, we provide a comprehensive characterization of the deterministic strict equilibrium strategy. The unique non-zero equilibrium, if exists, can be determined by solving an autonomous ODE. In the case of time-variant probability weighting functions, we observe that there may be infinitely many non-zero deterministic strict equilibrium strategies, which are derived from the positive solutions to a nonlinear singular ODE. By specifying the maximal solution to the singular ODE, we are able to identify all the positive solutions. In addition, we address the issue of selecting an optimal strategy from the numerous equilibrium strategies available.

q-fin.MF↗

arXiv2024

Compact Efficient Polarizers for Relativistic Electron Beams

Relativistic spin-polarized electron beams are important for fundamental research and the industry, but their generation currently requires conventional accelerators or ultrastrong laser facilities, limiting their accessibility and broad applications. Here, we put forward a novel method for constructing a compact efficient "polarizer" that achieves direct ultrafast conversion of relativistic dense electron beams into polarized ones, based on the beam "self-polarization" mechanism via simple beam-target interactions. In this scheme, as the electron beam grazes through the polarizer (a double-layer solid target), it ionizes the target and excites an asymmetric plasma field due to the plasma backflows. This field then reacts on the beam itself, triggering spontaneous radiative polarization and reflection of the beam, and ultimately yielding a dense polarized electron beam. Moreover, the double-layer target setup induces a plasma bubble that focuses the polarized beam and reshapes its polarization distribution. Our method is robust with respect to the beam and target parameters, and opens a new avenue for relativistic beam polarization with compact accessible devices, which would facilitate their broad applications and the development of related experiments, such as in strong-field QED studies, and polarized electron-positron and electron-ion colliders.

physics.plasm-ph↗

arXiv2024

Deep learning-driven evaluation and prediction of ion-doped NASICON materials for enhanced solid-state battery performance

We developed a convolutional neural network (CNN) model capable of predicting the performance of various ion-doped NASICON compounds by leveraging extensive datasets from prior experimental investigation.The model demonstrated high accuracy and efficiency in predicting ionic conductivity and electrochemical properties. Key findings include the successful synthesis and validation of three NASICON materials predicted by the model, with experimental results closely matching the model predictions. This research not only enhances the understanding of ion-doping effects in NASICON materials but also establishes a robust framework for material design and practical applications. It bridges the gap between theoretical predictions and experimental validations.

cond-mat.mtrl-sci↗

arXiv2024

Crystal, ferromagnetism, and magnetoresistance with sign reversal in a EuAgP semiconductor

We synthesized the ferromagnetic EuAgP semiconductor and conducted a comprehensive study of its crystalline, magnetic, heat capacity, band gap, and magnetoresistance properties. Our investigation utilized a combination of X-ray diffraction, optical, and PPMS DynaCool measurements. EuAgP adopts a hexagonal structure with the $P6_3/mmc$ space group. As the temperature decreases, it undergoes a magnetic phase transition from high-temperature paramagnetism to low-temperature ferromagnetism. We determined the ferromagnetic transition temperature to be $T_{\textrm{C}} =$ 16.45(1) K by fitting the measured magnetic susceptibility using a Curie-Weiss law. Heat capacity analysis of EuAgP considered contributions from electrons, phonons, and magnons, revealing $η$ = 0.03 J/mol/$\textrm{K}^\textrm{2}$, indicative of semiconducting behavior. Additionally, we calculated a band gap of $\sim$ 1.324(4) eV based on absorption spectrum measurements. The resistivity versus temperature of EuAgP measured in the absence of an applied magnetic field shows a pronounced peak around $T_{\textrm{C}}$, which diminishes rapidly with increasing applied magnetic fields, ranging from 1 to 14 T. An intriguing phenomenon emerges in the form of a distinct magnetoresistance transition, shifting from positive (e.g., 1.95\% at 300 K and 14 T) to negative (e.g., -30.73\% at 14.25 K and 14 T) as the temperature decreases. This behavior could be attributed to spin-disordered scattering.

cond-mat.str-el↗

arXiv2024

Credibility Theory Based on Winsorizing

The classical Bühlmann credibility model has been widely applied to premium estimation for group insurance contracts and other insurance types. In this paper, we develop a robust Bühlmann credibility model using the winsorized version of loss data, also known as the winsorized mean (a robust alternative to the traditional individual mean). This approach assumes that the observed sample data come from a contaminated underlying model with a small percentage of contaminated sample data. This framework provides explicit formulas for the structural parameters in credibility estimation for scale-shape distribution families, location-scale distribution families, and their variants, commonly used in insurance risk modeling. Using the theory of $L$-estimators (different from the influence function approach), we derive the asymptotic properties of the proposed method and validate them through a comprehensive simulation study, comparing their performance to credibility based on the trimmed mean. By varying the winsorizing/trimming thresholds in several parametric models, we find that all structural parameters derived from the winsorized approach are less volatile than those from the trimmed approach. Using the winsorized mean as a robust risk measure can reduce the influence of parametric loss assumptions on credibility estimation. Additionally, we discuss non-parametric estimations in credibility. Finally, a numerical illustration from the Wisconsin Local Government Property Insurance Fund indicates that the proposed robust credibility approach mitigates the impact of model mis-specification and captures the risk behavior of loss data from a broader perspective.

stat.AP↗