Scalable and Accurate Density-Peaks Clustering on Fully Dynamic Data

Clustering is a primitive and important operator that analyzes a given dataset to discover its hidden patterns and features. Because datasets are usually updated dynamically (i.e., it accepts continuous insertions and arbitrary deletions), analyzing such dynamic data is also an important topic, and dynamic clustering effectively supports it, but is a challenging problem. In this paper, we consider the problem of density-peaks clustering (DPC) on dynamic data. DPC is one of the density-based clustering algorithms and attracts attention for many applications, due to its effectiveness. We investigate the hardness of this problem theoretically to measure the efficiencies of dynamic DPC algorithms. We prove that any exact solutions are costly, and propose an approximation algorithm to enable faster updates. We conduct experiments on real datasets, and the results confirm that our algorithm is much faster and more accurate than state-of-the-art.Amagata D., . Scalable and Accurate Density-Peaks Clustering on Fully Dynamic Data. Proceedings - 2022 IEEE International Conference on Big Data, Big Data 2022 , 445 (2022); https://doi.org/10.1109/BigData55660.2022.10020690

Fast Density-Peaks Clustering: Multicore-based Parallelization Approach

Clustering multi-dimensional points is a fundamental task in many fields, and density-based clustering supports many applications as it can discover clusters of arbitrary shapes. This paper addresses the problem of Density-Peaks Clustering (DPC), a recently proposed density-based clustering framework. Although DPC already has many applications, its straightforward implementation incurs a quadratic time computation to the number of points in a given dataset, thereby does not scale to large datasets. To enable DPC on large datasets, we propose efficient algorithms for DPC. Specifically, we propose an exact algorithm, Ex-DPC, and two approximation algorithms, Approx-DPC and S-Approx-DPC. Under a reasonable assumption about a DPC parameter, our algorithms are sub-quadratic, i.e., break the quadratic barrier. Besides, Approx-DPC does not require any additional parameters and can return the same cluster centers as those of Ex-DPC, rendering an accurate clustering result. S-Approx-DPC requires an approximation parameter but can speed up its computational efficiency. We further present that their efficiencies can be accelerated by leveraging multicore processing. We conduct extensive experiments using synthetic and real datasets, and our experimental results demonstrate that our algorithms are efficient, scalable, and accurate

Networks of innovation: measuring, modelling and enhancing innovation in surgery

The rate of innovation occurring in surgery is beyond our systemic capacity to quantify, with several methodological and practical challenges. The existing paucity of surgical innovation metrics presents a global healthcare problem especially as surgical innovations become increasingly costlier at a time when healthcare provision is experiencing a radical transformation driven by pressures to reduce costs, an ageing population with ever-increasing healthcare needs and patients with growing expectations. This thesis aims to devise a novel, quantitative, network-based framework that will permit modelling and measuring surgical innovation to add the most value to patient care. It involves the systematic, graphical and analytical assessment of surgical innovation in a way that has never been done before. This is based on successful models previously applied in the industry with advanced analytical techniques derived from social science (network analysis). In doing so, it offers an exciting new perspective and opportunity for understanding how the innovation process originates and evolves in surgery and how it can be measured in terms of value and virality, a priority for the NHS, RCS, Imperial and the wider surgical community. The ability to measure value and rank innovations is expected to play a fundamental role in guiding policy, strategically direct surgical research funding, and uncover innovation barriers and catalysts. This will ensure participation in the forefront of novel surgical technology and lay the scientific foundations for the development of improved healthcare models and services to enhance the quality of healthcare delivered.Open Acces

Analysis of the uptake of small and medium scale wind turbines under the Feed-in Tariff in Great Britain

As part of the UK’s energy system transition to a low-carbon electricity supply, decentralised energy sources such as small and medium scale wind turbines have become increasingly relevant. Decentralised energy generation has a central role in a proposed societal pathway to deliver a low-carbon energy system transition. Given the vast onshore wind energy potential of Great Britain, small and medium scale wind turbines will be a key part in this transition. With the introduction of the Feed-in Tariff (FIT) in April 2010, small and medium scale wind turbine deployment was expected to increase towards the technical potential of the technology, estimated to be up to 400,000 turbines. However, only 6,000 wind turbines have been installed in Great Britain since April 2010, highlighting there is still significant potential for small and medium scale wind turbine deployment. To fulfil this potential, an understanding of the influencing factors on previous wind turbine adoptions is required. A key part of this analysis is an investigation of the wind resource assessment methodology prescribed in the FIT policy. The Microgeneration Certification Scheme (MCS) is designed to offer a low-cost and quick scoping tool for prospective wind turbine installations. Analysis carried out in this work shows that long-term mean near surface wind speed predictions from the MCS method have a mean percentage error of 2.36 %. Over the same sample of 124 sites across Great Britain, a Boundary Layer Scaling (BLS) method, developed in this work, using UK wind map data offered wind speed predictions with a mean percentage error of 1.43 %. While these errors appear small, they equate, in the most extreme cases, to a difference of over £500 in annual FIT payments for a single wind turbine. While the MCS method is mandated in the FIT accreditation process, there is a risk that the potential financial returns of an installation can be severely miscalculated. Using the more accurate wind speed predictions available from the BLS model, it is possible to understand the influence of available wind resource on wind turbine adoption patterns. Throughout this work, wind turbine adoptions in Great Britain from 1995 until 2015 at both local authority and statistical geography resolution were analysed. Using a regression model, it is shown that wind resource explains up to 34 % of the spatial variance in adoption patterns. A threshold wind speed of 4.5 ms−1, above which wind turbine deployment is likely, was found in the current adoption patterns. These results highlight that while wind resource is an important factor, it is not the sole factor which influences wind turbine deployment in Great Britain. Previous literature has identified a number of socio-economic factors that have influenced adopters of other microgeneration technologies. Using a regression model and additional variables, such as land availability and agricultural statistics, it is possible to understand the influence of these socio-economic factors on wind turbine adoption patterns. The Socio-Economic and Resource (SER) model developed in this work highlights that wind turbine adoptions are more likely to occur in rural areas where wind resource, availability of land and prevalence of agriculture are high. Wind adopters are more likely to be older, hold degree-level qualifications and live in a detached home. This regression model however, only accounts for up to 65 % of the spatial variance in adoption patterns. This is an improvement over using only the resource model, however, there are still additional factors which influence wind turbine adoption patterns. The additional factors examined in this research were the influence from changes to the subsidy level of the FIT and the potential visibility of neighbouring turbines on adoption patterns. The visibility of neighbouring microgeneration installations has been cited as a factor which raises awareness in adopters, a factor particularly prevalent to wind turbines, which are highly visible to close neighbours. The influence of these factors was examined using a peer effects model in areas of high installations. The model shows that reductions in the FIT subsidy level have severely affected deployment. A peer effect from visible neighbouring turbines can be seen in these clusters of installations, however, it is secondary to the level of FIT subsidy available. In some clusters, evidence for a slow diffusion of wind turbines between peers was observed. Overall, the model indicates that the subsidy level available from the FIT was more influential than the visual peer effects. However, it is anticipated that this peer effect, will increase as deployment increases. In conclusion, the research has found that adoptions of wind turbines in Great Britain are influenced by a number of factors, namely available wind resource, rurality of turbine location, income of individual adopters and the subsidy level available for energy generation. These findings indicate that the small and medium scale wind turbine market in Great Britain is approaching a critical stage in its adoption lifecycle. Additionally, the results were used to develop a number of potential deployment estimates to understand where future growth in the market may occur. To meet these potential deployment estimates, there needs to be higher levels of deployment in order to help reduce capital costs. To achieve this future deployment, the levels of subsidy available from the FIT need to be maintained, in addition to the introduction of a BLS methodology in the FIT policy to facilitate more accurate financial assessments. A reduction in capital costs and maintaining of FIT subsidies will increase the number of sites which are financially viable for wind turbine installation. Potential new sites must still have a sufficient long-term mean wind resource of 4.5 ms−1 or above to be economically viable, highlighting the need for the introduction of the more accurate BLS methodology. If these conditions occur, deployment of small and medium scale wind turbines can increase towards the technical potential and play a central role in the transition to a low-carbon electricity market in Great Britain

Examining the policy diffusion of organic food and agriculture legislation in the U.S. - the role of the states in developing organic standards

2014 Spring.From 1976-2010, 38 states created and passed legislation regarding the regulation of organic food and agriculture. Most legislation was passed during the time period of 1985-1990, a period that ended with Congress passing the Organic Food Production Act [OFPA] in 1990. OFPA was passed to eliminate the patchwork of state and private third-party organic standards regulating the market and to maintain access to international markets by assuring U.S. standards were harmonized with key markets. Subsequently, it may have been expected that state adoption of organic policies would cease after federal action in 1990. However, many states continued to adopt and modify existing policies after the passage of OFPA. This research examines the diffusion of organic food and agriculture legislation and dynamics of legislative refinement in the United States both prior to and after federal adoption of organic legislation. With both theoretical and applied implications to be derived, this research uses the policy diffusion literature to examine the diffusion of organic legislation. A mixed-methods approach is utilized to answer the central research question of why do some states adopt organic food and agriculture legislation while others do not? The quantitative portion of this research uses time-series logistical regressions to test an enhanced unified model of policy diffusion. Time controls were used to evaluate the nationwide dynamics across several time periods. In addition, regional models were constructed for four statistically significant regions to further examine regional variations in diffusion factors. The qualitative portion of this research consists of a comparative case study between a leader and laggard state adopters. California and Georgia were the state cases selected for analysis. The results of this analysis suggest that wealth, political culture, partisan control of state government, state vegetable production, third-party certification organizations, horizontal pressures, national-scale pressures, and salience are key explanatory factors for state adoption of organic food and agriculture legislature from 1976-2010. Per capita wealth, issue salience, and regional effects are the most robust explanatory power over the 35-year time period and for each adoption-type. Pre-1990 state adoptions were also strongly influenced by the presence of third-party certifiers and the policy type design. Post-1990 state adoptions were additionally influenced by federal adoption and implementation, partisan control of state government, and state vegetable production. Action at the federal level, including federal adoption and implementation, did not dramatically deter state adoption or cause the repeal of state organic food and agriculture statutes. Across all time periods, certain regions remain distinctive in terms of diffusion dynamics including the Far West, North Central, Southeast, and Mid-Atlantic regions. Two case studies, California and Georgia, shed some light on how adoption of organic food and agriculture legislation occurred in the Far West and Southeast regions

Assessment of Policy Alternatives for Mitigation of Barriers to EV Adoption

Electric Vehicle (EV) has become an increasingly important topic in recent years due to energy and environmental concerns. Governments started to focus on remedies to the upcoming climate change threat and seek solutions through policies and regulations. The negative impact of carbon emissions along with pressure from governmental and social organizations force automotive manufacturers to shift to alternative energy sources. However, EV transition is a complex problem because its stakeholders are very diverse including governments, policy makers, EV manufacturers, and Non-Government Organizations (NGOs). Consequently, the barriers to EV adoption are not only consumer oriented, rather exist under many categories. The literature has yet to offer a comprehensive, quantified list of barriers to EV adoption. Although the enacted policies are known, the effectiveness of these policies in mitigating EV adoption is not known. The objective of this research is to assess policy alternatives for mitigation of EV adoption barriers by developing a comprehensive evaluation model. Barriers are grouped under Social, Technical, Environmental, Economic and Political (STEEP) perspectives that are perceived by decision makers as important for adoption process. The decision model of research links the perspectives to barriers, and policy alternatives. The research implements the hierarchical decision model (HDM) to construct a generalized policy assessment framework. Data for EV adoption barriers were collected from the abovementioned stakeholders. Experts\u27 qualitative judgments were collected and quantified using the pair-wise comparison method. The final rankings and effectiveness of policy alternatives were calculated. This research\u27s results showed that the most important perspective is Economic. The top three most important barriers to EV adoption were identified as Initial Cost, Battery Cost, and Entrenched Technology Resistance, respectively. The most effective policy in mitigating EV adoption barriers is R&D Incentives. The research also extended the policy effectiveness research with Policy Effectiveness Curves by reaching out to additional experts. These curves helped determine the effectiveness of each of the 6 policies at different implementation levels. Based on these results, 25 scenarios were applied by combinations of policies at different implementation levels to investigate how the effectiveness of policies can change compared to today\u27s conditions

Propagation in Networks : the impact of information processing at the actor level on system-wide propagation dynamics

Often systems can exhibit behavior which is diffcult to predict and steer. Interactions on the micro level (between actors within the system) result in propagation of behavior which can cause unforeseen dynamics on the system level. Understanding the effects of propagation, the process by which connected actors in uence each other, therefore is crucial in order to understand how the state and behavior of a system will change. Propagation literature has primary considered the way in which propagation dynamics scale from the local to the system-level, identifying the network structure as prime driver in this process. By focusing on the network structure, the impact of the mechanism by which propagation takes place has however been pushed to the background. In this dissertation it is argued that it is this mechanism which plays a crucial role in determining how propagation dynamics scale from the local to the system-level. To map the mechanism of propagation, this dissertation puts forward a framework for propagation as an information processing process. It describes the propagation mechanism using the distinct sub-processes; sending out information (Radiation), transferring information (Transmission) and processing information (Reception). This dissertation shows that using such a framework not only results in a more detailed and methodologically stronger model of propagation, but also that distinguishing these sub-processes is a prerequisite for effective interventions into propagation. It also shows that heterogeneity in different part of the mechanism have radically different effects on the dynamics at the system-level. This implies that specifying the mechanism is critical for understanding the system-level dynamics in cases of heterogeneous actor behavior. Finally, it shows that the effects of network structure are highly conditional on the mechanism of propagation. When more complex propagation mechanisms are compared, a single network structure can result in very different dynamics at the system level. As such, this dissertation identifies the mechanism of propagation as a critical component in understanding how micro-level behavior scales toward the system-level, and hence impacts system-wide dynamic

Essays In Industrial Organization And Applied Microeconomics

This dissertation consists of three essays in the areas of Industrial Organization and Applied Microeconomics. The first essay studies high-tech firms\u27 product portfolio choices under competition. I develop a model of dynamic portfolio adjustments in the context of the Chinese smartphone market, using the product life cycle as an empirically tractable heuristic to capture firms\u27 dynamic incentives in new product introductions. I first show that product life cycles endogenously arise in markets with rapid technological innovations, are heterogeneous across products, and are affected by the level of market competition. I then estimate smartphone demand and manufacturers\u27 variable, maintenance and sunk introduction costs on a detailed monthly market-level dataset of Chinese smartphones between 2009 and 2014. Finally, I use a 2012 large-scale pro-competitive policy introduced by the Chinese government as an experiment to decompose the handset manufacturers\u27 incentives to introduce new products and show that the increased competition reduces the average product\u27s short-run profits by 5% but its lifetime profits by 41% by shrinking its product life cycle. These dynamic incentives have large implications for both consumer welfare gains from product variety and the speed of technology adoption in this market. In the second essay, my co-authors and I explore the sensitivity of the U.S. government\u27s ongoing incentive auction to multi-license ownership by broadcasters. We document significant broadcast TV license purchases by private equity firms prior to the auction and perform a prospective analysis of the effect of ownership concentration on auction outcomes. We find that multi-license holders are able to raise spectrum acquisition costs by 22% by strategically withholding some of their licenses to increase the price for their remaining licenses. We analyze a potential rule change that reduces the distortion in payouts to license holders by up to 80%, but find that lower participation could greatly increase payouts and exacerbate strategic effects. The third essay studies whether liberalizations of gun permits in the U.S. deterred violent crimes. Setting off an ongoing controversy, Lott and Mustard (1997) famously contended that so-called shall-issue laws (SILs) deterred violent crime. In this controversy the weapon of choice has been the differences-in-differences (DD) estimator applied to state and county panel data spanning various intervals of time. By treating violent crime as a career choice, this essay brings to bear a more general method, a cohort panel data model (CPDM) that incorporates the fundamental dynamic insights regarding entering and exiting a career. Our model distinguishes among three key parameters that jointly determine the effect of SILs on crime, (i) a direct effect on entry decisions, (ii) a surprise effect on exit decisions by individuals who entered criminal careers prior to the passage of SILs, and (iii) a selection effect on exit decisions by those who entered in the presence of SILs. We find significant and time-invariant results that reject the deterrence hypothesis as well as the DD model specification. Our results suggest that passages of SILs contribute to about one third of total violent crimes in 2011, particularly through higher turnover of violent criminals

Washington County Notice of Adopted Amendment (2007-08-09)

51 pp. Adopted 2007-08-09. Department of Land Conservation and Development Notice of Adopted Amendment(limit of 500 characters): A-Engrossed Ordinance No. 683 amended the Comprehensive Framework Plan for the Urban Area, the 2020 Transportation Plan, the Aloha-Reedville-Cooper Mountain Community Plan, the Raleigh Hills-Garden Home Community Plan, and the Community Development Code relating to housekeeping and general updates

Washington County Notice of Adopted Amendment (2007-08-09)

51 pp. Adopted 2007-08-09. Department of Land Conservation and Development Notice of Adopted Amendment(limit of 500 characters): A-Engrossed Ordinance No. 683 amended the Comprehensive Framework Plan for the Urban Area, the 2020 Transportation Plan, the Aloha-Reedville-Cooper Mountain Community Plan, the Raleigh Hills-Garden Home Community Plan, and the Community Development Code relating to housekeeping and general updates