CLOSER: A Collaborative Locality-aware Overlay SERvice

Current Peer-to-Peer (P2P) file sharing systems make use of a considerable percentage of Internet Service Providers (ISPs) bandwidth. This paper presents the Collaborative Locality-aware Overlay SERvice (CLOSER), an architecture that aims at lessening the usage of expensive international links by exploiting traffic locality (i.e., a resource is downloaded from the inside of the ISP whenever possible). The paper proves the effectiveness of CLOSER by analysis and simulation, also comparing this architecture with existing solutions for traffic locality in P2P systems. While savings on international links can be attractive for ISPs, it is necessary to offer some features that can be of interest for users to favor a wide adoption of the application. For this reason, CLOSER also introduces a privacy module that may arouse the users' interest and encourage them to switch to the new architectur

Healthcare digitalization and pay-for-performance incentives in smart hospital project financing

This study aims to explore the impact of healthcare digitalization on smart hospital project financing (PF) fostered by pay-for-performance (P4P) incentives. Digital platforms are a technology-enabled business model that facilitates exchanges between interacting agents. They represent a bridging link among disconnected nodes, improving the scalable value of networks. Application to healthcare public-private partnerships (PPPs) is significant due to the consistency of digital platforms with health issues and the complexity of the stakeholder’s interaction. In infrastructural PPPs, public and private players cooperate, usually following PF patterns. This relationship is complemented by digitized supply chains and is increasingly patient-centric. This paper reviews the literature, analyzes some supply chain bottlenecks, addresses solutions concerning the networking effects of platforms to improve PPP interactions, and investigates the cost-benefit analysis of digital health with an empirical case. Whereas diagnostic or infrastructural technology is an expensive investment with long-term payback, leapfrogging digital applications reduce contingent costs. “Digital” savings can be shared by key stakeholders with P4P schemes, incentivizing value co-creation patterns. Efficient sharing may apply network theory to a comprehensive PPP ecosystem where stakeholding nodes are digitally connected. This innovative approach improves stakeholder relationships, which are re-engineered around digital platforms that enhance patient-centered satisfaction and sustainability. Digital technologies are useful even for infectious disease surveillance, like that of the coronavirus pandemic, for supporting massive healthcare intervention, decongesting hospitals, and providing timely big data

The Quest for Bandwidth Estimation Techniques for large-scale Distributed Systems

In recent years the research community has developed many techniques to estimate the end-to-end available bandwidth of an Internet path. This important metric has been proposed for use in several distributed systems and, more recently, has even been considered to improve the congestion control mechanism of TCP. Thus, it has been suggested that some existing estimation techniques could be used for this purpose. However, existing tools were not designed for large-scale deployments and were mostly validated in controlled settings, considering only one measurement running at a time. In this paper, we argue that current tools, while offering good estimates when used alone, might not work in large-scale systems where several estimations severely interfere with each other. We analyze the properties of the measurement paradigms employed today and discuss their functioning, study their overhead and analyze their interference. Our testbed results show that current techniques are insufficient as they are. Finally, we will discuss and propose some principles that should be taken into account for including available bandwidth measurements in large-scale distributed systems. 1

Computer architecture for efficient algorithmic executions in real-time systems: New technology for avionics systems and advanced space vehicles

Improvements and advances in the development of computer architecture now provide innovative technology for the recasting of traditional sequential solutions into high-performance, low-cost, parallel system to increase system performance. Research conducted in development of specialized computer architecture for the algorithmic execution of an avionics system, guidance and control problem in real time is described. A comprehensive treatment of both the hardware and software structures of a customized computer which performs real-time computation of guidance commands with updated estimates of target motion and time-to-go is presented. An optimal, real-time allocation algorithm was developed which maps the algorithmic tasks onto the processing elements. This allocation is based on the critical path analysis. The final stage is the design and development of the hardware structures suitable for the efficient execution of the allocated task graph. The processing element is designed for rapid execution of the allocated tasks. Fault tolerance is a key feature of the overall architecture. Parallel numerical integration techniques, tasks definitions, and allocation algorithms are discussed. The parallel implementation is analytically verified and the experimental results are presented. The design of the data-driven computer architecture, customized for the execution of the particular algorithm, is discussed

Differential strengths of selection on S-RNases from Physalis and Solanum (Solanaceae)

<p>Abstract</p> <p>Background</p> <p>The S-RNases of the Solanaceae are highly polymorphic self-incompatibility (S-) alleles subject to strong balancing selection. Relatively recent diversification of S-alleles has occurred in the genus <it>Physalis </it>following a historical restriction of S-allele diversity. In contrast, the genus <it>Solanum </it>did not undergo a restriction of S-locus diversity and its S-alleles are generally much older. Because recovery from reduced S-locus diversity should involve increased selection, we employ a statistical framework to ask whether S-locus selection intensities are higher in <it>Physalis </it>than <it>Solanum</it>. Because different S-RNase lineages diversify in <it>Physalis </it>and <it>Solanum</it>, we also ask whether different sites are under selection in different lineages.</p> <p>Results</p> <p>Maximum-likelihood and Bayesian coalescent methods found higher intensities of selection and more sites under significant positive selection in the 48 <it>Physalis </it>S-RNase alleles than the 49 from <it>Solanum</it>. Highest posterior densities of dN/dS (ω) estimates show that the strength of selection is greater for <it>Physalis </it>at 36 codons. A nested maximum likelihood method was more conservative, but still found 16 sites with greater selection in <it>Physalis</it>. Neither method found any codons under significantly greater selection in <it>Solanum</it>. A random effects likelihood method that examines data from both taxa jointly confirmed higher selection intensities in <it>Physalis</it>, but did not find different proportions of sites under selection in the two datasets. The greatest differences in strengths of selection were found in the most variable regions of the S-RNases, as expected if these regions encode self-recognition specificities. Clade-specific likelihood models indicated some codons were under greater selection in background <it>Solanum </it>lineages than in specific lineages of <it>Physalis </it>implying that selection on sites may differ among lineages.</p> <p>Conclusions</p> <p>Likelihood and Bayesian methods provide a statistical approach to testing differential selection across populations or species. These tests appear robust to the levels of polymorphism found in diverse S-allele collections subject to strong balancing selection. As predicted, the intensity of selection at the S-locus was higher in the taxon with more recent S-locus diversification. This is the first confirmation by statistical test of differing selection intensities among self-incompatibility alleles from different populations or species.</p

A systematic review and critical assessment of incentive strategies for discovery and development of novel antibiotics

Despite the growing threat of antimicrobial resistance, pharmaceutical and biotechnology firms are reluctant to develop novel antibiotics because of a host of market failures. This problem is complicated by public health goals that demand antibiotic conservation and equitable patient access. Thus, an innovative incentive strategy is needed to encourage sustainable investment in antibiotics. This systematic review consolidates, classifies and critically assesses a total of 47 proposed incentives. Given the large number of possible strategies, a decision framework is presented to assist with the selection of incentives. This framework focuses on addressing market failures that result in limited investment, public health priorities regarding antibiotic stewardship and patient access, and implementation constraints and operational realities. The flexible nature of this framework allows policy makers to tailor an antibiotic incentive package that suits a country’s health system structure and needs

Cluster-Wise Ratio Tests for Fast Camera Localization

Feature point matching for camera localization suffers from scalability problems. Even when feature descriptors associated with 3D scene points are locally unique, as coverage grows, similar or repeated features become increasingly common. As a result, the standard distance ratio-test used to identify reliable image feature points is overly restrictive and rejects many good candidate matches. We propose a simple coarse-to-fine strategy that uses conservative approximations to robust local ratio-tests that can be computed efficiently using global approximate k-nearest neighbor search. We treat these forward matches as votes in camera pose space and use them to prioritize back-matching within candidate camera pose clusters, exploiting feature co-visibility captured by clustering the 3D model camera pose graph. This approach achieves state-of-the-art camera localization results on a variety of popular benchmarks, outperforming several methods that use more complicated data structures and that make more restrictive assumptions on camera pose. We also carry out diagnostic analyses on a difficult test dataset containing globally repetitive structure that suggest our approach successfully adapts to the challenges of large-scale image localization

The Internet-Wide Impact of P2P Traffic Localization on ISP Profitability

We conduct a detailed simulation study to examine how localizing P2P traffic within network boundaries impacts the profitability of an ISP. A distinguishing aspect of our work is the focus on Internet-wide implications, i.e., how adoption of localization within an ISP affects both itself and other ISPs. Our simulations are based on detailed models that estimate inter-autonomous-system (AS) P2P traffic and inter-AS routing, localization models that predict the extent to which P2P traffic is reduced, and pricing models that predict the impact of changes in traffic on the profit of an ISP. We evaluate our models by using a large-scale crawl of BitTorrent containing over 138 million users sharing 2.75 million files. Our results show that the benefits of localization must not be taken for granted. Some of our key findings include: 1) residential ISPs can actually lose money when localization is employed, and some of them will not see increased profitability until other ISPs employ localization; 2) the reduction in costs due to localization will be limited for small ISPs and tends to grow only logarithmically with client population; and 3) some ISPs can better increase profitability through alternate strategies to localization by taking advantage of the business relationships they have with other ISP