An intent-based network virtualization platform for SDN

© 2016 IFIP. Currently, the Software Defined Networking (SDN) paradigm has attracted significant interests from industry and academia as a future network architecture. SDN brings many benefits to network operations and management including programmability, agility, elasticity, and flexibility. With SDN and OpenFlow, one of the promising SDN protocols, software defined Network Virtualization (NV) techniques can be designed and implemented via flow table segmentation to provision independent virtual networks (VNs). In this paper, we propose an intent based virtual network management platform based on software defined NV. The objective of the proposed NV platform is to automate the management and configuration of virtual networks based on high level tenant requirement specifications, called intents. The design and implementation of the platform is based on ONOS, an open-source SDN controller, and OpenVirteX, a network hypervisor. The platform is designed to provide multiple VNs over the same physical infrastructure to multiple tenants

Growth mode mapping and structural properties of controlled perovskite BaTiO₃/SrTiO₃heterostructure

2007-2008 > Academic research: refereed > Publication in refereed journalVersion of RecordPublishe

On the discovery of continuous truth: a semi-supervised approach with partial ground truths

In many applications, the information regarding to the same object can be collected from multiple sources. However, these multi-source data are not reported consistently. In the light of this challenge, truth discovery is emerged to identify truth for each object from multi-source data. Most existing truth discovery methods assume that ground truths are completely unknown, and they focus on the exploration of unsupervised approaches to jointly estimate object truths and source reliabilities. However, in many real world applications, a set of ground truths could be partially available. In this paper, we propose a semi-supervised truth discovery framework to estimate continuous object truths. With the help of ground truths, even a small amount, the accuracy of truth discovery can be improved. We formulate the semi-supervised truth discovery problem as an optimization task where object truths and source reliabilities are modeled as variables. The ground truths are modeled as a regularization term and its contribution to the source weight estimation can be controlled by a parameter. The experiments show that the proposed method is more accurate and efficient than the existing truth discovery methods

Biological potential of polyethylene glycol (Peg)-functionalized graphene quantum dots in in vitro neural stem/progenitor cells

Stem cell therapy is one of the novel and prospective fields. The ability of stem cells to differentiate into different lineages makes them attractive candidates for several therapies. It is essential to understand the cell fate, distribution, and function of transplanted cells in the local microenvironment before their applications. Therefore, it is necessary to develop an accurate and reliable labeling method of stem cells for imaging techniques to track their translocation after transplantation. The graphitic quantum dots (GQDs) are selected among various stem cell labeling and tracking strategies which have high photoluminescence ability, photostability, relatively low cytotoxicity, tunable surface functional groups, and delivering capacity. Since GQDs interact easily with the cell and interfere with cell behavior through surface functional groups, an appropriate surface modification needs to be considered to get close to the ideal labeling nanoprobes. In this study, polyethylene glycol (PEG) is used to improve biocompatibility while simultaneously maintaining the photoluminescent potentials of GQDs. The biochemically inert PEG successfully covered the surface of GQDs. The PEG-GQDs composites show adequate bioimaging capabilities when internalized into neural stem/progenitor cells (NSPCs). Furthermore, the bio-inertness of the PEG-GQDs is confirmed. Herein, we introduce the PEG-GQDs as a valuable tool for stem cell labeling and tracking for biomedical therapies in the field of neural regeneration

Preclinical modeling of chronic inhibition of the Parkinson's disease associated kinase LRRK2 reveals altered function of the endolysosomal system in vivo

The most common mutation in the Leucine-rich repeat kinase 2 gene (LRRK2), G2019S, causes familial Parkinson's Disease (PD) and renders the encoded protein kinase hyperactive. While targeting LRRK2 activity is currently being tested in clinical trials as a therapeutic avenue for PD, to date, the molecular effects of chronic LRRK2 inhibition have not yet been examined in vivo. We evaluated the utility of newly available phospho-antibodies for Rab substrates and LRRK2 autophosphorylation to examine the pharmacodynamic response to treatment with the potent and specific LRRK2 inhibitor, MLi-2, in brain and peripheral tissue in G2019S LRRK2 knock-in mice. We report higher sensitivity of LRRK2 autophosphorylation to MLi-2 treatment and slower recovery in washout conditions compared to Rab GTPases phosphorylation, and we identify pS106 Rab12 as a robust readout of downstream LRRK2 activity across tissues. The downstream effects of long-term chronic LRRK2 inhibition in vivo were evaluated in G2019S LRRK2 knock-in mice by phospho- and total proteomic analyses following an in-diet administration of MLi-2 for 10 weeks. We observed significant alterations in endolysosomal and trafficking pathways in the kidney that were sensitive to MLi-2 treatment and were validated biochemically. Furthermore, a subtle but distinct biochemical signature affecting mitochondrial proteins was observed in brain tissue in the same animals that, again, was reverted by kinase inhibition. Proteomic analysis in the lung did not detect any major pathway of dysregulation that would be indicative of pulmonary impairment. This is the first study to examine the molecular underpinnings of chronic LRRK2 inhibition in a preclinical in vivo PD model and highlights cellular processes that may be influenced by therapeutic strategies aimed at restoring LRRK2 physiological activity in PD patients

Atomic-scale combination of germanium-zinc nanofibers for structural and electrochemical evolution

Alloys are recently receiving considerable attention in the community of rechargeable batteries as possible alternatives to carbonaceous negative electrodes; however, challenges remain for the practical utilization of these materials. Herein, we report the synthesis of germanium-zinc alloy nanofibers through electrospinning and a subsequent calcination step. Evidenced by in situ transmission electron microscopy and electrochemical impedance spectroscopy characterizations, this one-dimensional design possesses unique structures. Both germanium and zinc atoms are homogenously distributed allowing for outstanding electronic conductivity and high available capacity for lithium storage. The as-prepared materials present high rate capability (capacity of similar to 50% at 20 C compared to that at 0.2 C-rate) and cycle retention (73% at 3.0 C-rate) with a retaining capacity of 546 mAh g(-1) even after 1000 cycles. When assembled in a full cell, high energy density can be maintained during 400 cycles, which indicates that the current material has the potential to be used in a large-scale energy storage system

Visible light responsive titanium dioxide (TiO<inf>2</inf>)

Titanium dioxide (TiO2) is one of the most researched semiconductor oxides that has revolutionised technologies in the field of environmental purification and energy generation. It has found extensive applications in heterogenous photocatalysis for removing organic pollutants from air and water and also in hydrogen production from photocatalytic water-splitting. Its use is popular because of its low cost, low toxicity, high chemical and thermal stability, But one of the critical limitations of TiO 2 as photocatalyst is its poor response to visible light. Several attempts have been made to modify the surface and electronic structures of TiO2 to enhance its activity in the visible light region such as noble metal deposition, metal ion loading, cationic and anionic doping and sensitisation, Most of the results improved photocatalytic performance under visible light irradiation. This paper attempts to review and update some of the information on the TiO2 photocatalytic technology and its accomplishment towards visible light region

Benchmark performance of low-cost Sb2Se3 photocathodes for unassisted solar overall water splitting

Determining cost-effective semiconductors exhibiting desirable properties for commercial photoelectrochemical water splitting remains a challenge. Herein, we report a Sb2Se3 semiconductor that satisfies most requirements for an ideal high-performance photoelectrode, including a small band gap and favourable cost, optoelectronic properties, processability, and photocorrosion stability. Strong anisotropy, a major issue for Sb2Se3, is resolved by suppressing growth kinetics via close space sublimation to obtain high-quality compact thin films with favourable crystallographic orientation. The Sb2Se3 photocathode exhibits a high photocurrent density of almost 30mAcm(-2) at 0V against the reversible hydrogen electrode, the highest value so far. We demonstrate unassisted solar overall water splitting by combining the optimised Sb2Se3 photocathode with a BiVO4 photoanode, achieving a solar-to-hydrogen efficiency of 1.5% with stability over 10h under simulated 1 sun conditions employing a broad range of solar fluxes. Low-cost Sb2Se3 can thus be an attractive breakthrough material for commercial solar fuel production. While photoelectrochemical water splitting offers an integrated means to convert sunlight to a renewable fuel, cost-effective light-absorbers are rare. Here, authors report Sb2Se3 photocathodes for high-performance photoelectrochemical water splitting devices

A Polarization-Imaging-Based Machine Learning Framework for Quantitative Pathological Diagnosis of Cervical Precancerous Lesions

Polarization images encode high resolution microstructural information even at low resolution. We propose a framework combining polarization imaging and traditional microscopy imaging, constructing a dual-modality machine learning framework that is not only accurate but also generalizable and interpretable. We demonstrate the viability of our proposed framework using the cervical intraepithelial neoplasia grading task, providing a polarimetry feature parameter to quantitatively characterize microstructural variations with lesion progression in hematoxylin-eosin-stained pathological sections of cervical precancerous tissues. By taking advantages of polarization imaging techniques and machine learning methods, the model enables interpretable and quantitative diagnosis of cervical precancerous lesion cases with improved sensitivity and accuracy in a low-resolution and wide-field system. The proposed framework applies routine image-analysis technology to identify the macro-structure and segment the target region in H&#x0026;E-stained pathological images, and then employs emerging polarization method to extract the micro-structure information of the target region, which intends to expand the boundary of the current image-heavy digital pathology, bringing new possibilities for quantitative medical diagnosis