Field sweep rate dependence of the coercive field of single-molecule magnets: a classical approach with applications to the quantum regime

A method, based on the Neel-Brown model of thermally activated magnetization reversal of a magnetic single-domain particle, is proposed to study the field sweep rate dependence of the coercive field of single-molecule magnets (SMMs). The application to Mn12 and Mn84 SMMs allows the determination of the important parameters that characterize the magnetic properties: the energy barrier, the magnetic anisotropy constant, the spin, tau_0, and the crossover temperature from the classical to the quantum regime. The method may be particularly valuable for large SMMs that do not show quantum tunneling steps in the hysteresis loops.Comment: 6 pages, 6 figure

Resource Provisioning and Allocation in Function-as-a-Service Edge-Clouds

Edge computing has emerged as a new paradigm to bring cloud applications closer to users for increased performance. Unlike back-end cloud systems which consolidate their resources in a centralized data center location with virtually unlimited capacity, edge-clouds comprise distributed resources at various computation spots, each with very limited capacity. In this paper, we consider Function-as-a-Service (FaaS) edge-clouds where application providers deploy their latency-critical functions that process user requests with strict response time deadlines. In this setting, we investigate the problem of resource provisioning and allocation. After formulating the optimal solution, we propose resource allocation and provisioning algorithms across the spectrum of fully-centralized to fully-decentralized. We evaluate the performance of these algorithms in terms of their ability to utilize CPU resources and meet request deadlines under various system parameters. Our results indicate that practical decentralized strategies, which require no coordination among computation spots, achieve performance that is close to the optimal fully-centralized strategy with coordination overheads

Diagnosing and correcting the effects of multicollinearity:Bayesian implications of ridge regression

When faced with the problem of multicollinearity most tourism researchers recommend mean-centering the variables. This procedure however does not work. It is actually one of the biggest misconceptions we have in the field. We propose instead using Bayesian ridge regression and treat the biasing constant as a parameter about which inferences are to be made. It is well known that many estimates of the biasing constant have been proposed in the literature. When the coefficients in ridge regression have a conjugate prior distribution, formal selection can be based on the marginal likelihood. In the non-conjugate case, we propose a conditionally conjugate prior for the biasing constant, and show that Gibbs sampling can be employed to make inferences about ridge regression parameters as well as the biasing constant itself. We examine posterior sensitivity and apply the techniques to a tourism data set

FogSpot: Spot Pricing for Application Provisioning in Edge/Fog Computing

An increasing number of Low Latency Applications (LLAs) in the entertainment, IoT, and automotive domains require response times that challenge the traditional application provisioning using distant Data Centres. Fog computing paradigm extends cloud computing at the edge and middle-tier locations of the network, providing response times an order of magnitude smaller than those that can be achieved by the current "client-to-cloud" network model. Here, we address the challenges of provisioning heavily stateful LLA in the setting where fog infrastructure consists of third-party computing resources, i.e., cloudlets, that comes in the form of "data centres in the box". We introduce FogSpot, a charging mechanism for on-path, on-demand, application provisioning. In FogSpot, cloudlets offer their resources in the form of Virtual Machines (VMs) via markets, collocated with the cloudlets, that interact with forwarded users' application requests for VMs in real time. FogSpot associates each cloudlet with a spot price based on current application requests. The proposed mechanism's design takes into account the characteristics of cloudlets' resources, such as their limited elasticity, and LLAs' attributes, like the expected QoS gain and engagement duration. Lastly, FogSpot guarantees end users' requests truthfulness while focusing in maximising either each cloudlet's revenue or resource utilisation

Proof-of-Prestige: A Useful Work Reward System for Unverifiable Tasks

As cryptographic tokens and altcoins are increasingly being built to serve as utility tokens, the notion of useful work consensus protocols is becoming ever more important. With useful work consensus protocols, users get rewards after they have carried out some specific tasks useful for the network. While in some cases the proof of some utility or service can be provided, the majority of tasks are impossible to verify reliably. To deal with such cases, we design “Proof-of-Prestige” (PoP)—a reward system that can run directly on Proof-of-Stake (PoS) blockchains or as a smart contract on top of Proof-of-Work (PoW) blockchains. PoP introduces “prestige,” which is a volatile resource that, in contrast to coins, regenerates over time. Prestige can be gained by performing useful work, spent when benefiting from services, and directly translates to users minting power. Our scheme allows us to reliably reward decentralized workers while keeping the system free for the end-users. PoP is resistant against Sybil and collusion attacks and can be used with a vast range of unverifiable tasks. We build a simulator to assess the cryptoeconomic behavior of the system and deploy a full prototype of a content dissemination platform rewarding its participants. We implement the blockchain component on both Ethereum (PoW) and Cosmos (PoS), provide a mobile application, and connect it with our scheme with a negligible memory footprint. Finally, we adapt a fair exchange protocol allowing us to atomically exchange files for rewards also in scenarios where not all the parties have Internet connectivity. Our evaluation shows that even for large Ethereum traces, PoP introduces sub-millisecond computational overhead for miners in Cosmos and less than 0.013$ smart contract invocation cost for users in Ethereum