Network Proxy Research Articles

Changes in El Niño – Southern Oscillation (ENSO) conditions during the Greenland Stadial 1 (GS-1) chronozone revealed by New Zealand tree-rings

The warming trend at the end of the last glacial was disrupted by rapid cooling clearly identified in Greenland (Greenland Stadial 1 or GS-1) and Europe (Younger Dryas Stadial or YD). This reversal to glacial-like conditions is one of the best known examples of abrupt change but the exact timing and global spatial extent remain uncertain. Whilst the wider Atlantic region has a network of high-resolution proxy records spanning GS-1, the Pacific Ocean suffers from a scarcity of sub-decadally resolved sequences. Here we report the results from an investigation into a tree-ring chronology from northern New Zealand aimed at addressing the paucity of data. The conifer tree species kauri (Agathis australis) is known from contemporary studies to be sensitive to regional climate changes. An analysis of a ‘historic’ 452-year kauri chronology confirms a tropical-Pacific teleconnection via the El Nino – Southern Oscillation (ENSO). We then focus our study on a 1010-year sub-fossil kauri chronology that has been precisely dated by comprehensive radiocarbon dating and contains a striking ring-width downturn between ∼12,500 and 12,380 cal BP within GS-1. Wavelet analysis shows a marked increase in ENSO-like periodicities occurring after the downturn event. Comparison to low- and mid-latitude Pacific records suggests a coherency with ENSO and Southern Hemisphere atmospheric circulation change during this period. The driver(s) for this climate event remain unclear but may be related to solar changes that subsequently led to establishment and/or increased expression of ENSO across the mid-latitudes of the Pacific, seemingly independent of the Atlantic and polar regions.

Australasian Temperature Reconstructions Spanning the Last Millennium

Abstract Multiproxy warm season (September–February) temperature reconstructions are presented for the combined land–ocean region of Australasia (0°–50°S, 110°E–180°) covering 1000–2001. Using between 2 (R2) and 28 (R28) paleoclimate records, four 1000-member ensemble reconstructions of regional temperature are developed using four statistical methods: principal component regression (PCR), composite plus scale (CPS), Bayesian hierarchical models (LNA), and pairwise comparison (PaiCo). The reconstructions are then compared with a three-member ensemble of GISS-E2-R climate model simulations and independent paleoclimate records. Decadal fluctuations in Australasian temperatures are remarkably similar between the four reconstruction methods. There are, however, differences in the amplitude of temperature variations between the different statistical methods and proxy networks. When the R28 network is used, the warmest 30-yr periods occur after 1950 in 77% of ensemble members over all methods. However, reconstructions based on only the longest records (R2 and R3 networks) indicate that single 30- and 10-yr periods of similar or slightly higher temperatures than in the late twentieth century may have occurred during the first half of the millennium. Regardless, the most recent instrumental temperatures (1985–2014) are above the 90th percentile of all 12 reconstruction ensembles (four reconstruction methods based on three proxy networks—R28, R3, and R2). The reconstructed twentieth-century warming cannot be explained by natural variability alone using GISS-E2-R. In this climate model, anthropogenic forcing is required to produce the rate and magnitude of post-1950 warming observed in the Australasian region. These paleoclimate results are consistent with other studies that attribute the post-1950 warming in Australian temperature records to increases in atmospheric greenhouse gas concentrations.

Can a paleodrought record be used to reconstruct streamflow?: A case study for the Missouri River Basin

AbstractRecent advances in paleoclimatology have revealed dramatic long‐term hydroclimatic variations that provide a context for limited historical records. A notable data set derived from a relatively dense network of paleoclimate proxy records in North America is the Living Blended Drought Atlas (LBDA): a gridded tree‐ring‐based reconstruction of summer Palmer Drought Severity Index. This index has been used to assess North American drought frequency, persistence, and spatial extent over the past two millennia. Here, we explore whether the LBDA can be used to reconstruct annual streamflow. Relative to streamflow reconstructions that use tree rings within the river basin of interest, the use of a gridded proxy poses a novel challenge. The gridded series have high spatial correlation, since they rely on tree rings over a common radius of influence. A novel algorithm for reconstructing streamflow using regularized canonical regression and inputs of local and global covariates is developed and applied over the Missouri River Basin, as a test case. Effectiveness in reconstruction is demonstrated with reconstructions showing periods where streamflow deficits may have been more severe than during recent droughts (e.g., the Civil War, Dust Bowl, and 1950s droughts). The maximum persistence of droughts and floods over the past 500 years far exceeds those observed in the instrumental record and periods of multidecadal variability in the 1500s and 1600s are detected. Challenges for an extension to a national streamflow reconstruction or applications using other gridded paleoclimate data sets such as adequate spatial coverage of streamflow and applicability of annual reconstructions are discussed.

Network assisted latency reduction for mobile web browsing

To load a webpage, a web browser first downloads the base HTML file of the page in order to discover the list of objects referenced in the page. This process takes roughly one round-trip time and constitutes a significant portion of the web browsing delay on mobile devices as wireless networks suffer from longer transmission and access delays compared to wired networks. In this work, we propose a solution for eliminating this initial delay, which is transparent to end systems, does not require modifying HTTP, and is well suited for web browsing on mobile devices. Our solution, called WebPro, relies on a network proxy that builds an up-to-date database of resource lists for the websites visited frequently by network users. The proxy resides in the wired part of the network, and hence can afford to pro-actively build and refresh the resource list database periodically. When a request for a webpage comes to the proxy, it simultaneously fetches the base HTML and all referenced objects required to render the webpage using the corresponding resource list stored in the local database. We also show that the benefits of WebPro become more significant by increasing the complexity of webpages as it is able to circumvent the inter-object dependencies in a webpage. We have built a working prototype of WebPro and have used real-world traffic traces along with live experiments over Wi-Fi and LTE networks to assess its performance. Our results show an average of 26% reduction in page load time for a mix of popular web sites chosen from categories such as news, sports and shopping. Moreover, in comparison to another best known proxy-based solution, WebPro provides delay reductions ranging from 5% to 51% for a variety of web sites.

Current Networks of Long Proxies for Building Reconstruction Models of the Atlantic Multidecadal Oscillation

Currently available proxies were studied as networks for building reconstruction models of the Atlantic Multidecadal Oscillation (AMO). Only proxies that would double the current record length (backwards in time from AD 1564) were included. We present two proxy networks and corresponding reconstruction (transfer) models, one for tree-growth based proxies only and another for multiproxies. Both of them show a useful match in timing as well as amplitude with the AMO. These model structures demonstrated reasonable model performance (overall r2 = 0.45 - 0.36). The time stability of proxy-AMO relationships was also validated. The new models produced acceptable results in cross-calibration-verification (reduction of error and coefficient of efficiency statistics in 1856-1921 and 1922-1990 vary between 0.41 and 0.21). The spatial distribution of these data series indicate that proxies respond to an AMO-like climatic oscillation over much of the Northern Hemisphere.

Fragility of reconstructed temperature patterns over the Common Era: Implications for model evaluation

AbstractClimate field reconstructions(CFRs) enable spatially resolved estimates of past climates, providing important insights about climate variability over the Common Era. In particular, a reconstructed “La Niña‐like” pattern during the transition from the Medieval Climate Anomaly (MCA) to the Little Ice Age has been widely tied to medieval droughts in southwest North America. This pattern is now used as a key benchmark for global climate model simulations of the last millennium, which have yet to reproduce it. Here we test the pattern's robustness by using four different CFR methods and two proxy networks. With the older network, we find the reconstructed patterns to be highly method‐dependent, with the La Niña‐like pattern not reproduced by two of the CFR methodologies. With the updated proxy network, a globally uniform MCA emerges with all methods, in agreement with simulations from the Paleoclimate Modelling Intercomparison Project Phase 3 ensemble. Our results caution against drawing dynamical interpretations from a single CFR and affirm the importance of developing CFRs through improved statistical methodology and community‐driven proxy syntheses.

Statistical paleoclimate reconstructions via Markov random fields

Understanding centennial scale climate variability requires data sets that are accurate, long, continuous and of broad spatial coverage. Since instrumental measurements are generally only available after 1850, temperature fields must be reconstructed using paleoclimate archives, known as proxies. Various climate field reconstructions (CFR) methods have been proposed to relate past temperature to such proxy networks. In this work, we propose a new CFR method, called GraphEM, based on Gaussian Markov random fields embedded within an EM algorithm. Gaussian Markov random fields provide a natural and flexible framework for modeling high-dimensional spatial fields. At the same time, they provide the parameter reduction necessary for obtaining precise and well-conditioned estimates of the covariance structure, even in the sample-starved setting common in paleoclimate applications. In this paper, we propose and compare the performance of different methods to estimate the graphical structure of climate fields, and demonstrate how the GraphEM algorithm can be used to reconstruct past climate variations. The performance of GraphEM is compared to the widely used CFR method RegEM with regularization via truncated total least squares, using synthetic data. Our results show that GraphEM can yield significant improvements, with uniform gains over space, and far better risk properties. We demonstrate that the spatial structure of temperature fields can be well estimated by graphs where each neighbor is only connected to a few geographically close neighbors, and that the increase in performance is directly related to recovering the underlying sparsity in the covariance of the spatial field. Our work demonstrates how significant improvements can be made in climate reconstruction methods by better modeling the covariance structure of the climate field.

Establishing the skill of climate field reconstruction techniques for precipitation with pseudoproxy experiments

This study aims at assessing the skill of several climate field reconstruction techniques (CFR) to reconstruct past precipitation over continental Europe and the Mediterranean at seasonal time scales over the last two millennia from proxy records. A number of pseudoproxy experiments are performed within the virtual reality of a regional paleoclimate simulation at 45 km resolution to analyse different aspects of reconstruction skill. Canonical Correlation Analysis (CCA), two versions of an Analog Method (AM) and Bayesian hierarchical modeling (BHM) are applied to reconstruct precipitation from a synthetic network of pseudoproxies that are contaminated with various types of noise. The skill of the derived reconstructions is assessed through comparison with precipitation simulated by the regional climate model. Unlike BHM, CCA systematically underestimates the variance. The AM can be adjusted to overcome this shortcoming, presenting an intermediate behaviour between the two aforementioned techniques. However, a trade-off between reconstruction-target correlations and reconstructed variance is the drawback of all CFR techniques. CCA (BHM) presents the largest (lowest) skill in preserving the temporal evolution, whereas the AM can be tuned to reproduce better correlation at the expense of losing variance. While BHM has been shown to perform well for temperatures, it relies heavily on prescribed spatial correlation lengths. While this assumption is valid for temperature, it is hardly warranted for precipitation. In general, none of the methods outperforms the other. All experiments agree that a dense and regularly distributed proxy network is required to reconstruct precipitation accurately, reflecting its high spatial and temporal variability. This is especially true in summer, when a specifically short de-correlation distance from the proxy location is caused by localised summertime convective precipitation events.

Ocean surface temperature variability: large model-data differences at decadal and longer periods.

The variability of sea surface temperatures (SSTs) at multidecadal and longer timescales is poorly constrained, primarily because instrumental records are short and proxy records are noisy. Through applying a new noise filtering technique to a global network of late Holocene SST proxies, we estimate SST variability between annual and millennial timescales. Filtered estimates of SST variability obtained from coral, foraminifer, and alkenone records are shown to be consistent with one another and with instrumental records in the frequency bands at which they overlap. General circulation models, however, simulate SST variability that is systematically smaller than instrumental and proxy-based estimates. Discrepancies in variability are largest at low latitudes and increase with timescale, reaching two orders of magnitude for tropical variability at millennial timescales. This result implies major deficiencies in observational estimates or model simulations, or both, and has implications for the attribution of past variations and prediction of future change.

Stateful traffic replay for web application proxies

AbstractIt is a common practice to test a network device by replaying network traffic onto it and observe its reactions. Many replay tools support Transmission Control Protocol/Internet Protocol stateful traffic replay and hence can be used to test switches, routers, and gateway devices. However, they often fail if the device under test (DUT) is an application level proxy. In this paper, we design and implement ProxyReplay to replay application‐layer traffic for network proxies. As many application proxies have built‐in security functions, the main purpose of this tool is to evaluate the security functionalities of DUTs using payloads constructed from real network traces. ProxyReplay modifies requests and responses and maintains queues for request‐response pairs to resolve the issues of protocol dependency, functional dependency, concurrent replay, and error resistance. The solution provides two replay modes, that is, the preprocess mode and the concurrent mode. Depending on the benchmark scenario, we show that the preprocess mode is better for benchmarking the performance capability of a DUT. In contrast, the concurrent mode is used when the replayed trace file is extremely large. Our experiments show 99% accuracy. In addition, the replay performance exceeds 320 Mbps by running the benchmark with an off‐the‐shelf personal computer in the preprocess mode. Copyright © 2014 John Wiley & Sons, Ltd.

Comparing direct, network scale-up, and proxy respondent methods in estimating risky behaviors among collegians

Background and aim: Various methods of estimating the size of hidden populations are prone to different degrees of information biases. This study aimed to assess such biases.Methods: We estimated the prevalence of risky behaviors using direct, modified network scale-up (NSU) and proxy respondent method (PRM) among college students.Results: The network sizes of the male and female students were 25.8 and 29.5. We found alcohol use was the most prevalent risky behaviors among males (PRM: 18.12%, NSU: 8.68%, direct: 13.4%) and the extra-marital sex was the most prevalent one among females (PRM: 3.47%, NSU: 0.95%, direct: 1.4%). Based on PRM, intravenous drug use (IDU) was the least prevalent behavior among college students.Conclusion: We concluded that direct and NSU methods might underestimate the results.

The backbone of bipartite projections: Inferring relationships from co-authorship, co-sponsorship, co-attendance and other co-behaviors

The analysis and visualization of weighted networks pose many challenges, which have led to the development of techniques for extracting the network's backbone, a subgraph composed of only the most significant edges. Weighted edges are particularly common in bipartite projections (e.g. networks of co-authorship, co-attendance, co-sponsorship), which are often used as proxies for one-mode networks where direct measurement is impractical or impossible (e.g. networks of collaboration, friendship, alliance). However, extracting the backbone of bipartite projections requires special care. This paper reviews existing methods for extracting the backbone from bipartite projections, and proposes a new method that aims to overcome their limitations. The stochastic degree sequence model (SDSM) involves the construction of empirical edge weight distributions from random bipartite networks with stochastic marginals, and is demonstrated using data on bill sponsorship in the 108th U.S. Senate. The extracted backbone's validity as a network reflecting political alliances and antagonisms is established through comparisons with data on political party affiliations and political ideologies, which offer an empirical ground-truth. The projection and backbone extraction methods discussed in this paper can be performed using the -onemode- command in Stata.

Contributions of solar and greenhouse gases forcing during the present warm period

Due to the dramatic increase in the global mean surface temperature (GMST) during the twentieth century, the climate science community has endeavored to determine which mechanisms are responsible for global warming. By analyzing a millennium simulation (the period of 1000–1990 ad) of a global climate model and global climate proxy network dataset, we estimate the contribution of solar and greenhouse gas forcings on the increase in GMST during the present warm period (1891–1990 ad). Linear regression analysis reveals that both solar and greenhouse gas forcing considerably explain the increase in global mean temperature during the present warm period, respectively, in the global climate model. Using the global climate proxy network dataset, on the other hand, statistical approach suggests that the contribution of greenhouse gas forcing is slightly larger than that of solar forcing to the increase in global mean temperature during the present warm period. Overall, our result indicates that the solar forcing as well as the anthropogenic greenhouse gas forcing plays an important role to increase the global mean temperature during the present warm period.

Mobility in wireless sensor networks – Survey and proposal

Targeting an increasing number of potential application domains, wireless sensor networks (WSN) have been the subject of intense research, in an attempt to optimize their performance while guaranteeing reliability in highly demanding scenarios. However, hardware constraints have limited their application, and real deployments have demonstrated that WSNs have difficulties in coping with complex communication tasks – such as mobility – in addition to application-related tasks. Mobility support in WSNs is crucial for a very high percentage of application scenarios and, most notably, for the Internet of Things. It is, thus, important to know the existing solutions for mobility in WSNs, identifying their main characteristics and limitations. With this in mind, we firstly present a survey of models for mobility support in WSNs. We then present the Network of Proxies (NoP) assisted mobility proposal, which relieves resource-constrained WSN nodes from the heavy procedures inherent to mobility management. The presented proposal was implemented and evaluated in a real platform, demonstrating not only its advantages over conventional solutions, but also its very good performance in the simultaneous handling of several mobile nodes, leading to high handoff success rate and low handoff time.

Inter-hemispheric temperature variability over the past millennium

The Earth’s climate system is driven by a complex interplay of internal chaotic dynamics and natural and anthropogenic external forcing. Recent instrumental data have shown a remarkable degree of asynchronicity between Northern Hemisphere and Southern Hemisphere temperature fluctuations, thereby questioning the relative importance of internal versus external drivers of past as well as future climate variability1, 2, 3. However, large-scale temperature reconstructions for the past millennium have focused on the Northern Hemisphere4, 5, limiting empirical assessments of inter-hemispheric variability on multi-decadal to centennial timescales. Here, we introduce a new millennial ensemble reconstruction of annually resolved temperature variations for the Southern Hemisphere based on an unprecedented network of terrestrial and oceanic palaeoclimate proxy records. In conjunction with an independent Northern Hemisphere temperature reconstruction ensemble5, this record reveals an extended cold period (1594–1677) in both hemispheres but no globally coherent warm phase during the pre-industrial (1000–1850) era. The current (post-1974) warm phase is the only period of the past millennium where both hemispheres are likely to have experienced contemporaneous warm extremes. Our analysis of inter-hemispheric temperature variability in an ensemble of climate model simulations for the past millennium suggests that models tend to overemphasize Northern Hemisphere–Southern Hemisphere synchronicity by underestimating the role of internal ocean–atmosphere dynamics, particularly in the ocean-dominated Southern Hemisphere. Our results imply that climate system predictability on decadal to century timescales may be lower than expected based on assessments of external climate forcing and Northern Hemisphere temperature variations5, 6 alone.

Questions of trust in health research on social capital: What aspects of personal network social capital do they measure?

Health research on personal social capital has often utilized measures of respondents' perceived trust of others as either a proxy for one's social capital in the absence of more focused measures or as a subjective component of social capital. Little empirical work has evaluated the validity of such practices. We test the construct validity of two trust measures used commonly in health research on social capital—generalized trust and trust of neighbors—with respect to measures of people's general network-, organization-, family-, friend-, and neighborhood-based social capital and the extent to which these two trust measures are associated with self-rated general health and mental health when social capital measures are included in the same models. Analyses of 2008 Canadian General Social Survey data (response rate 57.3%) indicate that generalized trust and trust of neighbors are both positively—yet modestly—associated with measures of several domains of network-based social capital. Both trust measures are positively associated with general and mental health, but these associations remain robust after adjusting for social capital measures. Our findings suggest that (a) trust is conceptually distinct from social capital, (b) trust measures are inadequate proxies for actual personal social networks, and (c) trust measures may only be capturing psychological aspects relevant to—but not indicative of—social capital. Though links between perceived trust and health deserve study, health research on social capital needs to utilize measures of respondents' actual social networks and their inherent resources.

Design and Implementation of Network Proxy Protocols for Energy Saving of Network Terminal

그린 네트워킹 기술은 ICT의 핵심인 네트워킹 분야에서 에너지 효율적인 네트워킹 기술로서 기존 네트워킹 기술과 동일한 성능을 나타내지만 에너지를 더 적게 사용하는 네트워킹 기술이다. 그린 네트워킹은 자원 통합, 선택적 네트워크 연결성 제공, 가상화와 에너지 비례 컴퓨팅으로 구분될 수 있다. 본 논문에서는 그린 네트워킹 분야 중 선택적 연결성 제공 기술에 해당하는 네트워크 단말들의 에너지 절감을 위한 네트워크 프록시 기술을 분석하고 프록시 개발을 위한 요구 사항을 도출하였다. 또한 도출된 결과에 따라서 네트워크 프록시 프로토콜을 설계하고 기능 검증을 위해 실험적인 프로토콜 구현과 테스트를 실행하였으며 제안한 프록시 프로토콜이 정상적으로 동작하는 것을 확인할 수 있었다.

Network proxy research and implementation for Internet of things applications based on constrained application protocol

To solve the problem of high technology bottleneck caused by the three-layer research and development independence of Internet of Things( IoT), a solution based on Constrained Application Protocol( CoAP) was proposed. This solution developed CoAP-HTTP network proxy based on CoAP implementation, which enabled users to access the IoT node resources through the browser directly, to discover resources, to query data and to subscribe resources, etc. The test results show that the proxy mode does not affect the response rate of the system and it is stable enough to support multiple users accessing the network node data at the same time. The CoAP Agent model can help application developers circumvent the complexity of low-level software development and data exchange development; hence they are able to generate a new application independently. The CoAP Agent model provides a new way for the application development of IoT.

A fast algorithm of bitstream extraction using distortion prediction based on simulated annealing

Scalable video streams can be extracted to meet the bandwidth limitation of different networks and end-users. Bitstream extraction is usually performed at the network proxy or gateway during transmission, where a low computational complexity is always preferred. How to quickly and accurately select the best resolution combination for a video to meet different bandwidth requirements by each user is crucial in bitstream extraction. In this paper a fast algorithm of bitstream extraction for scalable video is proposed. The interlayer dependency between the base quality layer and the first quality layer was used to predict the distortion of higher quality layers. When quality of every layer is available, the proposed method searches for the optimized combination of quality layers based on simulated annealing. Experimental results show that the proposed method provides an optimized performance, which is significantly higher than that can be achieved by the basic extraction method. Compared to the quality layer based extraction method in the reference software model of H.264/SVC (i.e., JSVM), the proposed algorithm can greatly decrease the decoding times from 2NT to only 2 without losing rate-distortion performance. Furthermore, the proposed method obtains a more smoothed video quality which is always favorable to the observer.

Interaction relationships of caches in agent-based HD video surveillance: Discovery and utilization

In the research of networked HD video surveillance, various caches embedded in the IP camera, the data management server and the network proxy server are used for lossless video storage and complete playback. How to utilize agent technology to achieve effective and collaborative caching in the end-to-end process of video capture, network data transmission and video data management, has been considered as an emerging solution. However, on one hand, according to the cache's location in HD video surveillance implementation, all caches can form a top-down hierarchical structure in a vertical direction; while on the other hand, according to the cache's storage performance such as free storage space, “similar” caches at the same level can be grouped by clustering in a horizontal direction. Hence, how to discover the complex interaction relationships among caches by considering both vertical and horizontal directions and further effectively make optimized video stream transmission though collaborative caching is still an open and challenging problem. In this paper, we designed an agent-based collaborative caching model, in which three roles of agents: the Capturer Agent, the Transmitter Agent and the Manager Agent are developed for discovering the intrinsic interaction relationships of caches. By utilizing the relationship, approaches to controlling caching activities and planning video stream transmission are further proposed. The case study and experimental evaluations demonstrate the capability of the proposed approach for HD video surveillance.