Virtual Slices Research Articles

Abstract 14436: Effects of Uncertainty in Border Delineation on GLS BL versus LVEF

Background: There are many clinical scenarios in which early detection of small changes in cardiac function can be critical in determining a patient’s treatment. Two of the most common imaging-based metrics in the clinic are LVEF and border-length-based GLS (GLS BL ). The aim of this project was to analyze the inherent biases and uncertainties in GLS BL relative to LVEF with respect to uncertainties in delineation of the endocardial border. Methods: I n silico 3D human LV endocardial surface models were created at end-diastole and end-systole based on MRIs of a healthy human volunteer. Twelve virtual long-axis imaging slices (in a radially-prescribed) were projected through the surface models to generate noise-free endocardial contour points. To simulate border delineation uncertainty, the set of endocardial contour points was subjected to inward and outward shifts of 1-5 mm, incrementally (Fig 1A). The expected uncertainty when using EC is around 3 mm. The % error vs. offsets were calculated for bi-plane GLS BL , bi-plane area-length LVEF AL , and bi-plane method-of-disks LVEF MOD , with 3D conformal surface-fit LVEF SURF as ground-truth. Results: The GLS BL %error vs. offset was linear (R 2 = 1.000) with a slope of 4.92 (Fig. 1B). Each of the LVEF measures were quadratic with offset (R 2 = 1.000), with slopes: LVEF AL : 6.68; LVEF MOD : 6.71; and LVEF SURF : 10.26. Conclusions: GLS BL is less sensitive to boundary delineation uncertainty than LVEF, due to the linear vs. quadratic dependence on offset. LVEF AL and LVEF MOD consistently underestimate true LVEF, making them less sensitive to boundary uncertainty. However, if border uncertainty is negligible, then LVEF is more sensitive than GLS BL to small changes in LV volume, and LVEF SURF is more sensitive than either LVEF AL and LVEF MOD .

Multi-tier multi-tenant network slicing: A multi-domain games approach

The 5G slice networks will play a critical role in meeting the stringent quality-of-service requirements of different use cases, reducing the Capital Expenditure (CapEX) and Operational Expenditure (OpEX) of mobile network operators. Owing to the flexibility and ability of 5G slice networks to meet the needs of different verticals, it attracts new network players and entities to the mobile network ecosystem, and therefore it creates new business models and structures. Motivated by this development, this paper addresses the dynamic resource allocation in a multi-slice multi-tier multi-domain network with different network players. The dynamic resource allocation problem is formulated as a maximum utility optimisation problem from a multiplayer multi-domain perspective. Furthermore, a 3-level hierarchical business model comprising Infrastructure Providers (InPs), Mobile Virtual Network Operators (MVNOs), Service Providers (SPs), and slice users are investigated. We propose two schemes: a multi-tier multi-domain slice user matching game scheme and a distributed backtracking multiplayer multi-domain game scheme in solving the transformed maximum utility optimisation problem. We compare the multi-tier multi-tenant multi-domain game scheme with a Genetic Algorithm (GA) Intelligent Latency-Aware Resource (GI-LARE) allocation scheme, and a static slicing resource allocation scheme via Monte Carlo simulation. Our findings reveal that the proposed scheme significantly outperforms these other schemes.

X-ray microtomography reveals a lattice-like network within aortic elastic lamellae.

The arterial wall consists of three concentric layers: intima, media, and adventitia. Beyond their resident cells, these layers are characterized by an extracellular matrix (ECM), which provides both biochemical and mechanical support. Elastin, the major component of arterial ECM, is present in the medial layer and organized in concentric elastic lamellae that confer resilience to the wall. We explored the arterial wall structures from C57Bl6 (control), db/db (diabetic), and ApoE-/- (atherogenic) mice aged 3months using synchrotron X-ray computed microtomography on fixed and unstained tissues with a large image field (8 mm3 ). This approach combined a good resolution (0.83µm/voxel), large 3D imaging field. and an excellent signal to noise ratio conferred by phase-contrast imaging. We determined from 2D virtual slices that the thickness of intramural ECM structures was comparable between strains but automated image analysis of the 3D arterial volumes revealed a lattice-like network within concentric elastic lamellae. We hypothesize that this network could play a role in arterial mechanics. This work demonstrates that phase-contrast synchrotron X-ray computed microtomography is a powerful technique which to characterize unstained soft tissues.

DICOM 3D viewers, virtual reality or 3D printing - a pilot usability study for assessing the preference of orthopedic surgeons.

As standard practice in orthopedic surgery, the information gathered by analyzing Computer Tomography (CT) 2D images is used for patient diagnosis and planning surgery. Lately, these virtual slices are the input for generating 3D virtual models using DICOM viewers, facilitating spatial orientation, and diagnosis. Virtual Reality (VR) and 3D printing (3DP) technologies are also reported for use in anatomy visualization, medical training, and diagnosis. However, it has not been yet investigated whether the surgeons consider that the advantages offered by 3DP and VR outweigh their development efforts. Moreover, no comparative evaluation for understanding surgeon's preference in using these investigation tools has been performed so far. Therefore, in this paper, a pilot usability test was conducted for collecting surgeons' opinions. 3D models of knee, hip and foot were displayed using DICOM 3D viewer, two VR environments and as 3D-printed replicas. These tools adequacy for diagnosis was comparatively assessed in three cases scenarios, the time for completing the diagnosis tasks was recorded and questionnaires filled in. The time for preparing the models for VR and 3DP, the resources needed and the associated costs were presented in order to provide surgeons with the whole context. Results showed a preference in using desktop DICOM viewer with 3D capabilities along with the information provided by Unity-based VR solution for visualizing the virtual model from various angles challenging to analyze on the computer screen. 3D-printed replicas were considered more useful for physically simulating the surgery than for diagnosis. For the VR and 3DP models, the lack of information on bone quality was considered an important drawback. The following order of using the tools was preferred: DICOM viewer, followed by Unity VR and 3DP.

Augmented reality simulator for CT-guided interventions

IntroductionCT-guided interventions are taught using a mentored approach on real patients. It is well established that simulation is a valuable training tool in medicine. This project assessed the feasibility and acceptance of replicating a CT-guided intervention using a bespoke software application with an augmented reality head-mounted display (ARHMD).MethodsA virtual patient was generated using a CT dataset obtained from The Cancer Imaging Archive. A surface mesh of a virtual patient was projected into the field-of-view of the operator. ChArUco markers, placed on both the needle and agar jelly phantom, were tracked using RGB cameras built into the ARHMD. A virtual CT slice simulating the needle position was generated on voice command. The application was trialled by senior interventional radiologists and trainee radiologists with a structured questionnaire evaluating face validity and technical aspects.ResultsSixteen users trialled the application and feedback was received from all. Eleven felt the accuracy and realism was adequate for training and twelve felt more confident about their CT biopsy skills after this training session.DiscussionThe study showed the feasibility of simulating a CT-guided procedure with augmented reality and that this could be used as a training tool.Key Points• Simulating a CT-guided procedure using augmented reality is possible.• The simulator developed could be an effective training tool for clinical practical skills.• Complexity of cases can be tailored to address the training level demands.

Realization of an ideal Weyl semimetal band in a quantum gas with 3D spin-orbit coupling.

Weyl semimetals are three-dimensional (3D) gapless topological phases with Weyl cones in the bulk band. According to lattice theory, Weyl cones must come in pairs, with the minimum number of cones being two. A semimetal with only two Weyl cones is an ideal Weyl semimetal (IWSM). Here we report the experimental realization of an IWSM band by engineering 3D spin-orbit coupling for ultracold atoms. The topological Weyl points are clearly measured via the virtual slicing imaging technique in equilibrium and are further resolved in the quench dynamics. The realization of an IWSM band opens an avenue to investigate various exotic phenomena that are difficult to access in solids.

Visual Perception: How Better Imaging Can Make Things Worse

Summary Visual search is critical to daily life and to socially important tasks — from cancer screening to airport security. New research shows how a technological advancement can interact with the human visual system to improve search for one type of target while making matters worse for another. Part of the problem is that we are surprisingly bad at knowing where we have looked.

Virtual IoT Service Slice Functions for Multiaccess Edge Computing Platform

With the advancement of the Internet of Things (IoT), different use cases, such as smart factories, smart cities, and smart cars are being developed. Technologies, such as multiaccess edge computing (MEC) and network slicing are being researched to support various vertical use cases. However, simply utilizing network slicing and MEC is insufficient when providing a specific IoT service. Accordingly, there is a need for a method of arranging and operating common service functions (CSFs) used in the IoT platform at the edge of the network. In addition, it is necessary not only to deliver virtual CSFs but also to implement autoscaling policies of these virtual common service resources. This study extends the idea of resource slicing technologies for the IoT service layer. The IoT service layer exposes the virtual IoT CSFs as an IoT slice service to be provided in the form of virtual network functions (VNFs) with MEC applications on top of the network function virtualization infrastructure (NFVI) at the edge of the network. We propose a framework architecture for virtual IoT slice service orchestration, and illustrate how to instantiate the virtual IoT service functions in the existing centralized cloud to the MEC platform. We also propose an elastic computing algorithm of virtual IoT slice services functions(vIoT-SSFs) resources at the NFVI so that the IoT applications and underlying IoT resources can access vIoT-SSFs at the edge of the network.

5G network slices resource orchestration using Machine Learning techniques

To efficiently serve heterogeneous demands in terms of data rate, reliability, latency and mobility, network operators must optimize the utilization of their infrastructure resources. In this context, we propose a framework to orchestrate resources for 5G networks by leveraging Machine Learning (ML) techniques. We start by classifying the demands for resources into groups in order to adequately serve them by dedicated logical virtual networks or Network Slices (NSs). To optimally implement these heterogeneous NSs that share the same infrastructure, we develop a new dynamic slicing approach of Physical Resource Blocks (PRBs). On first hand, we propose a predictive approach to achieve optimal slicing decisions of the PRBs from a limited resource pool. On second hand, we design an admission controller and a slice scheduler and formalize them as Knapsack problems. Finally, we design an adaptive resource manager by leveraging Deep Reinforcement Learning (DRL). Using our 5G experimental prototype based on OpenAirInterface (OAI), we generate a realistic dataset for evaluating ML based approaches as well as two baselines solutions (i.e. static slicing and uninformed random slicing-decisions). Simulation results show that using regression trees for both classification and prediction, coupled with the DRL-based adaptive resource manager, outperform alternative approaches in terms of prediction accuracy, resource smoothing, system utilization and network throughput.

CloudSimHypervisor: Modeling and Simulating Network Slicing in Software-Defined Cloud Networks

Software-Defined Networking (SDN) is an innovative technology which provides a programmable network control which is decoupled from the physical infrastructure. Network Virtualization (NV) is the phenomenon where a given physical network infrastructure and its resources are abstracted to create multiple logical virtual network slices of the underlying substrate. NV enables independent virtual networks to co-exist on one or more shared physical network infrastructure. Edge computing makes use of the edge resources in close proximity to end-users to reduce service delay and the network traffic volume in the end-to-end networks. Similarly, network slicing which is a key enabling technology for 5G networks is designed to support different services from different platforms at different scales enables sharing of physical network infrastructure on many different virtual network layers. These innovative technologies and strategies have gained significant attention from both academia and industry as they have the potential to maximize network resource utilization and optimize end-to-end network service delivery in 5G solutions deployment. To enable continuous simulation and development of applicable 5G networking concepts using these technologies, there is a need for an accessible and easy-to-learn testbed which is able to efficiently measure the performance of physical and virtual network capacities, provisioning approaches and management of multiple architectural models using large-scale network slicing configurations in a repeatable and controllable manner. These tools and toolkits provide scalable, lightweight and controlled cloud simulation environments necessary to analyse network traffic flows, allocation capacities and policies and the behaviour of multiple heterogeneous networks at an extremely low cost as compared to the huge financial commitments involved in conducting similar experiments in a real-life event. Existing solutions do not support Network Slicing and end- to -end heterogenous network automation which are key enablers of 5G network implementation. Hence in this paper, the CloudSimHypervisor framework is developed in this based on CloudSimSDN-NFV. The complete architecture and features of the CloudSimHypervisor framework and some used cases are presented in this paper. We validate the CloudSimHypervisor framework with two use case experiments in the cloud computing environment: Joint compute and network resource utilization and network traffic prioritization. Results from these experiments display the efficiency of the CloudSimHypervisor in estimating and measuring processing speed, transmission speed, compute and network usage efficiency and energy consumption.

Synergetic effects of network interconnections in the conditions of virtual reality

PURPOSE: The disclosure of the content of the synergetic effect, as a result of network interactions of development institutions in a new economic virtual reality, and the presentation of the general characteristics of their relationships through knowledge of the functioning of clusters, which in the XXI century occurs during the digitalization of the economy, resulting in digital products/services and various platforms. METHODOLOGY: On the basis of dialectical, systemic and matrix methods and using the institutional-network approach, the characteristic features of network interactions of cluster formations in the conditions of virtual reality are studied, which are becoming the norm today, a good quality and effective rule for the practical implementation of various sectors of the economy in the course of digitalization of the economy. The method of comparison is used in terms of conditions for the formation of an innovation-digital cluster from the standpoint of the theory of institutionalism. FINDINGS: Network cooperation in the conditions of virtual reality demonstrates synergetic effects through new forms of qualitative accumulation and an increase of new knowledge, which occur through their network replication (division), and innovative growth is the result of the formation in the economy and society of a new, network model of coordination of connections, network cooperation of new quality, constantly adjusted by digital tools. The synergistic effect of networking creates a new phenomenon of growing marginal utility and growing marginal productivity from innovative glocalization and digital globalization. The greater the scale of innovation and digital activities is in the conditions of virtual reality, the greater the efficiency is of the use of additional resources. The effect of scale is especially pronounced within the network, which uses the standards produced and tested by it. IMPLICATIONS FOR THEORY AND PRACTICE: It is proved that the synergetic approach used in the formation and development of innovation-digital clusters is considered through the prism of the relationship “subject – subjective relationship of innovation-active organizations and digital enterprises.” In addition, in our case, this effect lies in the plane of restructuring the “old” development institutions in the “new”, under the influence of the relevant institutional and legal basis, systemic and comprehensive modernization and diversification of all sectors of production, improvement of the innovation and investment situation, construction of effective innovation and digital virtual-real infrastructure of the European standard, implementation of clustering of the economy using the opportunities of network cooperation.. ORIGINALITY AND VALUE: The content of a virtual slice of network interaction of cluster formations in the conditions of virtual reality offered by the authors is revealed; the authors’ vision of its structural elements is given, as from a digital network augmented and virtual socio-economic reality; the taxonomy and categorization of terminology with the help of which it is possible to reveal the formation of network cooperation in the conditions of virtual reality and its further development are investigated; on the basis of the conducted deep theoretical and methodological analysis and the presentation of a retrospective of innovation and digital changes, a step-by-step transformation of cluster formations is shown. The basis of network economy is network institutions, entities, organizations, in addition, it forms an environment in which any business entity or individual, which, no matter where it is in the economic system, has been able to communicate easily and at minimal cost with any other company or individual about working together, trading issues, or know-how, or just for fun in the conditions of the new virtual reality.

OFaaS: OpenFlow Switch as a Service for Multi Tenant Slicing in SD-CDN

In Software Defined-Content Delivery Networks (SD-CDN), the policies of tenants such as Youtube, Netflix, Office 365, etc. are not the same due to having different 5G traffic requirements for such contents as enhanced Mobile Broadband (eMBB), ultra-reliable low-latency (URLLC) and massive machine-type communication (mMTC). This leads SD-CDN multi-tenant slicing to provide different services with limited network resources, where each tenant can functionally manage their own virtual slice of a physical component according to service level agreements (SLAs). However, they are not permitted to dynamically configure their own components. Therefore, the physical end-to-end configuration of all edge devices causes extra hardware and bandwidth costs. Although software as a service (SaaS) is more preferred to handle cost-efficiency on a switch configuration that increases forwarding throughput (Mbps) with a less number of physical components in SD-CDN, the edge devices can be only served as infrastructure as a service (IaaS) currently. This motivation leads us to serve the switch as a service that includes both IaaS and SaaS characteristics. Therefore, we propose an OpenFlow as a service (OFaaS) design where each tenant has flow management and switch configuration permissions on their own virtual slice. In flow management, we define a novel Service Oriented Architecture (SOA) to orchestrate OFaaS driven topology by isolating each tenant from physical complexity. Here, each tenant can dynamically alter QoS on a flow and load balance between a sub-set of contents via OpenFlow protocol. In switch configuration; a novel OFaaS Management Algorithm for a multi-tenant slicing increases the number of tenants served per OpenFlow switch thanks to OFaaS design. It enables an end-to-end configuration via the NETCONF protocol with a novel YANG model of OFaaS. According to performance evaluation; OFaaS has the same forwarding throughput as conventional IaaS based OpenFlow switch for a homogenous content, whereas it has 71% more forwarding throughput (Mbps) and it has 40% more cost-efficient than conventional one for a heterogeneous content with $17 savings per tenant.

SANS: Self-Sovereign Authentication for Network Slices

5G communications proposed significant improvements over 4G in terms of efficiency and security. Among these novelties, the 5G network slicing seems to have a prominent role: deploy multiple virtual network slices, each providing a different service with different needs and features. Like this, a Slice Operator (SO) ruling a specific slice may want to offer a service for users meeting some requirements. It is of paramount importance to provide a robust authentication protocol, able to ensure that users meet the requirements, providing at the same time a privacy-by-design architecture. This makes even more sense having a growing density of Internet of Things (IoT) devices exchanging private information over the network. In this paper, we improve the 5G network slicing authentication using a Self-Sovereign Identity (SSI) scheme: granting users full control over their data. We introduce an approach to allow a user to prove his right to access a specific service without leaking any information about him. Such an approach is SANS, a protocol that provides nonlinkable protection for any issued information, preventing an SO or an eavesdropper from tracking users’ activity and relating it to their real identities. Furthermore, our protocol is scalable and can be taken as a framework for improving related technologies in similar scenarios, like authentication in the 5G Radio Access Network (RAN) or other wireless networks and services. Such features can be achieved using cryptographic primitives called Zero-Knowledge Proofs (ZKPs). Upon implementing our solution using a state-of-the-art ZKP library and performing several experiments, we provide benchmarks demonstrating that our approach is affordable in speed and memory consumption.

Data-Driven Network Slicing From Core to RAN for 5G Broadcasting Services

Network slicing is a widely discussed technology for 5G broadcasting services, as it allows the operators to create end-to-end virtual slices across the 5G core (5GC) and the 5G radio access network (RAN). The joint network slicing is necessary for 5GC and 5G RAN in a bid to enhance the overall resource utilization and quality of service. Nevertheless, as the dimension of network resource and user demand increase, the computational complexity of slice status update goes up as well. To overcome this scalability problem, we propose a data driven approach to realize joint slice reconfiguration from 5GC to 5G RAN in real-time. Our design shortens significantly the time of slice status update by mapping the resource status to a two-dimensional vector graph. In addition, we also introduce a corresponding slice reconfiguration algorithm to balance the load of each slice in 5GC and 5G RAN. Simulation results justify that our proposed approach can greatly reduce the cost of slice status update and achieve desirable slice reconfiguration performance.

Deep Federated Q-Learning-Based Network Slicing for Industrial IoT

Fifth generation and beyond networks are envisioned to support multi industrial Internet of Things (IIoT) applications with a diverse quality-of-service (QoS) requirements. Network slicing is recognized as a flagship technology that enables IIoT networks with multiservices and resource requirements by allowing the network-as-infrastructure transition to the network-as-service. Motivated by the increasing IIoT computational capacity, and taking into consideration the QoS satisfaction and private data sharing challenges, federated reinforcement learning (RL) has become a promising approach that distributes data acquisition and computation tasks over distributed network agents, exploiting local computation capacities and agent's self-learning experiences. This article proposes a novel deep RL scheme to provide a federated and dynamic network management and resource allocation for differentiated QoS services in future IIoT networks. This involves IIoT slices resource allocation in terms of transmission power (TP) and spreading factor (SF) according to the slices QoS requirements. Toward this goal, the proposed deep federated Q-learning (DFQL) is reached into two main steps. First, we propose a multiagent deep Q-learning-based dynamic slices TP and SF adjustment process that aims at maximizing self-QoS requirements in term of throughput and delay. Second, the deep federated learning is proposed to learn multiagent self-model and enable them to find an optimal action decision on the TP and the SF that satisfy IIoT virtual network slice QoS reward, exploiting the shared experiences between agents. Simulation results show that the proposed DFQL framework achieves efficient performance compared to the traditional approaches.

3D histopathology of human tumours by fast clearing and ultramicroscopy

Here, we describe a novel approach that allows pathologists to three-dimensionally analyse malignant tissues, including the tumour-host tissue interface. Our visualization technique utilizes a combination of ultrafast chemical tissue clearing and light-sheet microscopy to obtain virtual slices and 3D reconstructions of up to multiple centimetre sized tumour resectates. For the clearing of tumours we propose a preparation technique comprising three steps: (a) Fixation and enhancement of tissue autofluorescence with formalin/5-sulfosalicylic acid. (b) Ultrafast active chemical dehydration with 2,2-dimethoxypropane and (c) refractive index matching with dibenzyl ether at up to 56 °C. After clearing, the tumour resectates are imaged. The images are computationally post-processed for contrast enhancement and artefact removal and then 3D reconstructed. Importantly, the sequence a–c is fully reversible, allowing the morphological correlation of one and the same histological structures, once visualized with our novel technique and once visualized by standard H&E- and IHC-staining. After reverting the clearing procedure followed by standard H&E processing, the hallmarks of ductal carcinoma in situ (DCIS) found in the cleared samples could be successfully correlated with the corresponding structures present in H&E and IHC staining. Since the imaging of several thousands of optical sections is a fast process, it is possible to analyse a larger part of the tumour than by mechanical slicing. As this also adds further information about the 3D structure of malignancies, we expect that our technology will become a valuable addition for histological diagnosis in clinical pathology.

Nondestructive Determination of Fruit Surface Area Using Archimedean Buoyancy

Estimates of fruit surface area are often required in physiological and technological studies. The objective was to establish a procedure to accurately quantify the fruit surface area based on Archimedean buoyancy measurements. The setup comprised a fixed, linear stepper motor mounted with its spindle vertical and aligned directly above the pan of an electronic balance. A fruit was clamped to the motor spindle and a beaker of water rested on the balance pan. When the motor was activated, the fruit was progressively immersed, stepwise, in the water. Each vertical displacement step increased the buoyant upthrust on the fruit, which was opposed by a corresponding increase in the downthrust on the balance. Pairs of the step displacement length (mm) and corresponding buoyancy increment (g) values were recorded in an MS Excel (Microsoft, Redmond, WA) spreadsheet using Arduino components. Each displacement step immersed another “virtual slice” of the fruit in the water. From each pair of known displacement–buoyancy measurements, the volume (mL) of that slice could be calculated with high precision based on the known density of the liquid (g·mL−1). With the fruit orientated so that its morphological “long” axis was vertical, for most fruitcrop species, the slice can be assumed to have a circular cross-section. Hence, the slice can be analyzed geometrically as a truncated cone of known height (mm) and known volume (mL). Therefore, the surface area of its outer face is calculable. The surface area of the whole fruit was calculated as the cumulative total of the surface areas of all steps (virtual slices). The procedure was evaluated and calibrated using stainless-steel spheres in place of the fruit. However, the measured surface area was slightly greater than that calculated for a sphere. The calculated and measured areas did not differ by more than 1.7%. The surface area determinations were highly reproducible (cv = 0.95%). The magnitude of the displacement steps affected the variability of the surface area measurements. Increasing the step displacements decreased the measurement variability, but there were no significant effects on the surface area measurements of the surface tension of the liquid or of the wettability of the surface of the fruit or the stainless-steel subject. Using stainless-steel spheres (diameter, 5–60 mm) or rubber truncated cones (mean diameter, 8–45 mm) revealed an excellent agreement between the measured and calculated surface areas. Using tomatoes, grapes, blueberries, and strawberries, the measured surface areas were in excellent agreement with those calculated from the fruit dimensions and appropriate geometrical assumptions. The results demonstrate that the surface areas of fruit with approximately circular cross-sections normal to their morphological axes can be determined with high accuracy and reproducibility using Archimedean buoyancy.

Dynamic Virtual Network Slicing and Orchestration for Selective MEC Services over Wide-Area SDN

Multi-access edge computing (MEC) has become an essential technology for collecting, analyzing, and processing data generated by widely distributed user equipment (UE), wireless end-hosts, Internet of things (IoT) sensors, etc., providing real-time and high-quality networking services with ultralow end-to-end latency guaranteed between various user devices and edge cloud computing nodes. However, the cloud resources at the MEC on-site (access point) and edge site are restricted and insufficient mainly because of the operation and management constraints (e.g., limited space and capacity), particularly in the case of on-demand and dynamic service resource deployment. In this regard, we propose a selective MEC resource allocation scheme adopting a multitier architecture over a wide-area software-defined network (SDN) on the basis of our recent research work on virtual network slicing and resource orchestration. The proposed scheme provides an optimized MEC selection model considering end-to-end latency and efficient service resource utilization on the basis of the hierarchical MEC service architecture.

You Calculate and I Provision: A DRL-Assisted Service Framework to Realize Distributed and Tenant-Driven Virtual Network Slicing

This article studies the problem of virtual network (VNT) slicing in datacenter interconnections (DCIs), and proposes a novel service framework to better balance the tradeoff between cost-effectiveness, and time-efficiency. Our idea is to partition a DCI into non-overlapped subgraphs, divide the VNT slicing in each subgraph into four collaborative steps, and get tenants involved in the calculation of virtual network embedding (VNE) schemes. With our proposal, an agent of infrastructure provider (InP) leverages deep reinforcement learning (DRL) to price, and advertise the substrate resources in one subgraph, motivates tenants to request resources in a load-balanced manner, and accepts VNE schemes from the tenants to avoid resource conflicts. Meanwhile, the tenants' task is to compute their own VNE schemes independently, and distributedly according to the resource information (i.e., the available resources, and their prices) advertised by the agent. We first design the DRL model based on the deep deterministic policy gradient (DDPG), and develop a VNT compression method based on auto-encoder (AE) to generalize the DRL's operation. Then, we study how to resolve resource conflicts among the distributedly-calculated VNE schemes, build a conflict graph (CG) to transform the VNE selection into finding the maximum weighted independent set (MWIS) in the CG, and design a polynomial-time approximation algorithm to solve the problem. Extensive simulations confirm that compared with the centralized service framework relying solely on the InP for VNE calculation, our proposed DRL-assisted distributed framework provisions VNT requests with significantly shorter computation time, and comparable blocking performance.

Assessment of plaque morphology in Alzheimer\u2019s mouse cerebellum using three-dimensional X-ray phase-based virtual histology

Visualization and characterization of beta-amyloid deposits is a fundamental task in pre-clinical study of Alzheimer’s disease (AD) to assess its evolution and monitor the efficiency of new therapeutic strategies. While the cerebellum is one of the brain areas most underestimated in the context of AD, renewed interest in cerebellar lesions has recently arisen as they may link to motor and cognitive alterations. Thus, we quantitatively investigated three-dimensional plaque morphology in the cerebellum in APP/PS1 transgenic mouse, as a model of AD. In order to obtain a complete high-resolution three-dimensional view of the investigated tissue, we exploited synchrotron X-ray phase contrast tomography (XPCT), providing virtual slices with histology-matching resolution. We found the formation of plaques elongated in shape, and with a specific orientation in space depending on the investigated region of the cerebellar cortex. Remarkably, a similar shape is observed in human cerebellum from demented patients. Our findings demonstrate the capability of XPCT in volumetric quantification, supporting the current knowledge about plaque morphology in the cerebellum and the fundamental role of the surrounding tissue in driving their evolution. A good correlation with the human neuropathology is also reported.