arXiv subjects

Wenzhong Liu

Publications and source records attributed to Wenzhong Liu.

18 recordsLinked to original sources

arXiv2026

An Integrated Hardware-Software Design for Low-Data Spatial Defect Detection in Robotic Visual Inspection with Hybrid Optoelectronic Neural Networks

To address data overload and inefficient shape-level annotation in robotic visual inspection, this paper proposes a hardware-software integrated optoelectronic architecture. A non-imaging, low-data paradigm is established to minimize annotation dependency. First, a sensor-in-the-loop strategy reconfigures a Digital Micromirror Device (DMD) as a physical optical convolutional layer, enabling photonic-domain feature extraction that unifies sensing hardware and processing software. To suppress data volume at the source, a block-based compressed sensing strategy encodes spatial information into low-dimensional temporal signals, drastically reducing redundancy. Subsequently, to bypass laborious manual defect shape annotation, natural language descriptions guide the network to align with highly generalizable features from Contrastive Language-Image Pre-training (CLIP), steering the attention maps of the optoelectronic neural network toward defect shapes. Furthermore, a Localization Accuracy for Attention (LAA) metric is proposed to quantify shape-level defect localization performance. Experiments on transparent material defect detection validate the system's effectiveness. Parametric analysis reveals how measurement matrices, compression ratios, and block sizes affect accuracy. Results show that, compared to traditional imaging, the proposed architecture maintains equivalent accuracy while reducing data volume by 90% for Vision Transformers and computational workload by 60% for Convolutional Neural Networks. This low-data paradigm offers an efficient solution for industrial automation scenarios involving massive data streams, high acquisition costs, or constrained edge resources.

cs.RO↗

arXiv2026

The point-thicknesses of complete multipartite graphs

The point-thickness $θ'(G)$ of a graph $G$ is the minimum number of subsets into which the vertex set $V(G)$ of $G$ is partitioned such that each subset induces a planar subgraph. In this paper, we determine the point-thickness of complete multipartite graphs. As a special case, we also obtain the point-thickness of complete graphs.

math.CO↗

arXiv2025

High-Precision Temperature Estimation Based on Magnetic Nanoparticles Dominated by Brownian Relaxation under Combined AC and DC Magnetic Fields

Brownian relaxation is one of the primary mechanisms that allows magnetic nanoparticles (MNPs) to convert magnetic energy into thermal energy under an excitation magnetic field. Accurately characterizing the MNPs' magnetization dynamics dominated by Brownian relaxation is crucial for achieving high-precision temperature estimation. However, the lack of a readily applicable analytical expression remains a major obstacle to the advancement of magnetic nanoparticle hyperthermia (MNPH). In this paper, the perturbation method was applied to derive analytical expressions from the Fokker-Planck equation, which characterized MNPs' magnetization behaviors under the AC and DC magnetic fields. Numerical simulations were conducted to validate the accuracy of the analytical expressions and to explore the correlation between temperature and the magnetization response. Then, a temperature analysis model based on magnetization harmonics was constructed. The first and second harmonic ratios and first harmonic phase were used to calculate MNPs' temperature, respectively. The experimental results demonstrate that within 310 K to 320 K, the estimation error of the temperature using the amplitude ratio of the first to second harmonics is below 0.0151 K, while the error using the first harmonic phase is below 0.0218 K. The derived analytical expressions are expected to enhance the accuracy of MNP-based temperature measurements and facilitate their broader applications in MNPH and MNP imaging.

cond-mat.mes-hall↗

arXiv2025

TasselNetV4: A vision foundation model for cross-scene, cross-scale, and cross-species plant counting

Accurate plant counting provides valuable information for agriculture such as crop yield prediction, plant density assessment, and phenotype quantification. Vision-based approaches are currently the mainstream solution. Prior art typically uses a detection or a regression model to count a specific plant. However, plants have biodiversity, and new cultivars are increasingly bred each year. It is almost impossible to exhaust and build all species-dependent counting models. Inspired by class-agnostic counting (CAC) in computer vision, we argue that it is time to rethink the problem formulation of plant counting, from what plants to count to how to count plants. In contrast to most daily objects with spatial and temporal invariance, plants are dynamic, changing with time and space. Their non-rigid structure often leads to worse performance than counting rigid instances like heads and cars such that current CAC and open-world detection models are suboptimal to count plants. In this work, we inherit the vein of the TasselNet plant counting model and introduce a new extension, TasselNetV4, shifting from species-specific counting to cross-species counting. TasselNetV4 marries the local counting idea of TasselNet with the extract-and-match paradigm in CAC. It builds upon a plain vision transformer and incorporates novel multi-branch box-aware local counters used to enhance cross-scale robustness. Two challenging datasets, PAC-105 and PAC-Somalia, are harvested. Extensive experiments against state-of-the-art CAC models show that TasselNetV4 achieves not only superior counting performance but also high efficiency.Our results indicate that TasselNetV4 emerges to be a vision foundation model for cross-scene, cross-scale, and cross-species plant counting.

cs.CV↗

arXiv2025

Solving an Interacting Quintessence Model with a Sound Horizon Initial Condition and its Observational Constraints

Astronomical observations suggest that the current standard $Λ$-Cold Dark Matter model in modern cosmology has some discrepancies when fitting the data during the whole expansion history of the universe. To solve the Hubble constant ($H_0$) tension, usually an unknown mechanism is considered that shifts the sound horizon at the decoupling era. On the other hand, dynamical dark energy models are also considered to resolve the problems of the cosmological constant, and the additional degrees of freedom require initial conditions for a solution. In this article we have considered a coupled quintessence dark energy model with a special focus on its early-time behaviour. In our solution the initial conditions are naturally decided by setting the value of the sound horizon at the recombination time, $θ^*$. We find that during this process, $H_0$ could be derived and its value rises with the coupling strength of the interaction. We also performed the background and cosmic microwave background power spectrum analysis, and find that the existence of the interaction term affects the energy density during a narrow time interval range and shifts the early cosmic microwave background spectrum. We also constrained the parameter space of the underlying scenario using the markov chain monte carlo analysis. We find that the best-fit values of $H_0$ and $S_8$ are improved slightly for the interacting model, but not enough to release the tensions.

gr-qc↗

arXiv2025

Training-free Ultra Small Model for Universal Sparse Reconstruction in Compressed Sensing

Pre-trained large models attract widespread attention in recent years, but they face challenges in applications that require high interpretability or have limited resources, such as physical sensing, medical imaging, and bioinformatics. Compressed Sensing (CS) is a well-proved theory that drives many recent breakthroughs in these applications. However, as a typical under-determined linear system, CS suffers from excessively long sparse reconstruction times when using traditional iterative methods, particularly with large-scale data. Current AI methods like deep unfolding fail to substitute them because pre-trained models exhibit poor generality beyond their training conditions and dataset distributions, or lack interpretability. Instead of following the big model fervor, this paper proposes ultra-small artificial neural models called coefficients learning (CL), enabling training-free and rapid sparse reconstruction while perfectly inheriting the generality and interpretability of traditional iterative methods, bringing new feature of incorporating prior knowledges. In CL, a signal of length $n$ only needs a minimal of $n$ trainable parameters. A case study model called CLOMP is implemented for evaluation. Experiments are conducted on both synthetic and real one-dimensional and two-dimensional signals, demonstrating significant improvements in efficiency and accuracy. Compared to representative iterative methods, CLOMP improves efficiency by 100 to 1000 folds for large-scale data. Test results on eight diverse image datasets indicate that CLOMP improves structural similarity index by 292%, 98%, 45% for sampling rates of 0.1, 0.3, 0.5, respectively. We believe this method can truly usher CS reconstruction into the AI era, benefiting countless under-determined linear systems that rely on sparse solution.

cs.LG↗

arXiv2024

Uniquely $C_{4}^{+}$-saturated graphs

A graph $G$ is uniquely $H$-saturated if it contains no copy of a graph $H$ as a subgraph, but adding any new edge into $G$ creates exactly one copy of $H$. Let $C_{4}^{+}$ be the diamond graph consisting of a $4$-cycle $C_{4}$ with one chord and $C_{3}^{*}$ be the graph consisting of a triangle with a pendant edge. In this paper we prove that a nontrivial uniquely $C_{4}^{+}$-saturated graph $G$ has girth $3$ or $4$. Further, $G$ has girth $4$ if and only if it is a strongly regular graph with special parameters. For $n>18k^{2}-24k+10$ with $k\geq2$, there are no uniquely $C_{4}^{+}$-saturated graphs on $n$ vertices with $k$ triangles. In particular, $C_{3}^{*}$ is the only nontrivial uniquely $C_{4}^{+}$-saturated graph with one triangle, and there are no uniquely $C_{4}^{+}$-saturated graphs with two, three or four triangles.

math.CO↗

arXiv2024

Adversarial Filtering Based Evasion and Backdoor Attacks to EEG-Based Brain-Computer Interfaces

A brain-computer interface (BCI) enables direct communication between the brain and an external device. Electroencephalogram (EEG) is a common input signal for BCIs, due to its convenience and low cost. Most research on EEG-based BCIs focuses on the accurate decoding of EEG signals, while ignoring their security. Recent studies have shown that machine learning models in BCIs are vulnerable to adversarial attacks. This paper proposes adversarial filtering based evasion and backdoor attacks to EEG-based BCIs, which are very easy to implement. Experiments on three datasets from different BCI paradigms demonstrated the effectiveness of our proposed attack approaches. To our knowledge, this is the first study on adversarial filtering for EEG-based BCIs, raising a new security concern and calling for more attention on the security of BCIs.

cs.HC↗

arXiv2024

Multi-Branch Mutual-Distillation Transformer for EEG-Based Seizure Subtype Classification

Cross-subject electroencephalogram (EEG) based seizure subtype classification is very important in precise epilepsy diagnostics. Deep learning is a promising solution, due to its ability to automatically extract latent patterns. However, it usually requires a large amount of training data, which may not always be available in clinical practice. This paper proposes Multi-Branch Mutual-Distillation (MBMD) Transformer for cross-subject EEG-based seizure subtype classification, which can be effectively trained from small labeled data. MBMD Transformer replaces all even-numbered encoder blocks of the vanilla Vision Transformer by our designed multi-branch encoder blocks. A mutual-distillation strategy is proposed to transfer knowledge between the raw EEG data and its wavelets of different frequency bands. Experiments on two public EEG datasets demonstrated that our proposed MBMD Transformer outperformed several traditional machine learning and state-of-the-art deep learning approaches. To our knowledge, this is the first work on knowledge distillation for EEG-based seizure subtype classification.

eess.SP↗

arXiv2023

A Novel Estimation Method for Temperature of Magnetic Nanoparticles Dominated by Brownian Relaxation Based on Magnetic Particle Spectroscopy

This paper presents a novel method for estimating the temperature of magnetic nanoparticles (MNPs) based on AC magnetization harmonics of MNPs dominated by Brownian relaxation. The difference in the AC magnetization response and magnetization harmonic between the Fokker-Planck equation and the Langevin function was analyzed, and we studied the relationship between the magnetization harmonic and the key factors, such as Brownian relaxation time, temperature, magnetic field strength, core size and hydrodynamic size of MNPs, excitation frequency, and so on. We proposed a compensation function for AC magnetization harmonic with consideration of the key factors and the difference between the Fokker-Planck equation and the Langevin function. Then a temperature estimation model based on the compensation function and the Langevin function was established. By employing the least squares algorithm, the temperature was successfully calculated. The experimental results show that the temperature error is less than 0.035 K in the temperature range from 310 K to 320 K. The temperature estimation model is expected to improve the performance of the magnetic nanoparticle thermometer and be applied to magnetic nanoparticle-mediated hyperthermia.

physics.med-ph↗

arXiv2023

Magnetic nanoparticle detection based on nonlinear Faraday rotation

Magnetic nanoparticle (MNP) has attracted interest in various research fields due to its special superparamagnetic and strong magneto-optical effects, especially as contrast agents to enhance the contrast of medical imaging. By introducing the interaction coefficient, we propose a model of the nonlinear Faraday rotation of MNP under the excitation of an external alternating magnetic field. In our homemade device (which can detect the rotation angle as low as about 2e-7 rad), it has been verified that the higher harmonics of the Faraday rotation can avoid the interference of paramagnetic and diamagnetic background at lower concentrations. What's more, the higher harmonics of the Faraday rotation of MNP can be detected in real time and they have a linear relationship with concentration. In the future, it is expected to use MNP as a magneto-optical contrast agent to achieve high-resolution imaging in vivo.

physics.optics↗

arXiv2023

Highly sensitive detection of magneto-optical markers based on magneto-optical gate effect

In this paper, a new concept of magnetic nanoparticles as magneto-optical markers is proposed, and a "magneto-optical gate" effect is explored. High-sensitivity detection of magnetic nanoparticles (MNPs) was developed based on this principle. Under irradiation from monochromatic light with a wavelength much larger than the size of a given MNP, Rayleigh scattering occurs on the MNP surface. The MNPs anisotropic and orientate themselves into chains, meaning that the Rayleigh scattering now decreases under magnetic field excitation. After applying an AC magnetic field of frequency f, the transmitted light passing through the MNPs reagent generates an AC signal with a frequency of 2f. Moreover, the 2f signal disappears after removal of the magnetic field. This creates a "magneto-optical gate" effect. The instantaneous and highly sensitive detection of magneto-optical markers with a concentration of 0.2 μg/mL and an effective optical path length of 10 μm is realized based on this "magneto-optical gate" effect.

physics.app-ph↗

arXiv2022

Constraints from High-Precision Measurements of the Cosmic Microwave Background: The Case of Disintegrating Dark Matter with $Λ$ or Dynamical Dark Energy

In recent years discrepancies have emerged in measurements of the present-day rate of expansion of the universe $H_0$ and in estimates of the clustering of matter $S_8$. Using the most recent cosmological observations we reexamine a novel model proposed to address these tensions, in which cold dark matter disintegrates into dark radiation. The disintegration process is controlled by its rate $Q = α\mathcal{H} ρ_{\rm ddm}$, where $α$ is a (constant) dimensionless parameter quantifying the strength of the disintegration mechanism and $\mathcal{H}$ is the conformal Hubble rate in the spatially flat Friedmann-Lemaître-Robertson-Walker universe and $ρ_{\rm ddm}$ is the energy density of the disintegrating cold dark matter. We constrain this model with the latest 2018 Planck temperature and polarization data, showing that there is no evidence for $α\neq 0$ and that it cannot solve the $H_0$ tension below $3σ$, clashing with the result obtained by analyzing the Planck 2015 temperature data. We also investigate two possible extensions of the model in which the dark energy equation-of-state parameter $w \neq -1$. In this case it is possible to combine Planck data with the SH0ES measurement, and we demonstrate that in both these models the $H_0$ tension is resolved at the $1σ$ level, but the condition $w \neq -1$ exacerbates the $S_8$ tension. We also demonstrate that the addition of intermediate-redshift data (from the Pantheon supernova type Ia dataset and baryon acoustic oscillations) weakens the effectiveness of all these models to address the $H_0$ and $S_8$ tensions.

astro-ph.CO↗

arXiv2021

Temperature Measurement Based on Electron Spin Resonance of Magnetic Nanoparticles

Magnetic nanoparticles (MNPs) have excellent magnetic-temperature characteristic. However, current temperature measurement based on MNPs is interfered by concentration. Utilizing the electron spin resonance (ESR), we propose a highly sensitive temperature measurement method without concentration coupling. The anisotropic field is affected by temperature, thus affecting the g-value. The influence of the MNP concentration, size, and the data analysis method on temperature estimation are studied. The optimal temperature sensitivity is achieved with 15-nm MNPs while Gaussian smoothing method allows an optimal accuracy at Fe concentration of 5 mg/ml with a root mean squared error of 0.07 K.

cond-mat.mes-hall↗

arXiv2021

Quadrangular embeddings of complete graphs and the Even Map Color Theorem (with details)

Hartsfield and Ringel constructed orientable quadrangular embeddings of the complete graph $K_n$ for $n\equiv 5 \pmod 8$, and nonorientable ones for $n \ge 9$ and $n\equiv 1 \pmod 4$. These provide minimal quadrangulations of their underlying surfaces. We extend these results to determine, for every complete graph $K_n$, $n \ge 4$, the minimum genus, both orientable and nonorientable, for the surface in which $K_n$ has an embedding with all faces of degree at least $4$, and also for the surface in which $K_n$ has an embedding with all faces of even degree. These last embeddings provide sharpness examples for a result of Hutchinson bounding the chromatic number of graphs embedded with all faces of even degree, completing the proof of the Even Map Color Theorem. We also show that if a connected simple graph $G$ has a perfect matching and a cycle then the lexicographic product $G[K_4]$ has orientable and nonorientable quadrangular embeddings; this provides new examples of minimal quadrangulations.

math.CO↗

arXiv2021

Minimal quadrangulations of surfaces

A quadrangular embedding of a graph in a surface $Σ$, also known as a quadrangulation of $Σ$, is a cellular embedding in which every face is bounded by a $4$-cycle. A quadrangulation of $Σ$ is minimal if there is no quadrangular embedding of a (simple) graph of smaller order in $Σ$. In this paper we determine $n(Σ)$, the order of a minimal quadrangulation of a surface $Σ$, for all surfaces, both orientable and nonorientable. Letting $S_0$ denote the sphere and $N_2$ the Klein bottle, we prove that $n(S_0)=4, n(N_2)=6$, and $n(Σ)=\lceil (5+\sqrt{25-16χ(Σ)})/2\rceil$ for all other surfaces $Σ$, where $χ(Σ)$ is the Euler characteristic. Our proofs use a `diagonal technique', introduced by Hartsfield in 1994. We explain the general features of this method.

math.CO↗

arXiv2020

Improving magnetic nanothermometry accuracy through mixing-frequency excitation

In this study, we proposed a temperature model of magnetic nanoparticle relaxation and a phase measurement method under a mixing-frequency excitation field, which can improve the temperature accuracy of magnetic nanothermometry. According to the Debye-based magnetization model for magnetic nanoparticles, the phases at the mixing frequencies are used to solve the relaxation phase delay to the magnetic field with the higher frequency. The method could improve the signal-to-noise ratio of the magnetic response signal, and also weaken the phase shift of the detection coils caused by temperature changes. Experimental results show that the method can achieve static temperature measurement error less than 0.1K and dynamic temperature measurement error less than 0.2K.

eess.SY↗

arXiv2013

The quintessence field as a perfect cosmic fluid of constant pressure

We study the cosmology of a quintessence scalar field which is equivalent to a non-barotropic perfect fluid of constant pressure. The coincidence problem is alleviated by such a quintessence equation-of-state that interpolates between plateau of zero at large redshifts and plateau of minus one as the redshift approaches to zero. The quintessence field is neither a unified dark matter nor a mixture of cosmological constant and cold dark matter, because the quintessence density contrasts decay monotonously on sub-horizon scales and the squared sound speeds of quintessence perturbations do not vanish. What a role does the quintessence play is dynamic dark energy. Though the clustering of quintessence decays drastically, it could remarkably impact the growth rate of the density perturbations of non-relativistic matters at early stage.

astro-ph.CO↗