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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., Comment: The 6th International Symposium on Mobile Internet Security (MobiSec 22)

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.

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., Comment: 13 pages, 21 figures

Combining the $b\to s\mu^+\mu^-$ 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 $U(1)_{L_\mu-L_\tau}$ 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 $b\to s\mu^+\mu^-$ 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 $Z'\to\mu^+\mu^-$. 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$^{-1}$., Comment: 18 pages, 7 figures, 2 tables

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., Comment: 15 pages, 17 figures, 4 tables

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., Comment: 14 pages, 16 figures

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., Comment: We applied to withdraw this version of the paper for the following reasons: 1. The method proposed in this paper has fundamental technical defects. This method cannot construct new structural features, but only improves resolution, which violates the original intention of high-resolution topology optimization. Connect to Abstract field

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., Comment: 24 pages, 15 figures