arXiv subjects

Bowen Liu

Publications and source records attributed to Bowen Liu.

At least 91 records · Page 5Linked to original sources

arXiv2024

CTA-Net: A CNN-Transformer Aggregation Network for Improving Multi-Scale Feature Extraction

Convolutional neural networks (CNNs) and vision transformers (ViTs) have become essential in computer vision for local and global feature extraction. However, aggregating these architectures in existing methods often results in inefficiencies. To address this, the CNN-Transformer Aggregation Network (CTA-Net) was developed. CTA-Net combines CNNs and ViTs, with transformers capturing long-range dependencies and CNNs extracting localized features. This integration enables efficient processing of detailed local and broader contextual information. CTA-Net introduces the Light Weight Multi-Scale Feature Fusion Multi-Head Self-Attention (LMF-MHSA) module for effective multi-scale feature integration with reduced parameters. Additionally, the Reverse Reconstruction CNN-Variants (RRCV) module enhances the embedding of CNNs within the transformer architecture. Extensive experiments on small-scale datasets with fewer than 100,000 samples show that CTA-Net achieves superior performance (TOP-1 Acc 86.76\%), fewer parameters (20.32M), and greater efficiency (FLOPs 2.83B), making it a highly efficient and lightweight solution for visual tasks on small-scale datasets (fewer than 100,000).

cs.CV↗

arXiv2024

ADRS-CNet: An adaptive dimensionality reduction selection and classification network for DNA storage clustering algorithms

DNA storage technology offers new possibilities for addressing massive data storage due to its high storage density, long-term preservation, low maintenance cost, and compact size. To improve the reliability of stored information, base errors and missing storage sequences are challenges that must be faced. Currently, clustering and comparison of sequenced sequences are employed to recover the original sequence information as much as possible. Nonetheless, extracting DNA sequences of different lengths as features leads to the curse of dimensionality, which needs to be overcome. To address this, techniques like PCA, UMAP, and t-SNE are commonly employed to project high-dimensional features into low-dimensional space. Considering that these methods exhibit varying effectiveness in dimensionality reduction when dealing with different datasets, this paper proposes training a multilayer perceptron model to classify input DNA sequence features and adaptively select the most suitable dimensionality reduction method to enhance subsequent clustering results. Through testing on open-source datasets and comparing our approach with various baseline methods, experimental results demonstrate that our model exhibits superior classification performance and significantly improves clustering outcomes. This displays that our approach effectively mitigates the impact of the curse of dimensionality on clustering models.

cs.LG↗

arXiv2024

Linear stability of the elliptic relative equilibria for the restricted N-body problem: two special cases

In this paper, we consider the elliptic relative equilibria of the restricted $N$-body problems, where the $N-1$ primaries form an Euler-Moulton collinear central configuration or a $(1+n)$-gon central configuration. We obtain the symplectic reduction to the general restricted $N$-body problem. For the first case, by analyzing the relationship between this restricted $N$-body problems and the elliptic Lagrangian solutions, we obtain the linear stability of the restricted $N$-body problem by the $ω$-Maslov index. Via numerical computations, we also obtain conditions of the stability on the mass parameters under $N=4$ and the symmetry of the central configuration. For the second case, there exist three positions $S_1,S_2$ and $S_3$ of the massless body (up to rotations of angle $\frac{2π}{n}$). For ${m_0\over m}$ sufficiently large, we show that the elliptic relative equilibria is linearly unstable if the eccentricity $0\le e<e_0$ and the massless body lies at $S_1$ or $S_2$; while the elliptic relative equilibria is linear stability if the massless body lies at $S_3$.

math.DS↗

arXiv2024

Fine-grained Classification of Port Wine Stains Using Optical Coherence Tomography Angiography

Accurate classification of port wine stains (PWS, vascular malformations present at birth), is critical for subsequent treatment planning. However, the current method of classifying PWS based on the external skin appearance rarely reflects the underlying angiopathological heterogeneity of PWS lesions, resulting in inconsistent outcomes with the common vascular-targeted photodynamic therapy (V-PDT) treatments. Conversely, optical coherence tomography angiography (OCTA) is an ideal tool for visualizing the vascular malformations of PWS. Previous studies have shown no significant correlation between OCTA quantitative metrics and the PWS subtypes determined by the current classification approach. This study proposes a new classification approach for PWS using both OCT and OCTA. By examining the hypodermic histopathology and vascular structure of PWS, we have devised a fine-grained classification method that subdivides PWS into five distinct types. To assess the angiopathological differences of various PWS subtypes, we have analyzed six metrics related to vascular morphology and depth information of PWS lesions. The five PWS types present significant differences across all metrics compared to the conventional subtypes. Our findings suggest that an angiopathology-based classification accurately reflects the heterogeneity in PWS lesions. This research marks the first attempt to classify PWS based on angiopathology, potentially guiding more effective subtyping and treatment strategies for PWS.

eess.IV↗

arXiv2024

YOLO-pdd: A Novel Multi-scale PCB Defect Detection Method Using Deep Representations with Sequential Images

With the rapid growth of the PCB manufacturing industry, there is an increasing demand for computer vision inspection to detect defects during production. Improving the accuracy and generalization of PCB defect detection models remains a significant challenge. This paper proposes a high-precision, robust, and real-time end-to-end method for PCB defect detection based on deep Convolutional Neural Networks (CNN). Traditional methods often suffer from low accuracy and limited applicability. We propose a novel approach combining YOLOv5 and multiscale modules for hierarchical residual-like connections. In PCB defect detection, noise can confuse the background and small targets. The YOLOv5 model provides a strong foundation with its real-time processing and accurate object detection capabilities. The multi-scale module extends traditional approaches by incorporating hierarchical residual-like connections within a single block, enabling multiscale feature extraction. This plug-and-play module significantly enhances performance by extracting features at multiple scales and levels, which are useful for identifying defects of varying sizes and complexities. Our multi-scale architecture integrates feature extraction, defect localization, and classification into a unified network. Experiments on a large-scale PCB dataset demonstrate significant improvements in precision, recall, and F1-score compared to existing methods. This work advances computer vision inspection for PCB defect detection, providing a reliable solution for high-precision, robust, real-time, and domain-adaptive defect detection in the PCB manufacturing industry.

cs.CV↗

arXiv2024

783-MHz fundamental repetition rate all-fiber ring laser mode-locked by carbon nanotubes

We demonstrate a 783-MHz fundamental repetition rate mode-locked Er-doped all-fiber ring laser with a pulse width of 623 fs. By using carbon nanotubes (CNT) saturable absorber (SA), a relatively low self-starting pump threshold of 108 mW is achieved. The laser has a very compact footprint less than 10 cm * 10 cm, benefiting from the all-active-fiber cavity design. The robust mode-locking is confirmed by the low relative intensity noise (RIN) and a long-term stability test. We propose a new scheme for generating high repetition rate femtosecond optical pulses from a compact and stable all-active-fiber ring oscillator.

physics.optics↗

arXiv2024

Mode-Locked Fiber Laser with up to 19 kHz Wavelength Sweep Rate via External Pump LD Modulation

For the first time, we introduce a rapid wavelength-swept, passively mode-locked fiber laser in an all-polarization-maintaining and all-fiber configuration. Achieving an exceptional wavelength sweep rate of up to 19 kHz through external modulation of the LD driver pump current, this laser offers a high sweep rate, simple cavity design, cost-effectiveness, and excellent repeatability.

physics.optics↗

arXiv2024

Temperature dependent multi-pulse threshold due to SWCNT/PDMS saturable absorber

The threshold pump power for modelocking decreased by 18% when the temperature was increased from 25 to 100 degrees C, where a SWCNT/PDMS coated tapered fiber was used as the saturable absorber in a fiber laser. Further, the pump power at which multi-pulse operation began decreased by 24%, and the pump power range over which fundamental modelocking could be maintained decreased by 59% over the same temperature range. This decrease in stability is attributed to the large thermo-optic coefficient of the PDMS polymer, which results in a 40% reduction of the overlap between the evanescent field and SWCNT coating of the taper fiber over a temperature range of 75 degrees C.

physics.optics↗

arXiv2024

Real-fluid Transport Property Computations Based on the Boltzmann-weighted Full-dimensional Potential Model

The intermolecular potential plays crucial roles in real-fluid interactions away from the ideal-gas equilibrium, such as supercritical fluid, high-enthalpy fluid, plasma interactions, etc. We propose a Boltzmann-weighted Full-dimensional (BWF) potential model for real-fluid computations. It includes diverse intermolecular interactions so as to determine the potential well, molecular diameter, dipole moment, polarizability of species without introducing bath gases, allowing more accurate descriptions of potential surfaces with more potential parameters. The anisotropy and temperature dependence of potential parameters are also considered by applying the Boltzmann weighting on all orientations. Through the high-level Symmetry-Adapted Perturbation Theory calculations, full-dimensional potential energy surface datasets are obtained in 432 orientations for each species. Subsequently, the Boltzmann-weighted Full-dimensional potential parameters are derived by training the dataset exceeding 5*106 data, including nonpolar and polar molecules, radicals, long-chain molecules, and ions. These BWF transport properties calculated by the BWF potential have been compared against the Lennard-Jones transport properties as well as experimental viscosity, mass diffusivity, and thermal conductivity coefficients. It shows discrepancies of viscosity coefficients within 1% and 5% for nonpolar and polar molecules, respectively. Furthermore, this potential model is applied to study radicals, long-chain molecules, and ions, for which the experimental data is rarely accessed in high accuracy. It indicates significant prediction improvements of complex interactions between various particles. The new transport properties are also embedded to predict the laminar flame speeds and the flame extinction limits of methane, dimethyl ether, and n-heptane at elevated pressures, confirming its predictivity and effectiveness.

physics.app-ph↗

arXiv2024

Rapid-scanned and self-corrected repetition rates enabled in a bidirectional polarization-multiplexed fiber laser

Repetition-rate-scanned lasers are practical in accordion frequency comb generation that serves as a variable gearbox connecting optical and radio wave domains. Rapid and wide-range scanned repetition rate can benefit versatile purposes, however scanning robustness remains unsecured that typically requires complicated feedback loops. Recently, multiplexed lasers have been demonstrated with the nature of common-noise rejection among simultaneously emitted combs. Here, we propose a bidirectional polarization-multiplexed fiber laser that delivers synchronized pulses with rapid-scanned and reference-free repetition rates. Benefiting from the all polarization-maintaining fiber configuration, the laser shows good robustness and inter-comb coherence. As rapid as 493.5 kHz/s scanning rate over 329-kHz scanning range of fundamental repetition rate is realized. The 1-hour and 1-day maximal variations of difference frequency are merely 0.52 Hz and 5.46 Hz. The capability to rebuilt steady state after mode hopping is also demonstrated. These results provide a promising solution for developing high-performance accordion-frequency laser sources.

physics.optics↗

arXiv2024

Efficient molecular conformation generation with quantum-inspired algorithm

Conformation generation, also known as molecular unfolding (MU), is a crucial step in structure-based drug design, remaining a challenging combinatorial optimization problem. Quantum annealing (QA) has shown great potential for solving certain combinatorial optimization problems over traditional classical methods such as simulated annealing (SA). However, a recent study showed that a 2000-qubit QA hardware was still unable to outperform SA for the MU problem. Here, we propose the use of quantum-inspired algorithm to solve the MU problem, in order to go beyond traditional SA. We introduce a highly-compact phase encoding method which can exponentially reduce the representation space, compared with the previous one-hot encoding method. For benchmarking, we tested this new approach on the public QM9 dataset generated by density functional theory (DFT). The root-mean-square deviation between the conformation determined by our approach and DFT is negligible (less than about 0.5 Angstrom), which underpins the validity of our approach. Furthermore, the median time-to-target metric can be reduced by a factor of five compared to SA. Additionally, we demonstrate a simulation experiment by MindQuantum using quantum approximate optimization algorithm (QAOA) to reach optimal results. These results indicate that quantum-inspired algorithms can be applied to solve practical problems even before quantum hardware become mature.

quant-ph↗

arXiv2024

Quantum molecular docking with quantum-inspired algorithm

Molecular docking (MD) is a crucial task in drug design, which predicts the position, orientation, and conformation of the ligand when bound to a target protein. It can be interpreted as a combinatorial optimization problem, where quantum annealing (QA) has shown promising advantage for solving combinatorial optimization. In this work, we propose a novel quantum molecular docking (QMD) approach based on QA-inspired algorithm. We construct two binary encoding methods to efficiently discretize the degrees of freedom with exponentially reduced number of bits and propose a smoothing filter to rescale the rugged objective function. We propose a new quantum-inspired algorithm, hopscotch simulated bifurcation (hSB), showing great advantage in optimizing over extremely rugged energy landscapes. This hSB can be applied to any formulation of objective function under binary variables. An adaptive local continuous search is also introduced for further optimization of the discretized solution from hSB. Concerning the stability of docking, we propose a perturbation detection method to help ranking the candidate poses. We demonstrate our approach on a typical dataset. QMD has shown advantages over the search-based Autodock Vina and the deep-learning DIFFDOCK in both re-docking and self-docking scenarios. These results indicate that quantum-inspired algorithms can be applied to solve practical problems in the drug discovery even before quantum hardware become mature.

physics.chem-ph↗

arXiv2024

VisionCLIP: An Med-AIGC based Ethical Language-Image Foundation Model for Generalizable Retina Image Analysis

Generalist foundation model has ushered in newfound capabilities in medical domain. However, the contradiction between the growing demand for high-quality annotated data with patient privacy continues to intensify. The utilization of medical artificial intelligence generated content (Med-AIGC) as an inexhaustible resource repository arises as a potential solution to address the aforementioned challenge. Here we harness 1 million open-source synthetic fundus images paired with natural language descriptions, to curate an ethical language-image foundation model for retina image analysis named VisionCLIP. VisionCLIP achieves competitive performance on three external datasets compared with the existing method pre-trained on real-world data in a zero-shot fashion. The employment of artificially synthetic images alongside corresponding textual data for training enables the medical foundation model to successfully assimilate knowledge of disease symptomatology, thereby circumventing potential breaches of patient confidentiality.

cs.CV↗

arXiv2024

Category-Agnostic Pose Estimation for Point Clouds

The goal of object pose estimation is to visually determine the pose of a specific object in the RGB-D input. Unfortunately, when faced with new categories, both instance-based and category-based methods are unable to deal with unseen objects of unseen categories, which is a challenge for pose estimation. To address this issue, this paper proposes a method to introduce geometric features for pose estimation of point clouds without requiring category information. The method is based only on the patch feature of the point cloud, a geometric feature with rotation invariance. After training without category information, our method achieves as good results as other category-based methods. Our method successfully achieved pose annotation of no category information instances on the CAMERA25 dataset and ModelNet40 dataset.

cs.CV↗

arXiv2024

Quantum-Inspired Machine Learning for Molecular Docking

Molecular docking is an important tool for structure-based drug design, accelerating the efficiency of drug development. Complex and dynamic binding processes between proteins and small molecules require searching and sampling over a wide spatial range. Traditional docking by searching for possible binding sites and conformations is computationally complex and results poorly under blind docking. Quantum-inspired algorithms combining quantum properties and annealing show great advantages in solving combinatorial optimization problems. Inspired by this, we achieve an improved in blind docking by using quantum-inspired combined with gradients learned by deep learning in the encoded molecular space. Numerical simulation shows that our method outperforms traditional docking algorithms and deep learning-based algorithms over 10\%. Compared to the current state-of-the-art deep learning-based docking algorithm DiffDock, the success rate of Top-1 (RMSD<2) achieves an improvement from 33\% to 35\% in our same setup. In particular, a 6\% improvement is realized in the high-precision region(RMSD<1) on molecules data unseen in DiffDock, which demonstrates the well-generalized of our method.

physics.chem-ph↗

arXiv2024

Pump-power-controlled L-band wavelength-tunable mode-locked fiber laser utilizing all polarization maintaining nonlinear polarization rotation

For the first time, we present the pump power-controlled wavelength-tunable mode-locked fiber laser in the L-band (1565 nm to 1625 nm), achieved by all-polarization maintaining (all-PM) nonlinear polarization rotation (NPR). The wavelength of the laser can be tuned over 20 nm, from 1568.2 nm to 1588.9 nm simply by controlling the pump power from 45 mW to 115 mW. In contrast to conventional wavelength tuning mechanisms such as optical bandpass filters, our tuning method is non-mechanical and electrically controllable, featuring simplicity and cost-effectiveness in a superior all-fiber design.

physics.optics↗

arXiv2023

The application of accumulation tests in Peaks-Over-Threshold modeling with Norwegian Fire insurance Data

Modeling excess remains to be an important topic in insurance data modeling. Among the alternatives of modeling excess, the Peaks Over Threshold (POT) framework with Generalized Pareto distribution (GPD) is regarded as an efficient approach due to its flexibility. However, the selection of an appropriate threshold for such framework is a major difficulty. To address such difficulty, we applied several accumulation tests along with Anderson-Darling test to determine an optimal threshold. Based on the selected thresholds, the fitted GPD with the estimated quantiles can be found. We applied the procedure to the well-known Norwegian Fire Insurance data and constructed the confidence intervals for the Value-at-Risks (VaR). The accumulation test approach provides satisfactory performance in modeling the high quantiles of Norwegian Fire Insurance data compared to the previous graphical methods.

stat.AP↗

arXiv2023

TrTr: A Versatile Pre-Trained Large Traffic Model based on Transformer for Capturing Trajectory Diversity in Vehicle Population

Understanding trajectory diversity is a fundamental aspect of addressing practical traffic tasks. However, capturing the diversity of trajectories presents challenges, particularly with traditional machine learning and recurrent neural networks due to the requirement of large-scale parameters. The emerging Transformer technology, renowned for its parallel computation capabilities enabling the utilization of models with hundreds of millions of parameters, offers a promising solution. In this study, we apply the Transformer architecture to traffic tasks, aiming to learn the diversity of trajectories within vehicle populations. We analyze the Transformer's attention mechanism and its adaptability to the goals of traffic tasks, and subsequently, design specific pre-training tasks. To achieve this, we create a data structure tailored to the attention mechanism and introduce a set of noises that correspond to spatio-temporal demands, which are incorporated into the structured data during the pre-training process. The designed pre-training model demonstrates excellent performance in capturing the spatial distribution of the vehicle population, with no instances of vehicle overlap and an RMSE of 0.6059 when compared to the ground truth values. In the context of time series prediction, approximately 95% of the predicted trajectories' speeds closely align with the true speeds, within a deviation of 7.5144m/s. Furthermore, in the stability test, the model exhibits robustness by continuously predicting a time series ten times longer than the input sequence, delivering smooth trajectories and showcasing diverse driving behaviors. The pre-trained model also provides a good basis for downstream fine-tuning tasks. The number of parameters of our model is over 50 million.

cs.AI↗