arXiv subjects

Yiming Ma

Publications and source records attributed to Yiming Ma.

46 records · Page 3Linked to original sources

arXiv2023

Deformed relativistic Hartree-Bogoliubov theory in continuum with a point-coupling functional. II. Examples of odd Nd isotopes

The aim of this work is to extend the deformed relativistic Hartree-Bogoliubov theory in continuum (DRHBc) based on the point-coupling density functionals to odd-$A$ and odd-odd nuclei and examine its applicability by taking odd-$A$ Nd isotopes as examples. In the DRHBc theory, the densities and potentials with axial deformation are expanded in terms of Legendre polynomials, and the relativistic Hartree-Bogoliubov equations are solved in a Dirac Woods-Saxon basis to include the continuum effects. For an odd-$A$ or odd-odd nucleus, the blocking effect of unpaired nucleon(s) is taken into account with the equal filling approximation. To determine its ground state, an automatic blocking procedure is adopted, in which the orbital with the lowest quasiparticle energy is blocked during the iteration. This procedure is justified by comparing with the results from the orbital-fixed blocking calculations, in which the blocked orbital near the Fermi surface is fixed during the iteration. The ground states for both light and heavy nuclei can be provided by the automatic blocking procedure as the orbital-fixed blocking procedure, but with considerably reduced computational cost. The numerical details for even-even nuclei are found to be valid for odd-$A$ and odd-odd nuclei as well. Taking Nd isotopes including both even-even and odd-$A$ ones as examples, the calculated ground-state properties with PC-PK1 are in good agreement with the available experimental data. This work paves the way to construct the DRHBc mass table including all even-even, odd-$A$ and odd-odd nuclei in the nuclear chart.

nucl-th↗

arXiv2023

Robust Multiview Multimodal Driver Monitoring System Using Masked Multi-Head Self-Attention

Driver Monitoring Systems (DMSs) are crucial for safe hand-over actions in Level-2+ self-driving vehicles. State-of-the-art DMSs leverage multiple sensors mounted at different locations to monitor the driver and the vehicle's interior scene and employ decision-level fusion to integrate these heterogenous data. However, this fusion method may not fully utilize the complementarity of different data sources and may overlook their relative importance. To address these limitations, we propose a novel multiview multimodal driver monitoring system based on feature-level fusion through multi-head self-attention (MHSA). We demonstrate its effectiveness by comparing it against four alternative fusion strategies (Sum, Conv, SE, and AFF). We also present a novel GPU-friendly supervised contrastive learning framework SuMoCo to learn better representations. Furthermore, We fine-grained the test split of the DAD dataset to enable the multi-class recognition of drivers' activities. Experiments on this enhanced database demonstrate that 1) the proposed MHSA-based fusion method (AUC-ROC: 97.0\%) outperforms all baselines and previous approaches, and 2) training MHSA with patch masking can improve its robustness against modality/view collapses. The code and annotations are publicly available.

cs.CV↗

arXiv2023

Nonparametric approaches for analyzing carbon emission: from statistical and machine learning perspectives

Linear regression models, especially the extended STIRPAT model, are routinely-applied for analyzing carbon emissions data. However, since the relationship between carbon emissions and the influencing factors is complex, fitting a simple parametric model may not be an ideal solution. This paper investigated various nonparametric approaches in statistics and machine learning (ML) for modeling carbon emissions data, including kernel regression, random forest and neural network. We selected data from ten Chinese cities from 2005 to 2019 for modeling studies. We found that neural network had the best performance in both fitting and prediction accuracy, which implies its capability of expressing the complex relationships between carbon emissions and the influencing factors. This study provides a new means for quantitative modeling of carbon emissions research that helps to understand how to characterize urban carbon emissions and to propose policy recommendations for "carbon reduction". In addition, we used the carbon emissions data of Wuhu city as an example to illustrate how to use this new approach.

stat.AP↗

arXiv2022

Inception-Based Crowd Counting -- Being Fast while Remaining Accurate

Recent sophisticated CNN-based algorithms have demonstrated their extraordinary ability to automate counting crowds from images, thanks to their structures which are designed to address the issue of various head scales. However, these complicated architectures also increase computational complexity enormously, making real-time estimation implausible. Thus, in this paper, a new method, based on Inception-V3, is proposed to reduce the amount of computation. This proposed approach (ICC), exploits the first five inception blocks and the contextual module designed in CAN to extract features at different receptive fields, thereby being context-aware. The employment of these two different strategies can also increase the model's robustness. Experiments show that ICC can at best reduce 85.3 percent calculations with 24.4 percent performance loss. This high efficiency contributes significantly to the deployment of crowd counting models in surveillance systems to guard the public safety. The code will be available at https://github.com/YIMINGMA/CrowdCounting-ICC,and its pre-trained weights on the Crowd Counting dataset, which comprises a large variety of scenes from surveillance perspectives, will also open-sourced.

cs.CV↗

arXiv2022

Real-Time Driver Monitoring Systems through Modality and View Analysis

Driver distractions are known to be the dominant cause of road accidents. While monitoring systems can detect non-driving-related activities and facilitate reducing the risks, they must be accurate and efficient to be applicable. Unfortunately, state-of-the-art methods prioritize accuracy while ignoring latency because they leverage cross-view and multimodal videos in which consecutive frames are highly similar. Thus, in this paper, we pursue time-effective detection models by neglecting the temporal relation between video frames and investigate the importance of each sensing modality in detecting drives' activities. Experiments demonstrate that 1) our proposed algorithms are real-time and can achieve similar performances (97.5\% AUC-PR) with significantly reduced computation compared with video-based models; 2) the top view with the infrared channel is more informative than any other single modality. Furthermore, we enhance the DAD dataset by manually annotating its test set to enable multiclassification. We also thoroughly analyze the influence of visual sensor types and their placements on the prediction of each class. The code and the new labels will be released.

cs.CV↗

arXiv2022

MHD Simulations on Magnetic Compression of Field Reversed Configurations

The magnetic compression has long been proposed a promising method for the plasma heating in a field reversed configuration (FRC), however, it remains a challenge to fully understand the physical mechanisms underlying the compression process, due to its highly dynamic nature beyond the one-dimensional (1D) adiabatic theory model [R. L. Spencer et al., Phys. Fluids 26, 1564 (1983)]. In this work, magnetohydrodynamics (MHD) simulations on the magnetic compression of FRCs using the NIMROD code [C. R. Sovinec et al., J. Comput. Phys. 195, 355 (2004)] and their comparisons with the 1D theory have been performed. The effects of the assumptions of the theory on the compression process have been explored, and the detailed profiles of the FRC during compression have been investigated. The pressure evolution agrees with the theoretical prediction under various initial conditions. The axial contraction of the FRC can be affected by the initial density profile and the ramping rate of the compression magnetic field, but the theoretical predictions on the FRC's length in general and the relation $r_s=\sqrt{2}r_o$ in particular hold approximately well during the whole compression process, where $r_s$ is the major radius of FRC separatrix and $r_o$ is that of the magnetic axis. The evolutions of the density and temperature can be affected significantly by the initial equilibrium profile and the ramping rate of the compression magnetic field. During the compression, the major radius of the FRC is another parameter that is susceptible to the ramping rate of the compression field. Basically, for the same magnetic compression ratio, the peak density is higher and the FRC's radius $r_s$ is smaller than the theoretical predictions.

physics.plasm-ph↗

arXiv2022

FusionCount: Efficient Crowd Counting via Multiscale Feature Fusion

State-of-the-art crowd counting models follow an encoder-decoder approach. Images are first processed by the encoder to extract features. Then, to account for perspective distortion, the highest-level feature map is fed to extra components to extract multiscale features, which are the input to the decoder to generate crowd densities. However, in these methods, features extracted at earlier stages during encoding are underutilised, and the multiscale modules can only capture a limited range of receptive fields, albeit with considerable computational cost. This paper proposes a novel crowd counting architecture (FusionCount), which exploits the adaptive fusion of a large majority of encoded features instead of relying on additional extraction components to obtain multiscale features. Thus, it can cover a more extensive scope of receptive field sizes and lower the computational cost. We also introduce a new channel reduction block, which can extract saliency information during decoding and further enhance the model's performance. Experiments on two benchmark databases demonstrate that our model achieves state-of-the-art results with reduced computational complexity.

cs.CV↗

arXiv2021

Realization of epitaxial thin films of the superconductor K-doped BaFe$_\text{2}$As$_\text{2}$

The iron-based superconductor Ba$_{1-x}$K$_x$Fe$_\text{2}$As$_\text{2}$ is emerging as a key material for high magnetic field applications owing to the recent developments in superconducting wires and bulk permanent magnets. Epitaxial thin films play important roles in investigating and artificially tuning physical properties; nevertheless, the synthesis of Ba$_{1-x}$K$_x$Fe$_2$As$_2$ epitaxial thin films remained challenging because of the high volatility of K. Herein, we report the successful growth of epitaxial Ba$_{1-x}$K$_x$Fe$_\text{2}$As$_\text{2}$ thin films by molecular-beam epitaxy with employing a combination of fluoride substrates (CaF$_\text{2}$, SrF$_\text{2}$, and BaF$_\text{2}$) and a low growth temperature (350$-$420$^\circ$C). Our epitaxial thin film grown on CaF$_\text{2}$ showed sharp superconducting transition at an onset critical temperature of 36 K, slightly lower than bulk crystals by ~2 K due presumably to the strain effect arising from the lattice and thermal expansion mismatch. Critical current density ($J$$_\text{c}$) determined by the magnetization hysteresis loop is as high as 2.2 MA/cm$^\text{2}$ at 4 K under self-field. In-field $J$$_\text{c}$ characteristics of the film are superior to the bulk crystals. The realization of epitaxial thin films opens opportunities for tuning superconducting properties by epitaxial strain and revealing intrinsic grain boundary transport of Ba$_{1-x}$K$_x$Fe$_\text{2}$As$_\text{2}$.

cond-mat.supr-con↗

arXiv2020

Optimal codes with small constant weight in $\ell_1$-metric

Motivated by the duplication-correcting problem for data storage in live DNA, we study the construction of constant-weight codes in $\ell_1$-metric. By using packings and group divisible designs in combinatorial design theory, we give constructions of optimal codes over non-negative integers and optimal ternary codes with $\ell_1$-weight $w\leq 4$ for all possible distances. In general, we derive the size of the largest ternary code with constant weight $w$ and distance $2w-2$ for sufficiently large length $n$ satisfying $n\equiv 1,w,-w+2,-2w+3\pmod{w(w-1)}$.

cs.IT↗

arXiv2019

Entity Personalized Talent Search Models with Tree Interaction Features

Talent Search systems aim to recommend potential candidates who are a good match to the hiring needs of a recruiter expressed in terms of the recruiter's search query or job posting. Past work in this domain has focused on linear and nonlinear models which lack preference personalization in the user-level due to being trained only with globally collected recruiter activity data. In this paper, we propose an entity-personalized Talent Search model which utilizes a combination of generalized linear mixed (GLMix) models and gradient boosted decision tree (GBDT) models, and provides personalized talent recommendations using nonlinear tree interaction features generated by the GBDT. We also present the offline and online system architecture for the productionization of this hybrid model approach in our Talent Search systems. Finally, we provide offline and online experiment results benchmarking our entity-personalized model with tree interaction features, which demonstrate significant improvements in our precision metrics compared to globally trained non-personalized models.

cs.AI↗