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In 2019, Shinzo Abe, then Prime Minister of Japan, stated that if the world wanted to achieve the benefits of the data-driven economy, members of the World Trade Organization should find a common approach to combining 'data free flow with trust'. However, he never explained what these rules should look like and how nations might find an internationally accepted approach to such rules. In this paper, I argue that trade policy-makers must pay closer attention to users' concerns if they truly want to achieve 'data free flow with trust'. I begin with an examination of what the most recent digital trade/ecommerce agreements say about trust and discuss whether they actually meet user concerns. Next, I turn to three different examples of online problems that users have expressed concerns about, namely internet shutdowns/censorship, disinformation, and ransomware, describing how these may yield both trade distortions and less trust online. I argue that policy-makers should address these issues if they believe trade agreements should build trust in cross-border data flows. Moreover, I argue how policy-makers respond to user concerns is as important as what they include in trade agreements. Finally, I note that trade negotiators will need to rethink how they involve the broad public in digital trade policy-making, while recognizing that trade policy agreements may not be the best place to address these problems. [ABSTRACT FROM AUTHOR]

The authors share their view on the importance of cultural change in data publishing. They believe that a data paper is a proper mechanism to boost the publication of biodiversity data. They argued against the views expressed by X. Huang and colleagues on the use of data paper to publish biodiversity data. Several reasons on why the authors prefer the publication of a data paper including the mobilization of data and making available a data set along with other details were noted.

For all health conditions, reliable age-disaggregated data are vital for both epidemiological analysis and monitoring the relative prioritization of different age groups in policy responses. This is especially essential in the case of Coronavirus Disease-2019 (COVID-19), given the strong association between age and case fatality. This paper assesses the availability and quality of age-based data on reported COVID-19 cases and deaths for low- and middle-income countries. It finds that the availability of reliable data which permit specific analyses of older people is largely absent. The paper explores the potential of excess mortality estimates as an alternative metric of the pandemic's effects on older populations. Notwithstanding some technical challenges, this may offer a better approach, especially in countries where cause of death data are unreliable. [ABSTRACT FROM AUTHOR]

The Coronavirus disease pandemic has disrupted survey systems globally and especially in low- and middle-income countries. Phone surveys started being implemented at a national scale in many places that previously had limited experience with them. As in-person data collection resumes, the experience gained provides the grounds to reflect on how phone surveys may be incorporated into survey and data systems in low- and middle-income countries. Reviewing evidence and experiences from before and during the pandemic, the paper provides guidance on the scope of and considerations for using phone surveys for agricultural data collection and the integration of phone interviews with in-person data. [ABSTRACT FROM AUTHOR]

In this article we perform an impact evaluation of a programme that provides ex post subsidies to researchers in Paraguay. The analysis spans across the first 2 years following the programme (short-run). Ex post subsidies are prevalent in Latin America; however, the analysis of their effects has received little attention in the literature. Thanks to the availability of data coming from electronic CVs of applicants, we are able to analyse the impact of the programme through dimensions of researchers' productivity that have been mostly overlooked previously. For example, we are able to use technical production, own education, other researchers' training, and other dimensions of the bibliographic production that are different to published articles. We also provide impact estimations on quantity and quality of publications based on more traditional sources of data. We find some positive impacts of the programme. However, some of the results are not robust to alternative methods of estimation. [ABSTRACT FROM AUTHOR]

The article explores on the simple procedure for processing event recorder data. The strip chart device called event recorder reads the state of an electrical switch with reference to time used in various studies is said to require a long to process the gathered data. According to the author, the usual procedure uses scissors to cut the strip charts into segments representing the desired time base which is tedious. He suggests the use of a stapler and paper cutter as an alternative for collecting the gathered data.

The data science technologies of artificial intelligence (AI), Internet of Things (IoT), big data and behavioral/predictive analytics, and blockchain are poised to revolutionize government and create a new generation of GovTech start-ups. The impact from the 'smartification' of public services and the national infrastructure will be much more significant in comparison to any other sector given government's function and importance to every institution and individual. Potential GovTech systems include Chatbots and intelligent assistants for public engagement, Robo-advisors to support civil servants, real-time management of the national infrastructure using IoT and blockchain, automated compliance/regulation, public records securely stored in blockchain distributed ledgers, online judicial and dispute resolution systems, and laws/statutes encoded as blockchain smart contracts. Government is potentially the major 'client' and also 'public champion' for these new data technologies. This review paper uses our simple taxonomy of government services to provide an overview of data science automation being deployed by governments world-wide. The goal of this review paper is to encourage the Computer Science community to engage with government to develop these new systems to transform public services and support the work of civil servants. [ABSTRACT FROM AUTHOR]

This paper investigates undesirable limitations of widely used count data instrumental variable models. To overcome the limitations, I propose a partially identifying single-equation model that requires neither strong separability of unobserved heterogeneity nor a triangular system. Sharp bounds (identified sets) of structural features are characterised by conditional moment inequalities. Numerical examples show that the size of an identified set can be very small when the support of an outcome is rich or instruments are strong. An algorithm for estimation and inference is presented. I illustrate the usefulness of the proposed model in an empirical application to effects of supplemental insurance on healthcare utilisation. [ABSTRACT FROM AUTHOR]

This paper studies the impact of the confirmatory bias on financial markets. We propose a model in which some traders may ignore new evidence inconsistent with their favorite hypothesis regarding the state of the world. The confirmatory bias provides a unified rationale for several existing stylized facts, including excess volatility, excess volume, and momentum. It also delivers novel predictions for which we find empirical support using data on analysts’ earnings forecasts: traders update beliefs depending on the sign of past signals and previous beliefs, and, at the stock level, differences of opinion are larger when past signals have different signs. [ABSTRACT FROM AUTHOR]

Data wrangling (DW) is the subject of growing interest given its potential to improve data quality. DW applies interactive and iterative data profiling, cleaning, transformation, integration and visualization operations to improve the quality of data. Several domain-independent DW tools have been developed to tackle data quality issues across domains. Using generic DW tools requires a time-consuming and costly DW process, often involving advanced IT knowledge beyond the skills set of traffic analysts. In this paper, we propose a conceptual approach to DW for traffic data by creating a domain-specific language for specifying traffic DW tasks and an abstract set of wrangling operators that serve as the target conceptual construct for mapping domain-specific wrangling tasks. The conceptual approach discussed in this paper is tool-independent and platform agnostic and can be mapped into specific implementations of DW functions available in existing scripting languages and tools such as R, Python, Trifacta. Our aim is to enable a typical traffic analyst without expert Data Science knowledge to be able to perform basic DW tasks relevant to his domain. [ABSTRACT FROM AUTHOR]

A revocable ID-based broadcast encryption scheme allows an authorized third party to revoke any receiver (decryptor) from the initial receiver set S of the original broadcast ciphertext without the need of decryption. However, the existing revocable ID-based broadcast encryption schemes in the literature cannot fully preserve the receiver privacy and have a large size of ciphertext when the revoked user sets are large. To solve these problems, in this paper, we propose a novel scheme: fully privacy-preserving ID-based broadcast encryption with authorization. Our scheme allows an authorized party to dynamically handle the decryption rights of receivers via an authorized user set L without knowing the message and the identities of the initial receivers. Only those users who are both in S and L can decrypt the ciphertext successfully. The final ciphertext reveals nothing about the identity information of receivers and the authorized users. Our scheme achieves full collusion resistance and is applicable to anonymous data sharing where the receivers are decided by the authorized third party (or multiple authorized third parties) excluding the data owner. We show that our proposed scheme is provably secure under the defined security models in the random oracle model. [ABSTRACT FROM AUTHOR]

This paper proposes stream model execution (SME), a new architectural technique that supports the efficient utilization of single-instruction multiple-data (SIMD) engines on general-purpose processors. SME adopts lightweight streaming processing as the intermediate representation. This process uses a hardware–software co-design approach to support efficient SIMD compilation and utilization. From a hardware perspective, the micro-architecture of SIMD engines is enabled for stream support to provide the key components to address the limitations of SIMD utilization, such as data rearrangement and non-consecutive memory access. Thus, sequential code can be compiled efficiently through a two-step procedure (i.e. C code → SME C code → vectorized code) and then executed by the SME hardware. Our experimental evaluation shows that SME can outperform previous optimization techniques by an average of 58% for multimedia kernels and 26% for applications. [ABSTRACT FROM AUTHOR]

An introduction to a special issue of the journal is presented that emphasizes the use of non-traditional data in the fields of social and behavioral research.

The paper investigates a change point estimation problem in the context of high dimensional Markov random-field models. Change points represent a key feature in many dynamically evolving network structures. The change point estimate is obtained by maximizing a profile penalized pseudolikelihood function under a sparsity assumption. We also derive a tight bound for the estimate, up to a logarithmic factor, even in settings where the number of possible edges in the network far exceeds the sample size. The performance of the estimator proposed is evaluated on synthetic data sets and is also used to explore voting patterns in the US Senate in the 1979-2012 period. [ABSTRACT FROM AUTHOR]

Accurate prognostic prediction using molecular information is a challenging area of research, which is essential to develop precision medicine. In this paper, we develop translational models to identify major actionable proteins that are associated with clinical outcomes, like the survival time of patients. There are considerable statistical and computational challenges due to the large dimension of the problems. Furthermore, data are available for different tumor types; hence data integration for various tumors is desirable. Having censored survival outcomes escalates one more level of complexity in the inferential procedure. We develop Bayesian hierarchical survival models, which accommodate all the challenges mentioned here. We use the hierarchical Bayesian accelerated failure time model for survival regression. Furthermore, we assume sparse horseshoe prior distribution for the regression coefficients to identify the major proteomic drivers. We borrow strength across tumor groups by introducing a correlation structure among the prior distributions. The proposed methods have been used to analyze data from the recently curated "The Cancer Proteome Atlas" (TCPA), which contains reverse‐phase protein arrays–based high‐quality protein expression data as well as detailed clinical annotation, including survival times. Our simulation and the TCPA data analysis illustrate the efficacy of the proposed integrative model, which links different tumors with the correlated prior structures. [ABSTRACT FROM AUTHOR]

A ternary search trie (TST) is a highly efficient dynamic dictionary structure applicable for strings and textual data. The strings are accessed based on a set of access probabilities and are to be arranged using a TST. We consider the scenario where the probabilities are not known a priori and is time-invariant. Our aim is to adaptively restructure the TST so as to yield the best access or retrieval time. Unlike the case of lists and binary search trees where numerous methods have been proposed, in the case of the TST, currently, the number of reported adaptive schemes are few. In this paper we consider various self-organizing schemes that were applied to binary search trees and apply them to TSTs. Three new schemes, which are the splaying, the conditional rotation and the randomization heuristics, have been proposed, tested and comparatively presented. The results demonstrate that the conditional rotation heuristic is the best when compared with other heuristics that are considered in the paper. [ABSTRACT FROM PUBLISHER]

Background care homes collect extensive data about their residents, and their care, in multiple ways, for multiple purposes. We aimed to (i) identify what data are routinely collected and (ii) collate care home managers' views and experiences of collecting, using and sharing data. Methods we examined data collected in six care homes across Lothian, Scotland. We extracted the meta-data, cross-referenced definitions and assessed the degree of harmonisation between care homes and with data sets currently in use in Scotland and internationally. We interviewed care home managers about their views and experiences of collecting, using and sharing data. Results we identified 15 core data items used routinely, with significant heterogeneity in tools and assessments used, and very limited harmonisation. Two overarching themes were identified of importance to the development of a care home data platform: (i) the rationale for collecting data, including to (a) support person-centred care, (b) share information, (c) manage workforce and budget and (d) provide evidence to statutory bodies and (ii) the reality of collecting data, including data accuracy, and understanding data in context. Discussion considerable information is collected by care home staff, in varied formats, with heterogeneity of scope and definition, for range of reasons. We discuss the issues that should be considered to ensure that individual resident-level form the strong foundations for any data platform for care homes, which must also include, robust infrastructure and clear interoperability, with appropriate governance. It must be co-produced by academics, policy makers and sector representatives, with residents, their families and care staff. [ABSTRACT FROM AUTHOR]

Marine biodiversity monitoring is a complex task given the vast ocean areas to be covered, the types of data to be integrated, and the large number of possible parameters to consider when measuring biodiversity change. International agreements, such as the Convention on Biological Diversity (CBD) or the recently adopted 'BBNJ treaty'protecting biodiversity beyond national jurisdiction (BBNJ), encourage states to monitor marine life both within and beyond their national waters to foster marine biodiversity conservation and sustainable use. While oceanographers in general, and marine biologists in particular, are aware of the challenges and complexities of measuring biodiversity in the ocean, the politics of marine biodiversity monitoring and the added value of social science perspectives tend to be neglected. This story from the frontline describes our experiences with turning marine biodiversity monitoring into an object of social science inquiry. The MARIPOLDATA project developed an innovative multi-sited approach to capture both the politics and practices of monitoring marine life. During our research, we were confronted with a diversity of perspectives and expectations of what monitoring is, how it works, why it should be done, and whom it should serve. By adopting a mixed-method approach and collecting data at different epistemic sites (negotiation rooms, scientific fields, laboratories, and conferences), we tried to cut through some of the observed ambiguities and trends. We conclude that studying monitoring necessitates a multidisciplinary approach and a sensitivity for the regional, institutional, and cultural specificities and inequalities that shape how we know and govern the ocean. [ABSTRACT FROM AUTHOR]

This paper deals with the detection and identification of changepoints among covariances of high-dimensional longitudinal data, where the number of features is greater than both the sample size and the number of repeated measurements. The proposed methods are applicable under general temporal-spatial dependence. A new test statistic is introduced for changepoint detection, and its asymptotic distribution is established. If a changepoint is detected, an estimate of the location is provided. The rate of convergence of the estimator is shown to depend on the data dimension, sample size, and signal-to-noise ratio. Binary segmentation is used to estimate the locations of possibly multiple changepoints, and the corresponding estimator is shown to be consistent under mild conditions. Simulation studies provide the empirical size and power of the proposed test and the accuracy of the changepoint estimator. An application to a time-course microarray dataset identifies gene sets with significant gene interaction changes over time. [ABSTRACT FROM AUTHOR]

Motivation Integration of data from different modalities is a necessary step for multi-scale data analysis in many fields, including biomedical research and systems biology. Directed graphical models offer an attractive tool for this problem because they can represent both the complex, multivariate probability distributions and the causal pathways influencing the system. Graphical models learned from biomedical data can be used for classification, biomarker selection and functional analysis, while revealing the underlying network structure and thus allowing for arbitrary likelihood queries over the data. Results In this paper, we present and test new methods for finding directed graphs over mixed data types (continuous and discrete variables). We used this new algorithm, CausalMGM, to identify variables directly linked to disease diagnosis and progression in various multi-modal datasets, including clinical datasets from chronic obstructive pulmonary disease (COPD). COPD is the third leading cause of death and a major cause of disability and thus determining the factors that cause longitudinal lung function decline is very important. Applied on a COPD dataset, mixed graphical models were able to confirm and extend previously described causal effects and provide new insights on the factors that potentially affect the longitudinal lung function decline of COPD patients. Availability and implementation The CausalMGM package is available on http://www.causalmgm.org. Supplementary information Supplementary data are available at Bioinformatics online. [ABSTRACT FROM AUTHOR]

Following a shift toward greater transparency, many academic journals across a variety of disciplines now require authors to post their data. At the same time, many university Institutional Review Boards (IRBs) have followed recent US federal guidelines and now require researchers to be more transparent with survey participants regarding what will happen to the collected data. In this paper, we take the first steps toward considering the interaction between these two survey research developments. Using a nationally representative panel, we show that informing survey participants that their de-identified data will be publicly shared by a researcher can affect how these participants answer certain questions. In some cases, public posting notifications can increase data quality (e.g. knowledge measures), but in other cases informing participants of the data's future use can exacerbate social desirability issues (e.g. turnout). Our results suggest conditional costs and benefits to the intersection between two critical ethical norms underlying survey research: data-sharing and informed consent. [ABSTRACT FROM AUTHOR]

Keyword search (KWS) has been known as an attractive query processor in retrieving information from various types of data which could be modeled as a graph. An answer in response to a keyword query is a set of cohesively connected structures which shows how the data containing query keywords are interconnected in the graph. Finding answers to a given query efficiently and ranking the retrieved answers in an effective way are still two challenging problems in KWS domain. In this paper, we first propose a novel scoring function to optimize the accuracy of ranking the answers. This function is defined based on a carefully designed model called SARM which is an integrated model of the content and structure of an answer. We then develop a two-level KWS approach to support the efficient retrieval of top-k answers to a given query. This approach is based on pruning the search space to concentrate the search on the promising regions. The efficiency of this approach is improved by estimating the boundary scores of the answers in the regions. Extensive experiments conducted on a standard evaluation framework with three real-world datasets confirm the efficiency and effectiveness of the proposed approach. [ABSTRACT FROM AUTHOR]

The global food and agricultural industry has a total market value of USD 8 trillion in 2016, and decision makers in the Agri sector require appropriate tools and up-to-date information to make predictions across a range of products and areas. Traditionally, these requirements are met with information processed into a data warehouse and data marts constructed for analyses. Increasingly however, data are coming from outside the enterprise and often in unprocessed forms. As these sources are outside the control of companies, they are prone to change and new sources may appear. In these cases, the process of accommodating these sources can be costly and very time consuming. To automate this process, what is required is a sufficiently robust extract–transform–load process; external sources are mapped to some form of ontology, and an integration process to merge the specific data sources. In this paper, we present an approach to automating the integration of data sources in an Agri environment, where new sources are examined before an attempt to merge them with existing data marts. Our validation uses a case study of real world Agri data to demonstrate the robustness of our approach and the efficiency of materializing data marts. [ABSTRACT FROM AUTHOR]

Data centers, receiving increased attention in data management and analysis communities, have posed new challenges in data-intensive applications, among which efficient querying processing holds a critical position. To accelerate the efficiency of multi-dimensional data retrieval, we propose a distributed multi-dimensional indexing scheme for switch-centric data centers in this paper. We first propose FR-Index, a two-layer indexing system integrating both Fat-tree topology and R-tree indexing structure. In the lower layer, each server indexes the local data with R-tree, while in the upper layer the distributed global index depicting an overview of the whole dataset. Based on the Fat-tree topology, we design a specific indexing space partitioning and mapping strategy for efficient global index maintenance and query processing. Furthermore, we develop a cost model to dynamically update FR-Index. Experiments on Amazon's EC2 platform, comparing FR-Index with RT-CAN and RB-Index, show that the proposed indexing schema is scalable, efficient and lightweight, which can significantly promote the efficiency of query processing in data centers. [ABSTRACT FROM AUTHOR]

Antiretroviral therapy (ART) programs in sub-Saharan Africa are moving towards universal treatment, yet the success of these programs is threatened by gaps within the continuum of HIV care. In this commentary, we seek to draw attention to the advantages and limitations of different data sources currently used for measuring retention in HIV care. Within the context of a piecemeal combination of imperfect data sources, selecting a gold standard of collecting retention data is difficult. Currently, our data systems for measuring retention provide an incomplete picture of the health of our national ART systems and, thus, the health of our patients. [ABSTRACT FROM AUTHOR]

The optimization of an information criterion in a variable selection procedure leads to an additional bias, which can be substantial for sparse, high-dimensional data. One can compensate for the bias by applying shrinkage while estimating within the selected models. This paper presents modified information criteria for use in variable selection and estimation without shrinkage. The analysis motivating the modified criteria follows two routes. The first, which we explore for signal-plus-noise observations only, proceeds by comparing estimators with and without shrinkage. The second, discussed for general regression models, describes the optimization or selection bias as a double-sided effect, which we call a mirror effect: among the numerous insignificant variables, those with large, noisy values appear more valuable than an arbitrary variable, while in fact they carry more noise than an arbitrary variable. The mirror effect is investigated for Akaike’s information criterion and for Mallows’ Cp, with special attention paid to the latter criterion as a stopping rule in a least-angle regression routine. The result is a new stopping rule, which focuses not on the quality of a lasso shrinkage selection but on the least-squares estimator without shrinkage within the same selection. [ABSTRACT FROM AUTHOR]

The paper uses accounting methods to decompose aggregate labour productivity and employment growth into their sectoral components as well as into within-sector and employment reallocation effects for a sample of 81 developed and developing countries using data going back to the mid-1980s. Key findings are that aggregate labour productivity growth for developing countries taken together is driven as much by services as by industry, in spite of strong differences between countries, and that within-sector effects on aggregate labour productivity growth are more important than employment reallocation effects, a pattern that holds for all regions. [ABSTRACT FROM PUBLISHER]

Motivation: Batch effects are one of the major source of technical variations that affect the measurements in high-throughput studies such as RNA sequencing. It has been well established that batch effects can be caused by different experimental platforms, laboratory conditions, different sources of samples and personnel differences. These differences can confound the outcomes of interest and lead to spurious results. A critical input for batch correction algorithms is the knowledge of batch factors, which in many cases are unknown or inaccurate. Hence, the primary motivation of our paper is to detect hidden batch factors that can be used in standard techniques to accurately capture the relationship between gene expression and other modeled variables of interest. Results: We introduce a new algorithm based on data-adaptive shrinkage and semi-Non-negative Matrix Factorization for the detection of unknown batch effects. We test our algorithm on three different datasets: (i) Sequencing Quality Control, (ii) Topotecan RNA-Seq and (iii) Single-cell RNA sequencing (scRNA-Seq) on Glioblastoma Multiforme. We have demonstrated a superior performance in identifying hidden batch effects as compared to existing algorithms for batch detection in all three datasets. In the Topotecan study, we were able to identify a new batch factor that has been missed by the original study, leading to under-representation of differentially expressed genes. For scRNA-Seq, we demonstrated the power of our method in detecting subtle batch effects. [ABSTRACT FROM AUTHOR]

Advanced LIGO (Laser Interferometer Gravitational Wave Observatory) observations achieved the first detection of the gravitational wave, which was from a merging binary black hole (BBH). In the near future, more merger events will be observed, and the mass distribution of them will become available. The mass distribution of merger events reflects the evolutionary path of BBHs: dynamical formation in dense star clusters or common envelope evolution from primordial binaries. In this paper, we estimate the detection rate of merging BBHs which dynamically formed in dense star clusters by combining the results of N-body simulations, modeling of globular clusters, and cosmic star-cluster formation history. We estimate that the merger rate density in the local universe within the redshift of 0.1 is 13-57 Gpc-3 yr-1. We find that the detection rate is 0.23-4.6 per year for the current sensitivity limit and that it would increase to 5.1-99 per year for the designed sensitivity which will be achieved in 2019. The distribution of merger rate density in the local universe as a function of redshifted chirp mass has a peak close to the low-mass end. The chirp mass function of the detected mergers, on the other hand, has a peak at the high-mass end, but is almost flat. This difference is simply because the detection range is larger for more massive BBHs. [ABSTRACT FROM AUTHOR]

. The paper considers the use of level-of-effort (LOE) paradata to model the non-response mechanism in surveys and to adjust for non-response bias, particularly bias that is missing not at random or non-ignorable. Our approach is based on an unconditional maximum likelihood estimation (call-back) model that adapts and extends the prior work to handle the complexities that are encountered for large-scale field surveys. A test of the 'missingness at random' assumption is also proposed that can be applied to essentially any survey when LOE data are available. The non-response adjustment and the test for missingness at random are then applied and evaluated for a large-scale field survey-the US National Survey on Drug Use and Health. Although evidence on non-ignorable non-response bias was found for this survey, the call-back model could not remove it. One likely explanation of this result is error in the LOE data. This possibility is explored and supported by a field investigation and simulation study informed by data obtained on LOE errors. [ABSTRACT FROM AUTHOR]

Cache sharing has been a popular technique used to facilitate data access in mobile network environments. The key to this technique is to allow an efficient sharing of cache contents between neighboring nodes without introducing excessive amount of communication overhead. In this paper, we propose a cache-sharing protocol, called ‘Pull with Piggybacked Push (PPP)’, which exploits data request broadcasts by performing data pull and index push operations together. Taking advantage of both push- and pull-based approaches, PPP gains benefits from both ends: performance and communication overhead. PPP is unique in that it yields good performance over diverse operating environments, while accomplishing it with lower communication overhead than the previous methods. In addition, it adapts well to different data access patterns. As a result, better scalability is achieved by the proposed protocol. [ABSTRACT FROM AUTHOR]

We study the impact of FinTech competition in payment services when a monopolist bank uses payment data to learn about consumers' credit quality. Competition from FinTech payment providers disrupts this information spillover. The bank's price for payment services and its loan offers are affected. FinTech competition promotes financial inclusion, may hurt consumers with a strong bank preference, and has an ambiguous effect on the loan market. Both FinTech data sales and consumer data portability increase bank lending, but the effects on consumer welfare are ambiguous. Under mild conditions, consumer welfare is higher under data sales than with data portability. [ABSTRACT FROM AUTHOR]

Disk arrays have been proposed as a way of improving I/O performance by using parallelism among multiple disks. This paper focuses, however, on improving the performance of disk array systems in the presence of disk failures, which are significant for applications where continuous operation is of concern. Although several approaches have been explored, the goals of achieving high performance and storage efficiency often conflict. In this paper, we propose a new variation of RAID organization, multi-partition RAID (mP-RAID), to improve storage efficiency and reduce performance degradation when disk failures occur. The idea is to recognize that frequently demanded data dominate the degree of the performance degradation when disk failures occur. mP-RAID subdivides a disk array into several partitions associated with different block organizations. Based upon data popularity, we assign data to appropriate partitions so that high performance and better storage efficiency can be achieved simultaneously. [ABSTRACT FROM AUTHOR]

Time and again, methodologists wrote papers with the potential to kill off dyadic designs in international relations research, only to pull back and--so long as one adopted the appropriate "tweak"--grant dyads a reprieve. Depending on the research question under consideration and the theoretical claims being evaluated, dyadic designs can provide valuable empirical insights. But these insights remain contingent on the researcher attempting to account for the limitations of dyadic data. To make this point, I concentrate on two difficulties for dyadic designs: the problem of interdependence and the problem of multilateral events. Drawing from empirical research on treaties and international organizations, I argue that a researcher need not abandon dyadic data so long as the researcher faces the former problem. [ABSTRACT FROM AUTHOR]

This paper grounds the critique of the ‘smart city’ in its historical and geographical context. Adapting Brenner and Theodore’s notion of ‘actually existing neoliberalism’, we suggest a greater attention be paid to the ‘actually existing smart city’, rather than the exceptional or paradigmatic smart cities of Songdo, Masdar and Living PlanIT Valley. Through a closer analysis of cases in Louisville and Philadelphia, we demonstrate the utility of understanding the material effects of these policies in actual cities around the world, with a particular focus on how and from where these policies have arisen, and how they have unevenly impacted the places that have adopted them. [ABSTRACT FROM AUTHOR]

Trachoma is a disease of the eye and the leading infectious cause of blindness worldwide. Years of repeated infections can cause in-turning of the lashes so that they rub against the eyeball, causing pain, discomfort and, if left untreated, blindness. This is known as trachomatous trichiasis (TT) and can be remedied by surgery. To improve oversight and reporting of TT outreach, Sightsavers developed a mobile phone application called the TT Tracker so that TT surgeons, assistants and supervisors can collect and analyse information about surgical outcomes and performance and determine when and where follow-up appointments are required. The TT Tracker is being used by seven national programmes. Examples of use and programme improvements from Nigeria, Benin and Senegal are discussed here. [ABSTRACT FROM AUTHOR]

The "blue economy" idea promotes the responsible and sustainable use of coastal and marine resources. The need to balance the exploitation and economic use of these resources has moved to the top of the research agenda and management priorities for small island developing states (SIDS). SIDS face unique challenges due to their geographic, economic, and environmental characteristics. While the specific challenges can vary based on the individual SIDS, some common future challenges include (i) climate change and sea-level rise resulting from the increased frequency and intensity of storms and coastal erosion; (ii) natural disasters such as hurricanes, cyclones, tsunamis, and earthquakes, resulting in extensive damage, disruption of essential services, and lead to loss of lives and livelihoods; (iii) lack of sustainable development and economic diversification leading to reliance on few key sectors, such as tourism and agriculture, which are vulnerable to market fluctuations; (iv) energy security and renewable energy transition; as there is dependency on imported fossil fuels for their energy needs, price fluctuations and supply disruptions occurs; (v) biodiversity conservation and marine resource management; they host unique ecosystems and rich biodiversity, including coral reefs, mangroves, and marine species; and (vi) access to finance and technology; there are direct opportunities, for accessing funds to support technology development under sustainable finance projects. This themed article set attracted 14 submissions, 7 of which were accepted for publication. The topics covered fisheries, climate change effects, marine litter, and development of new technologies and finance mechanisms to support the SIDS. Overall, these contributions have emphasized the remarkable role that SIDS play in the protection and management of coastal and marine ecosystems, but also highlighted several needs and key priorities. [ABSTRACT FROM AUTHOR]

In an essay, the author explores how the contours and limits of data have implications for the historical narratives that are constructed for various media forms. Particular focus is paid to the examples of DVDs, interactive documentaries, digitized collections of papers and archival records, and the Internet.

. The paper systematically reviews existing literature on the relationship between the level of effort to recruit a sampled person and the measurement quality of survey data. Hypotheses proposed for this relationship are reviewed. Empirical findings for the relationship between level of effort as measured by paradata (the number of follow-up attempts, refusal conversion and time in the field) and question-specific item non-response rates, aggregate measures of item non-response rates, response accuracy and various measurement errors on attitudinal questions are examined through a qualitative review. [ABSTRACT FROM AUTHOR]

Past research has shown that procedural justice enhances an authority's legitimacy and encourages people to cooperate with them. However, this past research has examined legitimacy by focusing solely on the perceived legitimacy of authorities and has ignored how people may perceive the legitimacy of the laws and rules authorities enforce. This distinction has relevance to the policing of ethnic minority groups who may come from different cultures or countries where distrust in the law and legal institutions is prevalent. Using survey data collected from a random sample of 1,203 Australians, this paper explores how procedural justice and both institutional and legal legitimacy impact on people's willingness to cooperate with police. The findings will be explained using Braithwaite's (2003; 2010) social distancing framework. [ABSTRACT FROM PUBLISHER]

In this paper, I study the impact of managers' affiliations with the Freemasonry on the performances of companies. Using a unique data set of 412 companies quoted on the London Stock Exchange between 1895 and 1902, I find that young and small firms run by Masonic managers exhibited larger leverage ratios. These companies earned higher profits, although the effect is not statistically significant. Large publicly quoted corporations managed by Freemasons instead had lower profits and lower Tobin's Q. I discuss the issue of the endogeneity of Freemasonry membership, and I use four different approaches to partially address this. [ABSTRACT FROM AUTHOR]

In this paper, a new approach is introduced to solve ill-posed linear inverse problems in geophysics. Our method combines classical quadratic regularization and data smoothing by imposing constraints on model and data smoothness simultaneously. When imposing a quadratic penalty term in the data space to control smoothness of the data predicted by classical zero-order regularization, the method leads to a direct regularization in standard form, which is simple to be implemented and ensures that the estimated model is smooth. In addition, by enforcing Tikhonov's predicted data to be sparse in a wavelet domain, the idea leads to an efficient regularization algorithm with two superior properties. First, the algorithm ensures the smoothness of the estimated model while substantially preserving the edges of it, so, it is well suited for recovering piecewise smooth/constant models. Second, parsimony of wavelets on the columns of the forward operator and existence of a fast wavelet transform algorithm provide an efficient sparse representation of the forward operator matrix. The reduced size of the forward operator makes the solution of large-scale problems straightforward, because during the inversion process, only sparse matrices need to be stored, which reduces the memory required. Additionally, all matrix–vector multiplications are carried out in sparse form, reducing CPU time. Applications on both synthetic and real 1-D seismic-velocity estimation experiments illustrate the idea. The performance of the method is compared with that of classical quadratic regularization, total-variation regularization and a two-step, wavelet-based, inversion method. [ABSTRACT FROM AUTHOR]

This paper presents a quantitative analysis of data collected from the mode of trial hearing in two English magistrates' courts. A model of the mode of trial procedure is offered that explores the factors that influence the mode of trial decision taken by magistrates. While legal factors such as seriousness of the offence play a part in the process, the mode of trial decision is also shaped by factors such as courtroom culture, the provision of bail and ethnicity. While this study is, in many respects, exploratory, it does point towards the importance of these extra legal factors in the mode of trial decision and indicate future areas for research. [ABSTRACT FROM AUTHOR]

Since the pioneering work of Koenker and Bassett, median-restricted models have attracted considerable interest. Attention in these models, so far, has focused on least absolute deviation (auto-)regression quantile estimation and the corresponding sign tests. These methods use a pseudolikelihood that is based on a double-exponential reference density and enjoy quite attractive properties of root n consistency (for estimators) and distribution freeness (for tests). The paper extends these results to general, i.e. not necessarily double-exponential, reference densities. Using residual signs and ranks (not signed ranks) and a general reference density f, we construct estimators that remain root n consistent, irrespective of the true underlying density g (i.e. also for g /= f). However, instead of reaching semiparametric efficiency bounds under double-exponential g, they reach these bounds when g coincides with the chosen reference density f. Moreover, we show that choosing reference densities other than the double-exponential in applications can lead to sizable gains in efficiency. The particular case of median regression is treated in detail; extensions to general quantile regression, heteroscedastic errors and time series models are briefly described. The performance of the method is also assessed by simulation and illustrated on financial data. [ABSTRACT FROM AUTHOR]

At AWE, an annual faecal sample forms part of the internal monitoring strategy for insoluble forms of plutonium. The setting of a derived investigation level (DIL) and assessment of doses from results for faecal samples can pose some difficulties in view of uncertainties related to: (a) the time and number of intakes during the monitoring period and (b) the result from a single sample. This paper uses Monte Carlo simulation to investigate the activity that would be expected in faeces following varying numbers of intake events during the monitoring period. The variation in faecal activity of a long-term excretor is also investigated. The implications of the results for the setting of the DIL and assessment of dose are discussed. [ABSTRACT FROM AUTHOR]

Previous work has shown that estimates of the admittance between topography and free-air gravity anomalies are often biased by spectral leakage, even after the application of multiple prolate spheroidal wavefunction data-tapers. Despite this, a number of authors who have used the free-air admittance method to estimate the weighted-average effective elastic thickness of the lithosphere and to identify topography supported by mantle convection have not tested their methods using synthetic data with a known relationship between topography and gravity. In this paper, I perform a range of such tests using both synthetic surface data and synthetic line-of-sight (LOS) accelerations of satellites orbiting around an extra-terrestrial planet. It is found that spectral leakage can cause the estimated admittance and coherence to be significantly in error—but only if the box in which they are estimated is too small. The definition of ‘small’ depends on the redness of the gravity spectrum. There is minimal error in the whole-box weighted-average estimate of if the admittance between surface gravity and topography is estimated within a box at least 3000-km-wide. When the synthetic (uniform) is less than 20 km and the coherence is high, the errors in are mostly ±5 km for all box sizes greater than 1000 km. On the other hand, when the true is greater than 20 km and the box size is 1000 km, the best-fitting is likely to be at least 5–10 km less than the true . However, even when the coherence is high, it is not possible to use elastic plate admittance models to distinguish between real and spurious small fractions of internal loading when the boxes are smaller than 2000 km in width. Noise in the gravity introduces error and uncertainty, but no additional bias, into the estimates of the admittance. It does, however, bias estimates of calculated using the coherence between Bouguer gravity and topography. The admittance at wavelengths between 1000 and 4000 km, which is of interest when studying mantle convection, is only well resolved when the box size is larger than 2000 km, or the synthetic is less than 15 km and the true long-wavelength admittance is greater than 30 mGal km−1. When the box size is 1000 km, it is not possible to distinguish between real and spurious convective support of long-wavelength topography. At the altitude of most orbiting satellites (200–400 km), gravity is much redder than it is at ground-level; therefore, it is necessary to use much larger boxes when using LOS accelerations to estimate the admittance than it is when using surface measurements. However, gradual changes in the altitude of the satellite, or even occasional sudden changes, make almost no difference to the estimate of the admittance. Finally, three short investigations are undertaken concerning the effect on the admittance of the application of irregular windows to the data, of lateral variations in , and of the infilling of flexural moats by low-density sediment. It is found that the bias due to the application of an irregular window is not significantly greater than the bias due to the application of a regular window of similar size; that the best-fitting is strongly biased by the value associated with the highest-amplitude topography; and that sediment infill raises the admittance at wavelengths corresponding to the width of the moat. [ABSTRACT FROM AUTHOR]

Historically, energy surveys have been carried out to identify efficiency improvements and understand annual or monthly energy use. However, meter readings are increasingly collected half-hourly, generating large volumes of data. This paper describes the management and analysis of around 2.5 million meter data points collected from buildings. A review of methods of gathering energy metering data is presented, including options for non-invasive monitoring. This is followed by a summary of mathematical methods which can be used for analysis of metering data. Finally, examples showing analysis of half-hourly metering data are presented. [ABSTRACT FROM AUTHOR]

We trace self-reference phenomena to the possibility of naming functions by names that belong to the domain over which the functions are defined. A naming system is a structure of the form (D, type( ),{ }), where D is a non-empty set; for every a∈ D, which is a name of a k-ary function, {a}: Dk → D is the function named by a, and type(a) is the type of a, which tells us if a is a name and, if it is, the arity of the named function. Under quite general conditions we get a fixed point theorem, whose special cases include the fixed point theorem underlying Gödel's proof, Kleene's recursion theorem and many other theorems of this nature, including the solution to simultaneous fixed point equations. Partial functions are accommodated by including “undefined” values; we investigate different systems arising out of different ways of dealing with them. Many-sorted naming systems are suggested as a natural approach to general computatability with many data types over arbitrary structures. The first part of the paper is a historical reconstruction of the way Gödel probably derived his proof from Cantor's diagonalization, through the semantic version of Richard. The incompleteness proof–including the fixed point construction–result from a natural line of thought, thereby dispelling the appearance of a “magic trick”. The analysis goes on to show how Kleene's recursion theorem is obtained along the same lines. [ABSTRACT FROM PUBLISHER]

Literature has mainly focused on understanding whether organized crime impacts on economic growth, broadly intended. Yet, at the local level, little is known as to how crime may affect economic activities. Using a unique geo-localized dataset on Italian firms, we exploit the strengthening of policy enforcement against corruption to show that when municipalities where the city council is dissolved because of organized-crime infiltration, the construction sector suffers a 7% reduction in the value added of firms located in the same area where the council dissolution occurred. We also find that the effect is larger, the longer the commissioner is present in the municipal council. Taken together our findings suggest that the action of the commissioner depresses the economic activity in the construction sector of the area where the temporary-administered municipality is located, given that it blocks all those relationships with firms related with criminality. This calls for a contemporaneous intervention stimulating public procurements with 'good' firms. (JEL codes: K42, D73, R10 and H32) [ABSTRACT FROM AUTHOR]