arXiv subjects

Hongxin Wang

Publications and source records attributed to Hongxin Wang.

At least 19 recordsLinked to original sources

arXiv2026

Thermal Evolution and Hydrodynamic Filtering of Pseudoscalar Dark Matter

Filtered dark matter provides a mechanism for producing massive dark matter particles during a first order phase transition. The resulting abundance can be modified by plasma hydrodynamics. We investigate this effect in a complex singlet extension of the Standard Model, focusing on the deflagration regime. The entropy normalized dark matter abundance $Y_\chi$ is calculated using both analytic and numerical methods. As the dark matter mass increases, we identify three patterns of abundance evolution before nucleation, ranging from thermal equilibrium to thermal suppression and freeze-out. For $\xi_w=0.01$, we find that shock heating increases $Y_\chi$ by factors of approximately $4.3$, $5.8$, and $32$ for $m_\chi=1.78$, $2.30$, and $5.03~\mathrm{TeV}$, respectively. For the two lighter masses, the numerical and analytic results agree within $5\%$, and the difference is reduced to about $1\%$ or less when hydrodynamic effects are included. The heaviest case requires a numerical treatment because dark matter freeze-out before nucleation. Our results show that hydrodynamic reheating can enhance $Y_\chi$ even when dark matter has already frozen out before the phase transition. In this regime, $Y_\chi$ is determined by the dark matter abundance established before nucleation and hydrodynamic filtering at the bubble wall.

hep-ph↗

arXiv2026

Gravitational Wave Signature of Aspherical Bubbles Driven by Thermal Fluctuation

Cosmological first-order phase transitions are a well-motivated source of stochastic gravitational waves (GWs), but most predictions are made based on the highly idealized model of perfectly spherical vacuum bubbles, neglecting thermal fluctuations. In this work we use $(3+1)$-dimensional lattice simulations of a scalar model with thermal initial conditions to quantify how thermal fluctuations distort bubble profiles and modify the resulting GW spectrum. We find that thermal fluctuations can strongly break spherical symmetry at early times, allowing even an isolated bubble to emit GWs. In multi-bubble simulations, thermal fluctuations systematically reshape the spectrum, suppressing the infrared part while enhancing and broadening the high-$k$ tail. We further provide an analytical estimate for the ultraviolet regime of the GW spectrum, which is in good agreement with our lattice results and suggests that this regime is dominated by thermal fluctuations. These effects could leave observable imprints in future GW searches.

hep-ph↗

arXiv2025

Perturbations in the Orthogonal Complement Subspace for Efficient Out-of-Distribution Detection

Out-of-distribution (OOD) detection is essential for deploying deep learning models in open-world environments. Existing approaches, such as energy-based scoring and gradient-projection methods, typically rely on high-dimensional representations to separate in-distribution (ID) and OOD samples. We introduce P-OCS (Perturbations in the Orthogonal Complement Subspace), a lightweight and theoretically grounded method that operates in the orthogonal complement of the principal subspace defined by ID features. P-OCS applies a single projected perturbation restricted to this complementary subspace, enhancing subtle ID-OOD distinctions while preserving the geometry of ID representations. We show that a one-step update is sufficient in the small-perturbation regime and provide convergence guarantees for the resulting detection score. Experiments across multiple architectures and datasets demonstrate that P-OCS achieves state-of-the-art OOD detection with negligible computational cost and without requiring model retraining, access to OOD data, or changes to model architecture.

stat.ML↗

arXiv2025

Enhancing Phase Transition Calculations with Fitting and Neural Network

The computation of bounce action in a phase transition involves solving partial differential equations, inherently introducing non-negligible numerical uncertainty. Deriving characteristic temperatures and properties of this transition necessitates both differentiation and integration of the action, thereby exacerbating the uncertainty. In this work, we fit the action curve as a function of temperature to mitigate the uncertainties inherent in the calculation of the phase transition parameters. We find that, after extracting a factor, the sixth-order polynomial yields an excellent fit for the action in the high temperature approximated potential. In a realistic model, the singlet extension of the Standard Model, this method performs satisfactorily across most of the parameter space after trimming the fitting data. This approach not only enhances the accuracy of phase transition calculations but also systematically reduces computation time and facilitates error estimation, particularly in models involving multiple scalar fields. Furthermore, we discussed the possible of using multiple neural networks to predict the action curve from model parameters.

hep-ph↗

arXiv2025

The Bubble Wall Velocity in Local Thermal Equilibrium and Energy Budget with Full Effective Potential

We develop a framework based on the full one-loop finite-temperature effective potential model, within which the bubble wall velocity is calculated using the local thermal equilibrium (LTE) approximation, and the kinetic energy fraction $K$ is computed directly. In cosmological phase transitions, these quantities play a critical role in determining the resulting gravitational wave signals. Using the xSM as a benchmark model, we compute the peak gravitational wave spectra under different methods for determining the wall velocity and the kinetic energy fraction $K$, and compare these results to those obtained using the commonly employed bag model. Within the scanned parameter space, we find: (1) Deflagration is the most prevalent mode of fluid motion.(2) Gravitational wave spectra based on the full effective potential with LTE-derived wall velocity and integrated $K$ can differ significantly from those using the bag model with fitted $K$. In the deflagration regime, discrepancies reach up to 48\% in peak frequency and 90\% in amplitude.(3) The bag model provides a good approximation to the full equation of state in many cases. Notably, in deflagration scenarios with input wall velocity, the gravitational wave spectra obtained from the bag model more closely resemble the LTE-based results than those derived using the full potential with this input wall velocity.

hep-ph↗

arXiv2025

Source Confusion of Massive Black Hole Binaries for the Taiji Mission

We systematically investigate the source confusion of massive black hole binaries (MBHBs) for the Taiji space-based gravitational wave mission. Source confusion, arising from the overlap of signals in both time and frequency domains, can degrade parameter recovery accuracy. To assess this effect, we simulate three representative models MBHB populations to estimate event overlap events. Assuming 100 detections per year, only 0.31-4.2 overlaps are expected annually. Based on Fisher information matrix with the $\texttt{IMRPhenomD}$ and $\texttt{IMRPhenomHM}$ waveform models, we find that overlap significantly enlarges parameter uncertainties, while the inclusion of higher-order modes (HMs) effectively mitigates this effect. Severe confusion ($\Delta \mathcal{M}_z / \mathcal{M}_z<$ 0.2%) occurs in fewer than 0.14% across the three population models. The full Bayesian analysis further corroborates the Fisher predictions, and also reveals that HMs help break key parameter degeneracies, with or without signal overlap. These findings underscore the importance of incorporating HMs for accurate inference in future space-based observations.

gr-qc↗

arXiv2023

Attacks Against Security Context in 5G Network

The security context used in 5G authentication is generated during the Authentication and Key Agreement (AKA) procedure and stored in both the user equipment (UE) and the network sides for the subsequent fast registration procedure. Given its importance, it is imperative to formally analyze the security mechanism of the security context. The security context in the UE can be stored in the Universal Subscriber Identity Module (USIM) card or in the baseband chip. In this work, we present a comprehensive and formal verification of the fast registration procedure based on the security context under the two scenarios in ProVerif. Our analysis identifies two vulnerabilities, including one that has not been reported before. Specifically, the security context stored in the USIM card can be read illegally, and the validity checking mechanism of the security context in the baseband chip can be bypassed. Moreover, these vulnerabilities also apply to 4G networks. As a consequence, an attacker can exploit these vulnerabilities to register to the network with the victim's identity and then launch other attacks, including one-tap authentication bypass leading to privacy disclosure, location spoofing, etc. To ensure that these attacks are indeed realizable in practice, we have responsibly confirmed them through experimentation in three operators. Our analysis reveals that these vulnerabilities stem from design flaws of the standard and unsafe practices by operators. We finally propose several potential countermeasures to prevent these attacks. We have reported our findings to the GSMA and received a coordinated vulnerability disclosure (CVD) number CVD-2022-0057.

cs.CR↗

arXiv2022

Spatio-Temporal Feedback Control of Small Target Motion Detection Visual System

Feedback is crucial to motion perception in animals' visual systems where its spatial and temporal dynamics are often shaped by movement patterns of surrounding environments. However, such spatio-temporal feedback has not been deeply explored in designing neural networks to detect small moving targets that cover only one or a few pixels in image while presenting extremely limited visual features. In this paper, we address small target motion detection problem by developing a visual system with spatio-temporal feedback loop, and further reveal its important roles in suppressing false positive background movement while enhancing network responses to small targets. Specifically, the proposed visual system is composed of two complementary subnetworks. The first subnetwork is designed to extract spatial and temporal motion patterns of cluttered backgrounds by neuronal ensemble coding. The second subnetwork is developed to capture small target motion information and integrate the spatio-temporal feedback signal from the first subnetwork to inhibit background false positives. Experimental results demonstrate that the proposed spatio-temporal feedback visual system is more competitive than existing methods in discriminating small moving targets from complex dynamic environment.

cs.CV↗

arXiv2022

Attention and Prediction Guided Motion Detection for Low-Contrast Small Moving Targets

Small target motion detection within complex natural environments is an extremely challenging task for autonomous robots. Surprisingly, the visual systems of insects have evolved to be highly efficient in detecting mates and tracking prey, even though targets occupy as small as a few degrees of their visual fields. The excellent sensitivity to small target motion relies on a class of specialized neurons called small target motion detectors (STMDs). However, existing STMD-based models are heavily dependent on visual contrast and perform poorly in complex natural environments where small targets generally exhibit extremely low contrast against neighbouring backgrounds. In this paper, we develop an attention and prediction guided visual system to overcome this limitation. The developed visual system comprises three main subsystems, namely, an attention module, an STMD-based neural network, and a prediction module. The attention module searches for potential small targets in the predicted areas of the input image and enhances their contrast against complex background. The STMD-based neural network receives the contrast-enhanced image and discriminates small moving targets from background false positives. The prediction module foresees future positions of the detected targets and generates a prediction map for the attention module. The three subsystems are connected in a recurrent architecture allowing information to be processed sequentially to activate specific areas for small target detection. Extensive experiments on synthetic and real-world datasets demonstrate the effectiveness and superiority of the proposed visual system for detecting small, low-contrast moving targets against complex natural environments.

cs.CV↗

arXiv2021

A Time-Delay Feedback Neural Network for Discriminating Small, Fast-Moving Targets in Complex Dynamic Environments

Discriminating small moving objects within complex visual environments is a significant challenge for autonomous micro robots that are generally limited in computational power. By exploiting their highly evolved visual systems, flying insects can effectively detect mates and track prey during rapid pursuits, even though the small targets equate to only a few pixels in their visual field. The high degree of sensitivity to small target movement is supported by a class of specialized neurons called small target motion detectors (STMDs). Existing STMD-based computational models normally comprise four sequentially arranged neural layers interconnected via feedforward loops to extract information on small target motion from raw visual inputs. However, feedback, another important regulatory circuit for motion perception, has not been investigated in the STMD pathway and its functional roles for small target motion detection are not clear. In this paper, we propose an STMD-based neural network with feedback connection (Feedback STMD), where the network output is temporally delayed, then fed back to the lower layers to mediate neural responses. We compare the properties of the model with and without the time-delay feedback loop, and find it shows preference for high-velocity objects. Extensive experiments suggest that the Feedback STMD achieves superior detection performance for fast-moving small targets, while significantly suppressing background false positive movements which display lower velocities. The proposed feedback model provides an effective solution in robotic visual systems for detecting fast-moving small targets that are always salient and potentially threatening.

cs.CV↗

arXiv2021

Enhancing LGMD's Looming Selectivity for UAV with Spatial-temporal Distributed Presynaptic Connections

Collision detection is one of the most challenging tasks for Unmanned Aerial Vehicles (UAVs). This is especially true for small or micro UAVs, due to their limited computational power. In nature, flying insects with compact and simple visual systems demonstrate their remarkable ability to navigate and avoid collision in complex environments. A good example of this is provided by locusts. They can avoid collisions in a dense swarm through the activity of a motion based visual neuron called the Lobula Giant Movement Detector (LGMD). The defining feature of the LGMD neuron is its preference for looming. As a flying insect's visual neuron, LGMD is considered to be an ideal basis for building UAV's collision detecting system. However, existing LGMD models cannot distinguish looming clearly from other visual cues such as complex background movements caused by UAV agile flights. To address this issue, we proposed a new model implementing distributed spatial-temporal synaptic interactions, which is inspired by recent findings in locusts' synaptic morphology. We first introduced the locally distributed excitation to enhance the excitation caused by visual motion with preferred velocities. Then radially extending temporal latency for inhibition is incorporated to compete with the distributed excitation and selectively suppress the non-preferred visual motions. Systematic experiments have been conducted to verify the performance of the proposed model for UAV agile flights. The results have demonstrated that this new model enhances the looming selectivity in complex flying scenes considerably, and has potential to be implemented on embedded collision detection systems for small or micro UAVs.

cs.AI↗

arXiv2021

Dark matter, $Z^{\prime}$, vector-like quark at the LHC and $b\to s μμ$ anomaly

Combining the $b\to sμ^+μ^-$ anomaly and dark matter observables, we study the capability of LHC to test dark matter, $Z^{\prime}$, and vector-like quark. We focus on a local $U(1)_{L_μ-L_τ}$ model with a vector-like $SU(2)_L$ doublet quark $Q$ and a complex singlet scalar whose lightest component $X_I$ is a candidate of dark matter. After imposing relevant constraints, we find that the $b\to sμ^+μ^-$ anomaly and the relic abundance of dark matter favor $m_{X_I}< 350$ GeV and $m_{Z^{\prime}}< 450$ GeV for $m_Q<$ 2 TeV and $m_{X_R}<$ 2 TeV (the heavy partner of $m_{X_I}$). The current searches for jets and missing transverse momentum at the LHC sizably reduce the mass ranges of the vector-like quark, and $m_Q$ is required to be larger than 1.7 TeV. Finally, we discuss the possibility of probing these new particles at the high luminosity LHC via the QCD process $pp \to D\bar{D}$ or $pp \to U\bar{U}$ followed by the decay $D\to s (b) Z'X_I$ or $U \to u (c) Z' X_I$ and then $Z'\toμ^+μ^-$. Taking a benchmark point of $m_Q$=1.93 TeV, $m_{Z^\prime}=170$ GeV, and $m_{X_I}=$ 145 GeV, we perform a detailed Monte Carlo simulation, and find that such benchmark point can be accessible at the 14 TeV LHC with an integrated luminosity 3000 fb$^{-1}$.

hep-ph↗

arXiv2020

An efficient and high-resolution topology optimization method based on convolutional neural networks

2. In Section 3, we used some vague statements to affirm the training process of the neural network, which cannot support others to reproduce the results of the paper. In addition, this section does not show the difference between this paper and other work, nor does it reflect innovation. 3. In Section 5, the numerical examples are not compared with other multi-resolution methods, which is not enough to explain the superiority of the method proposed in this paper. Figure 10 also fails to show that this method is significantly improved compared to the traditional method. Thanks for your understanding. Topology optimization is a pioneering design method that can provide various candidates with high mechanical properties. However, the high-resolution for the optimum structures is highly desired, normally in turn leading to computationally intractable puzzle, especially for the famous Solid Isotropic Material with Penalization (SIMP) method. In this paper, we introduce the Super-Resolution Convolutional Neural Network (SRCNN) technique into topology optimization framework to improve the resolution of topology solutions with extremely high computational efficiency. Additionally, a pooling strategy is established to balance the number of finite element analysis (FEA) and the output mesh in optimization process. Considering the high training cost of 3D neural networks, several 2D neural networks are combined to deal with 3D topology optimization design problems. The combined treatment method used in 3D topology optimization design eliminates the expense of retraining 3D convolutional neural network and guarantees the quality of 3D design. Some typical examples justify that the high-resolution topology optimization method adopting SRCNN has excellent applicability and high efficiency.

math.OC↗

arXiv2019

An efficient evolutionary structural optimization method for multi-resolution designs

To solve large-scale or high-resolution topology optimization problem, a novel algorithm is developed based on modified bi-directional evolutionary structure optimization (BESO) and extended finite element method (XFEM). Within XFEM, a set of enriched nodes are defined to divide the finite element into several uniform sub-regions, i.e. sub-triangles and sub-tetrahedrons. The material grid and shape functions are defined on each sub-region to improve the computational accuracy, whereas the equilibrium equation is established on the level of coarse finite elements to increase the computational efficiency. We set all the standard FE nodes and the enriched nodes as the design variables, and a modified material interpolation model is introduced to calculate the material properties for sub-regions. An enrichment function originating from modeling voids scheme is adopted to character the discontinuity between solid material to void material. To efficiently use the gradient-based algorithm, BESO, sensitivity analysis is performed with the aid of adjoint method. Typical numerical examples, involving millions of design variables, are carried to verify the effectiveness of the proposed method.

physics.app-ph↗

arXiv2019

A Robust Visual System for Small Target Motion Detection Against Cluttered Moving Backgrounds

Monitoring small objects against cluttered moving backgrounds is a huge challenge to future robotic vision systems. As a source of inspiration, insects are quite apt at searching for mates and tracking prey -- which always appear as small dim speckles in the visual field. The exquisite sensitivity of insects for small target motion, as revealed recently, is coming from a class of specific neurons called small target motion detectors (STMDs). Although a few STMD-based models have been proposed, these existing models only use motion information for small target detection and cannot discriminate small targets from small-target-like background features (named as fake features). To address this problem, this paper proposes a novel visual system model (STMD+) for small target motion detection, which is composed of four subsystems -- ommatidia, motion pathway, contrast pathway and mushroom body. Compared to existing STMD-based models, the additional contrast pathway extracts directional contrast from luminance signals to eliminate false positive background motion. The directional contrast and the extracted motion information by the motion pathway are integrated in the mushroom body for small target discrimination. Extensive experiments showed the significant and consistent improvements of the proposed visual system model over existing STMD-based models against fake features.

cs.CV↗

arXiv2019

Constant Angular Velocity Regulation for Visually Guided Terrain Following

Insects use visual cues to control their flight behaviours. By estimating the angular velocity of the visual stimuli and regulating it to a constant value, honeybees can perform a terrain following task which keeps the certain height above the undulated ground. For mimicking this behaviour in a bio-plausible computation structure, this paper presents a new angular velocity decoding model based on the honeybee's behavioural experiments. The model consists of three parts, the texture estimation layer for spatial information extraction, the motion detection layer for temporal information extraction and the decoding layer combining information from pervious layers to estimate the angular velocity. Compared to previous methods on this field, the proposed model produces responses largely independent of the spatial frequency and contrast in grating experiments. The angular velocity based control scheme is proposed to implement the model into a bee simulated by the game engine Unity. The perfect terrain following above patterned ground and successfully flying over irregular textured terrain show its potential for micro unmanned aerial vehicles' terrain following.

cs.RO↗

arXiv2019

Towards Computational Models and Applications of Insect Visual Systems for Motion Perception: A Review

Motion perception is a critical capability determining a variety of aspects of insects' life, including avoiding predators, foraging and so forth. A good number of motion detectors have been identified in the insects' visual pathways. Computational modelling of these motion detectors has not only been providing effective solutions to artificial intelligence, but also benefiting the understanding of complicated biological visual systems. These biological mechanisms through millions of years of evolutionary development will have formed solid modules for constructing dynamic vision systems for future intelligent machines. This article reviews the computational motion perception models originating from biological research of insects' visual systems in the literature. These motion perception models or neural networks comprise the looming sensitive neuronal models of lobula giant movement detectors (LGMDs) in locusts, the translation sensitive neural systems of direction selective neurons (DSNs) in fruit flies, bees and locusts, as well as the small target motion detectors (STMDs) in dragonflies and hover flies. We also review the applications of these models to robots and vehicles. Through these modelling studies, we summarise the methodologies that generate different direction and size selectivity in motion perception. At last, we discuss about multiple systems integration and hardware realisation of these bio-inspired motion perception models.

cs.CV↗

arXiv2019

A Visual Neural Network for Robust Collision Perception in Vehicle Driving Scenarios

This research addresses the challenging problem of visual collision detection in very complex and dynamic real physical scenes, specifically, the vehicle driving scenarios. This research takes inspiration from a large-field looming sensitive neuron, i.e., the lobula giant movement detector (LGMD) in the locust's visual pathways, which represents high spike frequency to rapid approaching objects. Building upon our previous models, in this paper we propose a novel inhibition mechanism that is capable of adapting to different levels of background complexity. This adaptive mechanism works effectively to mediate the local inhibition strength and tune the temporal latency of local excitation reaching the LGMD neuron. As a result, the proposed model is effective to extract colliding cues from complex dynamic visual scenes. We tested the proposed method using a range of stimuli including simulated movements in grating backgrounds and shifting of a natural panoramic scene, as well as vehicle crash video sequences. The experimental results demonstrate the proposed method is feasible for fast collision perception in real-world situations with potential applications in future autonomous vehicles.

cs.CV↗