Cache-Enabled in Cooperative Cognitive Radio Networks for Transmission Performance

The proliferation of mobile devices that support the acceleration of data services (especially smartphones) has resulted in a dramatic increase in mobile traffic. Mobile data also increased exponentially, already exceeding the throughput of the backhaul. To improve spectrum utilization and increase mobile network traffic, in combination with content caching, we study the cooperation between primary and secondary networks via content caching. We consider that the secondary base station assists the primary user by pre-caching some popular primary contents. Thus, the secondary base station can obtain more licensed bandwidth to serve its own user. We mainly focus on the time delay from the backhaul link to the secondary base station. First, in terms of the content caching and the transmission strategies, we provide a cooperation scheme to maximize the secondary user’s effective data transmission rates under the constraint of the primary users target rate. Then, we investigate the impact of the caching allocation and prove that the formulated problem is a concave problem with regard to the caching capacity allocation for any given power allocation. Furthermore, we obtain the joint caching and power allocation by an effective bisection search algorithm. Finally, our results show that the content caching cooperation scheme can achieve significant performance gain for the primary and secondary systems over the traditional two-hop relay cooperation without caching

Understanding the Robustness of 3D Object Detection with Bird's-Eye-View Representations in Autonomous Driving

3D object detection is an essential perception task in autonomous driving to understand the environments. The Bird's-Eye-View (BEV) representations have significantly improved the performance of 3D detectors with camera inputs on popular benchmarks. However, there still lacks a systematic understanding of the robustness of these vision-dependent BEV models, which is closely related to the safety of autonomous driving systems. In this paper, we evaluate the natural and adversarial robustness of various representative models under extensive settings, to fully understand their behaviors influenced by explicit BEV features compared with those without BEV. In addition to the classic settings, we propose a 3D consistent patch attack by applying adversarial patches in the 3D space to guarantee the spatiotemporal consistency, which is more realistic for the scenario of autonomous driving. With substantial experiments, we draw several findings: 1) BEV models tend to be more stable than previous methods under different natural conditions and common corruptions due to the expressive spatial representations; 2) BEV models are more vulnerable to adversarial noises, mainly caused by the redundant BEV features; 3) Camera-LiDAR fusion models have superior performance under different settings with multi-modal inputs, but BEV fusion model is still vulnerable to adversarial noises of both point cloud and image. These findings alert the safety issue in the applications of BEV detectors and could facilitate the development of more robust models.Comment: 8 pages, CVPR202

Deep learning-based edge caching for multi-cluster heterogeneous networks

© 2019, Springer-Verlag London Ltd., part of Springer Nature. In this work, we consider a time and space evolution cache refreshing in multi-cluster heterogeneous networks. We consider a two-step content placement probability optimization. At the initial complete cache refreshing optimization, the joint optimization of the activated base station density and the content placement probability is considered. And we transform this optimization problem into a GP problem. At the following partial cache refreshing optimization, we take the time–space evolution into consideration and derive a convex optimization problem subjected to the cache capacity constraint and the backhaul limit constraint. We exploit the redundant information in different content popularity using the deep neural network to avoid the repeated calculation because of the change in content popularity distribution at different time slots. Trained DNN can provide online response to content placement in a multi-cluster HetNet model instantaneously. Numerical results demonstrate the great approximation to the optimum and generalization ability

Infrared Imaging of Magnetic Octupole Domains in Non-collinear Antiferromagnets

Magnetic structure plays a pivotal role in the functionality of antiferromagnets (AFMs), which not only can be employed to encode digital data but also yields novel phenomena. Despite its growing significance, visualizing the antiferromagnetic domain structure remains a challenge, particularly for non-collinear AFMs. Currently, the observation of magnetic domains in non-collinear antiferromagnetic materials is feasible only in Mn$_{3}$Sn, underscoring the limitations of existing techniques that necessitate distinct methods for in-plane and out-of-plane magnetic domain imaging. In this study, we present a versatile method for imaging the antiferromagnetic domain structure in a series of non-collinear antiferromagnetic materials by utilizing the anomalous Ettingshausen effect (AEE), which resolves both the magnetic octupole moments parallel and perpendicular to the sample surface. Temperature modulation due to the AEE originating from different magnetic domains is measured by the lock-in thermography, revealing distinct behaviors of octupole domains in different antiferromagnets. This work delivers an efficient technique for the visualization of magnetic domains in non-collinear AFMs, which enables comprehensive study of the magnetization process at the microscopic level and paves the way for potential advancements in applications.Comment: National Science Review in pres

Fucoxanthin, a Marine Carotenoid, Attenuates β

Alzheimer’s disease (AD), the most common neurodegenerative disorder, is characterized by neurofibrillary tangles, synaptic impairments, and loss of neurons. Oligomers of β-amyloid (Aβ) are widely accepted as the main neurotoxins to induce oxidative stress and neuronal loss in AD. In this study, we discovered that fucoxanthin, a marine carotenoid with antioxidative stress properties, concentration dependently prevented Aβ oligomer-induced increase of neuronal apoptosis and intracellular reactive oxygen species in SH-SY5Y cells. Aβ oligomers inhibited the prosurvival phosphoinositide 3-kinase (PI3K)/Akt cascade and activated the proapoptotic extracellular signal-regulated kinase (ERK) pathway. Moreover, inhibitors of glycogen synthase kinase 3β (GSK3β) and mitogen-activated protein kinase (MEK) synergistically prevented Aβ oligomer-induced neuronal death, suggesting that the PI3K/Akt and ERK pathways might be involved in Aβ oligomer-induced neurotoxicity. Pretreatment with fucoxanthin significantly prevented Aβ oligomer-induced alteration of the PI3K/Akt and ERK pathways. Furthermore, LY294002 and wortmannin, two PI3K inhibitors, abolished the neuroprotective effects of fucoxanthin against Aβ oligomer-induced neurotoxicity. These results suggested that fucoxanthin might prevent Aβ oligomer-induced neuronal loss and oxidative stress via the activation of the PI3K/Akt cascade as well as inhibition of the ERK pathway, indicating that further studies of fucoxanthin and related compounds might lead to a useful treatment of AD