Multi-Hop Routing Mechanism for Reliable Sensor Computing

Current research on routing in wireless sensor computing concentrates on increasing the service lifetime, enabling scalability for large number of sensors and supporting fault tolerance for battery exhaustion and broken nodes. A sensor node is naturally exposed to various sources of unreliable communication channels and node failures. Sensor nodes have many failure modes, and each failure degrades the network performance. This work develops a novel mechanism, called Reliable Routing Mechanism (RRM), based on a hybrid cluster-based routing protocol to specify the best reliable routing path for sensor computing. Table-driven intra-cluster routing and on-demand inter-cluster routing are combined by changing the relationship between clusters for sensor computing. Applying a reliable routing mechanism in sensor computing can improve routing reliability, maintain low packet loss, minimize management overhead and save energy consumption. Simulation results indicate that the reliability of the proposed RRM mechanism is around 25% higher than that of the Dynamic Source Routing (DSR) and ad hoc On-demand Distance Vector routing (AODV) mechanisms

Power-Law Fading of the Frustration Effect in a Periodic Rectangular Superconducting Network with Increasing Aspect Ratio

Journals published by the American Physical Society can be found at http://journals.aps.org

Astrocytic LRP1 mediates brain Aβ clearance and impacts amyloid deposition

Accumulation and deposition of amyloid-β (Aβ) in the brain represent an early and perhaps necessary step in the pathogenesis of Alzheimer's disease (AD). Aβ accumulation leads to the formation of Aβ aggregates, which may directly and indirectly lead to eventual neurodegeneration. While Aβ production is accelerated in many familial forms of early-onset AD, increasing evidence indicates that impaired clearance of Aβ is more evident in late-onset AD. To uncover the mechanisms underlying impaired Aβ clearance in AD, we examined the role of low-density lipoprotein receptor-related protein 1 (LRP1) in astrocytes. Although LRP1 has been shown to play critical roles in brain Aβ metabolism in neurons and vascular mural cells, its role in astrocytes, the most abundant cell type in the brain responsible for maintaining neuronal homeostasis, remains unclear. Here, we show that astrocytic LRP1 plays a critical role in brain Aβ clearance. LRP1 knockdown in primary astrocytes resulted in decreased cellular Aβ uptake and degradation. In addition, silencing of LRP1 in astrocytes led to downregulation of several major Aβ-degrading enzymes, including matrix metalloproteases MMP2, MMP9, and insulin-degrading enzyme. More important, conditional knock-out of theLrp1gene in astrocytes in the background of APP/PS1 mice impaired brain Aβ clearance, exacerbated Aβ accumulation, and accelerated amyloid plaque deposition without affecting its production. Together, our results demonstrate that astrocytic LRP1 plays an important role in Aβ metabolism and that restoring LRP1 expression and function in the brain could be an effective strategy to facilitate Aβ clearance and counter amyloid pathology in AD.SIGNIFICANCE STATEMENTAstrocytes represent a major cell type regulating brain homeostasis; however, their roles in brain clearance of amyloid-β (Aβ) and underlying mechanism are not clear. In this study, we used both cellular models and conditional knock-out mouse models to address the role of a critical Aβ receptor, the low-density lipoprotein receptor-related protein 1 (LRP1) in astrocytes. We found that LRP1 in astrocytes plays a critical role in brain Aβ clearance by modulating several Aβ-degrading enzymes and cellular degradation pathways. Our results establish a critical role of astrocytic LRP1 in brain Aβ clearance and shed light on specific Aβ clearance pathways that may help to establish new targets for AD prevention and therapy.</jats:p

Towards Collaborative Intelligence: Routability Estimation based on Decentralized Private Data

Applying machine learning (ML) in design flow is a popular trend in EDA with various applications from design quality predictions to optimizations. Despite its promise, which has been demonstrated in both academic researches and industrial tools, its effectiveness largely hinges on the availability of a large amount of high-quality training data. In reality, EDA developers have very limited access to the latest design data, which is owned by design companies and mostly confidential. Although one can commission ML model training to a design company, the data of a single company might be still inadequate or biased, especially for small companies. Such data availability problem is becoming the limiting constraint on future growth of ML for chip design. In this work, we propose an Federated-Learning based approach for well-studied ML applications in EDA. Our approach allows an ML model to be collaboratively trained with data from multiple clients but without explicit access to the data for respecting their data privacy. To further strengthen the results, we co-design a customized ML model FLNet and its personalization under the decentralized training scenario. Experiments on a comprehensive dataset show that collaborative training improves accuracy by 11% compared with individual local models, and our customized model FLNet significantly outperforms the best of previous routability estimators in this collaborative training flow.Comment: 6 pages, 2 figures, 5 tables, accepted by DAC'2

6,6′-Dimethyl-2,2′-[oxalylbis(aza­nedi­yl)]dipyridinium dichloride acetonitrile solvate

In the crystal structure of the title compound, C14H16N4O2 2+·2Cl−·CH3CN, weak inter­molecular N—H⋯Cl hydrogen bonds are found between the H atoms bound to the pyridine and amine N atoms and the chloride anions. The asymmetric unit consits of one half cationic mol­ecule which is located on a centre of inversion, one chloride anion in a general position and one half acetonitrile mol­ecule which is located on a twofold axis. Because of symmetry, the C—H hydrogens of the acetonitrile solvent mol­ecule are disordered over two orientations

N-(6-Methyl-2-pyrid­yl)formamide

The mol­ecule of the title compound, C7H8N2O, is essentially planar with a maximum deviation of 0.0439 (1) Å from the best plane. In the crystal, N—H⋯O hydrogen bonds between self-complementary amide groups join mol­ecules into centrosymmetric dimers