Antenna Beamforming for Energy Harvesting in Cognitive Radio Networks

In this paper, a cooperative cognitive radio network (CRN) with energy harvesting capabilities of its secondary users is considered. Specifically, cooperative spectrum sensing and multi-antenna beamforming are employed to improve the sensing performance and the energy transfer efficiency, respectively. In our approach, a homogeneous CRN scenario is studied where the optimal sensing probability of each second user (SU) is obtained to maximize the control center (CC) throughput while satisfying the energy causality and primary user (PU) collision constraints. An iterative algorithm is proposed to obtain the optimal charging time. Numerical results depict that in an energy constrained scenario, cooperative spectrum sensing with beamforming performs much better than cooperative spectrum sensing without beamforming in terms of increased system throughpu

Massless monopoles and the moduli space approximation

We investigate the applicability of the moduli space approximation in theories with unbroken non-Abelian gauge symmetries. Such theories have massless magnetic monopoles that are manifested at the classical level as clouds of non-Abelian field surrounding one or more massive monopoles. Using an SO(5) example with one massive and one massless monopole, we compare the predictions of the moduli space approximation with the results of a numerical solution of the full field equations. We find that the two diverge when the cloud velocity becomes of order unity. After this time the cloud profile approximates a spherical wavefront moving at the speed of light. In the region well behind this wavefront the moduli space approximation continues to give a good approximation to the fields. We therefore expect it to provide a good description of the motion of the massive monopoles and of the transfer of energy between the massive and massless monopoles.Comment: 18 pages, 5 figure

The Fangshan/Family-based Ischemic Stroke Study In China (FISSIC) protocol

Background: The exact etiology of ischemic stroke remains unclear, because multiple genetic predispositions and environmental risk factors may be involved, and their interactions dictate the complexity. Family-based studies provide unique features in design, while they are currently underrepresented for studies of ischemic stroke in developing countries. The Fangshan/Family-based Ischemic Stroke Study In China (FISSIC) program aims to conduct a genetic pedigree study of ischemic stroke in rural communities of China. Methods/Design: The pedigrees of ischemic stroke with clear documentation are recruited by using the proband-initiated contact method, based on the stroke registry in hospital and communities. Blood samples and detailed information of pedigrees are collected through the health care network in the rural area, and prospective follow-up of the pedigrees cohort is scheduled. Complementary strategies of both family-based design and matched case-spousal control design are used, and comprehensive statistical methods will be implemented to ascertain potential complex genetic and environmental factors and their interactions as well. Discussion: This study is complementary to other genetic pedigree studies of ischemic stroke, such as the Siblings With Ischemic Stroke Study (SWISS), which are established in developed countries. We describe the protocol of this family-based genetic epidemiological study that may be used as a new practical guideline and research paradigm in developing countries and facilitate initiatives of stroke study for international collaborations.http://gateway.webofknowledge.com/gateway/Gateway.cgi?GWVersion=2&SrcApp=PARTNER_APP&SrcAuth=LinksAMR&KeyUT=WOS:000250034100001&DestLinkType=FullRecord&DestApp=ALL_WOS&UsrCustomerID=8e1609b174ce4e31116a60747a720701Genetics & HereditySCI(E)PubMed7ARTICLE60

Graph Contrastive Learning with Implicit Augmentations

Existing graph contrastive learning methods rely on augmentation techniques based on random perturbations (e.g., randomly adding or dropping edges and nodes). Nevertheless, altering certain edges or nodes can unexpectedly change the graph characteristics, and choosing the optimal perturbing ratio for each dataset requires onerous manual tuning. In this paper, we introduce Implicit Graph Contrastive Learning (iGCL), which utilizes augmentations in the latent space learned from a Variational Graph Auto-Encoder by reconstructing graph topological structure. Importantly, instead of explicitly sampling augmentations from latent distributions, we further propose an upper bound for the expected contrastive loss to improve the efficiency of our learning algorithm. Thus, graph semantics can be preserved within the augmentations in an intelligent way without arbitrary manual design or prior human knowledge. Experimental results on both graph-level and node-level tasks show that the proposed method achieves state-of-the-art performance compared to other benchmarks, where ablation studies in the end demonstrate the effectiveness of modules in iGCL

Hemiballism-hemichorea induced by ketotic hyperglycemia: case report with PET study and review of the literature

Hemiballism-hemichorea (HB-HC) is commonly used to describe the basal ganglion dysfunction in non-ketotic hyperglycemic elderly patients. Here we report two elderly female patients with acute onset of involuntary movements induced by hyperglycemia with positive urine ketones. We described the computed tomography and magnetic resonance imaging findings in these two patients, which is similar to that of non-ketotic hyperglycemic HB-HC patients. FDG-PET was performed and the glucose metabolism in the corresponding lesion in these two patients was contradictory with each other. We tried to clarify the underlying mechanisms of HB-HC and explain the contradictory neuroradiological findings in FDG-PET as being performed at different clinical stages