arXiv subjects

Jinke Yu

Publications and source records attributed to Jinke Yu.

11 recordsLinked to original sources

arXiv2026

Implementation Possibility of Quantum Simulation for Quantum Molecular Dynamics

In this work, we explore the implementation possibility of quantum simulation for quantum molecular dynamics, in particular for reaction dynamics, though several implementations have already reported through quantum-classical mixed simulations ({\it Acc. Chem. Res.} {\bf 54} (2021), 4229 and {\it J. Phys. Chem. Lett.} {\bf xx} (2026), XXXX). To analyze this aspect, we examine (1) the conjugacy relation between quantum simulator and the target molecular system, (2) the wave function correspondence in quantum algorithm and classical algorithm for multi-dimensional dynamics, (3) problems arisen from real-valued classical algorithms, and finally (4) geometric phase arisen from the separation among the degrees of freedom (DOFs). As is well known, the aforementioned first and second points play fundamental roles in quantum simulation of quantum many-body systems, and the third and fourth points are theoretical issues that might introduce problems in classical and quantum computing. In this work, we mainly focus on the third and fourth points by analysis of the first two points by reviewing previously reported quantum-classical mixed implementations of quantum simulation. We also consider gauge freedom in high-dimensional quantum molecular dynamics that has been introduced recently, and then discuss possibility of advantages and disadvantages of quantum simulation for molecular reaction dynamics.

physics.chem-ph↗

arXiv2026

A Primary Unified Geometric Framework of Molecular Reaction Dynamics Based on the Variational Principle

This work describes a geometric framework on molecular reaction dynamics based on the variational principle, where the Schr{\"o}dinger equation must be solved to ``see'' how a reaction occurs. First, the mathematical preliminaries are given by discussing the principle of least action and the mountain pass theorem. Second, we discuss the physical preliminaries, including the principle of equivalence for deriving the kinetic energy operator (KEO) and artificial intelligence (AI) techniques to build the potential energy surface (PES) in general spacetime. Moreover, we simplified electromagnetic interactions in curved spacetime within the molecular system and consequently, we are able to construct the nuclear Hamiltonian in nonzero curvature spacetime. This indicates possibility to introduce gauge fields through the curvature, such as additional term in the nuclear KEO near a conical intersection. Third, the single-particle approximation provides a powful ansatz to solve the Schr{\"o}dinger equation by variational principle. Thus, one can formulate the variational approaches for either electronic structure or quantum dynamics. In this work, based on previous discussions ({\it Phys. Chem. Chem. Phys.} {\bf 27} (2025), 20397) we unified them by a geometric description, where the geometric phase is naturally introduced. Finally, due to optimization characteristic of the present theory, further discussions on the present theory from optimization insight are also given, including two postulates, generative AI techniques, role of perturbation, and Markov process in optimization.

physics.chem-ph↗

arXiv2026

DOne: Decoupling Structure and Rendering for High-Fidelity Design-to-Code Generation

While Vision Language Models (VLMs) have shown promise in Design-to-Code generation, they suffer from a "holistic bottleneck-failing to reconcile high-level structural hierarchy with fine-grained visual details, often resulting in layout distortions or generic placeholders. To bridge this gap, we propose DOne, an end-to-end framework that decouples structure understanding from element rendering. DOne introduces (1) a learned layout segmentation module to decompose complex designs, avoiding the limitations of heuristic cropping; (2) a specialized hybrid element retriever to handle the extreme aspect ratios and densities of UI components; and (3) a schema-guided generation paradigm that bridges layout and code. To rigorously assess performance, we introduce HiFi2Code, a benchmark featuring significantly higher layout complexity than existing datasets. Extensive evaluations on the HiFi2Code demonstrate that DOne outperforms exiting methods in both high-level visual similarity (e.g., over 10% in GPT Score) and fine-grained element alignment. Human evaluations confirm a 3 times productivity gain with higher visual fidelity.

cs.CV↗

arXiv2025

Layout-Aware Parsing Meets Efficient LLMs: A Unified, Scalable Framework for Resume Information Extraction and Evaluation

Automated resume information extraction is critical for scaling talent acquisition, yet its real-world deployment faces three major challenges: the extreme heterogeneity of resume layouts and content, the high cost and latency of large language models (LLMs), and the lack of standardized datasets and evaluation tools. In this work, we present a layout-aware and efficiency-optimized framework for automated extraction and evaluation that addresses all three challenges. Our system combines a fine-tuned layout parser to normalize diverse document formats, an inference-efficient LLM extractor based on parallel prompting and instruction tuning, and a robust two-stage automated evaluation framework supported by new benchmark datasets. Extensive experiments show that our framework significantly outperforms strong baselines in both accuracy and efficiency. In particular, we demonstrate that a fine-tuned compact 0.6B LLM achieves top-tier accuracy while significantly reducing inference latency and computational cost. The system is fully deployed in Alibaba's intelligent HR platform, supporting real-time applications across its business units.

cs.CL↗

arXiv2025

The $[3+1]$ Formulation of Chemical Dynamics in Curved Spacetime under the Eulerian Observer

Traditionally, gravity is generally considered to exert an extremely weak effect in chemistry because the Newtonian gravitation is typically negligible compared to the dominant Coulomb potentials in a molecular system. In this work, we porpose a primitive framework of chemical dynamics in curved spacetime through fiducial-observer $[3+1]$ formulation by revising the nuclear Hamiltonian operator through the metric tensor of configuration space rather than by adding Newtonian gravitation in the potential energy term, where the absolute-sapce and universal-time viewpoint of Galileo is adopted. Using frames fixed on normal observers in the $[3+1]$ formalism ensures possibility of this treatment. Taking spherically symmetric curved spacetime ({\it i.e.} Schwarzschild spacetime) as numerical demonstration, we explore (1) the H + H$_2$ reaction dynamics, (2) the H$_2$ + H$_2$ scattering dynamics, (3) dynamics of dissociative chemsorption of H$_2$O on Cu(111), (4) the spectrum band of anthracene cation, and (5) the Berry phase in the nuclear wave function of a 98D surface scattering model. These calculations predict that (i) reaction or scattering probability and (ii) spectrum band decrease abruptly to zero as the spacetime curvature increases; meanwhile, the geometric phase is unaffected by the spacetime curvature. Finally, discussions on these numerical results, together with perspectives on the applications of quantum field theory to chemical dynamics in curved spacetime are given.

physics.chem-ph↗

arXiv2025

A Multi-Electronic-State Model to Interpret the Apparent Anomalous Arrhenius Curve of OH + HO$_2$ $\to$ O$_2$ + H$_2$O

A comprehensive multi-electronic-state model for OH + HO2 -> O2 + H2O has been developed through extensive multi-reference configuration interaction (MRCI) calculations, aiming to elucidate two key experimental observations: (1) an unusually deep and narrow ``well'' in the Arrhenius curve near 1100K and (2) a slightly negative temperature dependence in the range of 200K~500K. Moreover, the present model can serve as the basis for constructing multi-state Hamiltonian in multi-dimensional quantum dynamics calculations. The present model incorporates eight state-to-state processes involving OH (X) + HO2 (X/A), where three of four processes associated with HO2 (X) are exothermic, while those associated with HO2 (A) have three endothermic channels with the smallest barrier of 0.107 eV. At temperatures below 500K, the processes of HO2 (A) remain inaccessible, and the dominance of exothermic pathways results in a temperature-independent rate constant. To enable HO2 excitation at temperatures above 900K, a black-body radiation model that facilitates endothermic processes is introduced and reproduces the reduction factor of 0.3711 at 1100K. This value falls within the experimentally observed range of 0.30~0.76. Furthermore, a recombination process involving HO2 (A) is proposed to provide additional reduction of the rate constant. In conjunction with our previous works (J. Chem. Phys. 152 (2020), 134309 and J. Chem. Theory Comput. 20 (2024), 597), predicted values of the rate constants are well agree with experiments. At elevated temperatures exceeding 1242K (namely >0.107eV), the overall rate constant becomes temperature-dependent due to the activation of endothermic processes, leading to a distinct well in the Arrhenius curve between 900K and 1242K.

physics.chem-ph↗

arXiv2025

Berry Phase Effects of Nuclei in Chemical Reaction Dynamics

In calculations on quantum state-resolved dynamics of a chemical reaction, reactants are usually prepared in separated eigenstates of individual fragments, and their direct-product is then evolved in time. In this work, we focus on the essence in separating them and the Berry phase effects of the nuclear wave function. By the present theory, mechanism of inter/intramolecular energy redistribution is also proposed to deeply understand reactive dynamics with multirovibrational states. To demonstrate the phase transition of the nuclear wave function, two three-dimensional (3D) models reductively describing the molecular reaction are developed to simulate transport of the system along a closed path in a parameter space represented of inter/intramolecular energy transfers. Employing these 3D models, extensive multiconfigurational time-dependent Hartree (MCTDH) calculations are performed to solve the time-dependent nuclear Schr{\"o}dinger equation at various initial conditions. Moreover, 98D multilayer MCTDH (ML-MCTDH) calculations are launched to demonstrate the transition of Berry phase. These calculations clearly indicate that the wave function can change sign allowing quantum interference in the parameter space. Discussions on the separation of the reactants are made, while perspectives on the Berry phase effects predicted by the present work are given from the viewpoint of differential geometry. As a conclusion remark, the Berry phase effects on molecular dynamics are also thoroughly compared with those on electronic properties (see, for example, {\it Rev. Mod. Phys.} {\bf 82} (2010), 1959) and mode/bond-specific reactivity (see, for example, {\it Nat. Chem.} {\bf 14} (2022), 545).

physics.chem-ph↗

arXiv2022

Perspective Reconstruction of Human Faces by Joint Mesh and Landmark Regression

Even though 3D face reconstruction has achieved impressive progress, most orthogonal projection-based face reconstruction methods can not achieve accurate and consistent reconstruction results when the face is very close to the camera due to the distortion under the perspective projection. In this paper, we propose to simultaneously reconstruct 3D face mesh in the world space and predict 2D face landmarks on the image plane to address the problem of perspective 3D face reconstruction. Based on the predicted 3D vertices and 2D landmarks, the 6DoF (6 Degrees of Freedom) face pose can be easily estimated by the PnP solver to represent perspective projection. Our approach achieves 1st place on the leader-board of the ECCV 2022 WCPA challenge and our model is visually robust under different identities, expressions and poses. The training code and models are released to facilitate future research.

cs.CV↗

arXiv2019

RetinaFace: Single-stage Dense Face Localisation in the Wild

Though tremendous strides have been made in uncontrolled face detection, accurate and efficient face localisation in the wild remains an open challenge. This paper presents a robust single-stage face detector, named RetinaFace, which performs pixel-wise face localisation on various scales of faces by taking advantages of joint extra-supervised and self-supervised multi-task learning. Specifically, We make contributions in the following five aspects: (1) We manually annotate five facial landmarks on the WIDER FACE dataset and observe significant improvement in hard face detection with the assistance of this extra supervision signal. (2) We further add a self-supervised mesh decoder branch for predicting a pixel-wise 3D shape face information in parallel with the existing supervised branches. (3) On the WIDER FACE hard test set, RetinaFace outperforms the state of the art average precision (AP) by 1.1% (achieving AP equal to 91.4%). (4) On the IJB-C test set, RetinaFace enables state of the art methods (ArcFace) to improve their results in face verification (TAR=89.59% for FAR=1e-6). (5) By employing light-weight backbone networks, RetinaFace can run real-time on a single CPU core for a VGA-resolution image. Extra annotations and code have been made available at: https://github.com/deepinsight/insightface/tree/master/RetinaFace.

cs.CV↗

arXiv2019

PFLD: A Practical Facial Landmark Detector

Being accurate, efficient, and compact is essential to a facial landmark detector for practical use. To simultaneously consider the three concerns, this paper investigates a neat model with promising detection accuracy under wild environments e.g., unconstrained pose, expression, lighting, and occlusion conditions) and super real-time speed on a mobile device. More concretely, we customize an end-to-end single stage network associated with acceleration techniques. During the training phase, for each sample, rotation information is estimated for geometrically regularizing landmark localization, which is then NOT involved in the testing phase. A novel loss is designed to, besides considering the geometrical regularization, mitigate the issue of data imbalance by adjusting weights of samples to different states, such as large pose, extreme lighting, and occlusion, in the training set. Extensive experiments are conducted to demonstrate the efficacy of our design and reveal its superior performance over state-of-the-art alternatives on widely-adopted challenging benchmarks, i.e., 300W (including iBUG, LFPW, AFW, HELEN, and XM2VTS) and AFLW. Our model can be merely 2.1Mb of size and reach over 140 fps per face on a mobile phone (Qualcomm ARM 845 processor) with high precision, making it attractive for large-scale or real-time applications. We have made our practical system based on PFLD 0.25X model publicly available at \url{http://sites.google.com/view/xjguo/fld} for encouraging comparisons and improvements from the community.

cs.CV↗

arXiv2018

Fast Single Image Rain Removal via a Deep Decomposition-Composition Network

Rain effect in images typically is annoying for many multimedia and computer vision tasks. For removing rain effect from a single image, deep leaning techniques have been attracting considerable attentions. This paper designs a novel multi-task leaning architecture in an end-to-end manner to reduce the mapping range from input to output and boost the performance. Concretely, a decomposition net is built to split rain images into clean background and rain layers. Different from previous architectures, our model consists of, besides a component representing the desired clean image, an extra component for the rain layer. During the training phase, we further employ a composition structure to reproduce the input by the separated clean image and rain information for improving the quality of decomposition. Experimental results on both synthetic and real images are conducted to reveal the high-quality recovery by our design, and show its superiority over other state-of-the-art methods. Furthermore, our design is also applicable to other layer decomposition tasks like dust removal. More importantly, our method only requires about 50ms, significantly faster than the competitors, to process a testing image in VGA resolution on a GTX 1080 GPU, making it attractive for practical use.

cs.CV↗