scholarly journals Search for Continuous Gravitational-wave Signals in Pulsar Timing Residuals: A New Scalable Approach with Diffusive Nested Sampling

2021 ◽  
Vol 922 (2) ◽  
pp. 228
Author(s):  
Yu-Yang Songsheng ◽  
Yi-Qian Qian ◽  
Yan-Rong Li ◽  
Pu Du ◽  
Jie-Wen Chen ◽  
...  
Keyword(s):  
Bayesian Analysis ◽  
Large Scale ◽  
Detection Sensitivity ◽  
High Dimensionality ◽  
Close Binaries ◽  
Complex Signal ◽  
Multiple Sources ◽  
General Applicability ◽  
Pulsar Timing ◽  
Order Of Magnitude

Abstract Detecting continuous nanohertz gravitational waves (GWs) generated by individual close binaries of supermassive black holes (CB-SMBHs) is one of the primary objectives of pulsar timing arrays (PTAs). The detection sensitivity is slated to increase significantly as the number of well-timed millisecond pulsars will increase by more than an order of magnitude with the advent of next-generation radio telescopes. Currently, the Bayesian analysis pipeline using parallel tempering Markov Chain Monte Carlo has been applied in multiple studies for CB-SMBH searches, but it may be challenged by the high dimensionality of the parameter space for future large-scale PTAs. One solution is to reduce the dimensionality by maximizing or marginalizing over uninformative parameters semianalytically, but it is not clear whether this approach can be extended to more complex signal models without making overly simplified assumptions. Recently, the method of diffusive nested (DNest) sampling has shown capability in coping with high dimensionality and multimodality effectively in Bayesian analysis. In this paper, we apply DNest to search for continuous GWs in simulated pulsar timing residuals and find that it performs well in terms of accuracy, robustness, and efficiency for a PTA including  ( 10 2 ) pulsars. DNest also allows a simultaneous search of multiple sources elegantly, which demonstrates its scalability and general applicability. Our results show that it is convenient and also highly beneficial to include DNest in current toolboxes of PTA analysis.

scholarly journals Nebula: ultra-efficient mapping-free structural variant genotyper

2021 ◽  
Author(s):  
Parsoa Khorsand ◽  
Fereydoun Hormozdiari
Keyword(s):  
Large Scale ◽  
Structural Variants ◽  
Sequencing Technologies ◽  
Generic Framework ◽  
Common Genetic Variants ◽  
Order Of Magnitude ◽  
Complex Events ◽  
Comparable Accuracy ◽  
Using Data ◽  
Computational Resources

Abstract Large scale catalogs of common genetic variants (including indels and structural variants) are being created using data from second and third generation whole-genome sequencing technologies. However, the genotyping of these variants in newly sequenced samples is a nontrivial task that requires extensive computational resources. Furthermore, current approaches are mostly limited to only specific types of variants and are generally prone to various errors and ambiguities when genotyping complex events. We are proposing an ultra-efficient approach for genotyping any type of structural variation that is not limited by the shortcomings and complexities of current mapping-based approaches. Our method Nebula utilizes the changes in the count of k-mers to predict the genotype of structural variants. We have shown that not only Nebula is an order of magnitude faster than mapping based approaches for genotyping structural variants, but also has comparable accuracy to state-of-the-art approaches. Furthermore, Nebula is a generic framework not limited to any specific type of event. Nebula is publicly available at https://github.com/Parsoa/Nebula.

scholarly journals Accessible Routes Integrating Data from Multiple Sources

2020 ◽  
Vol 10 (1) ◽  
pp. 7
Author(s):  
Miguel R. Luaces ◽  
Jesús A. Fisteus ◽  
Luis Sánchez-Fernández ◽  
Mario Munoz-Organero ◽  
Jesús Balado ◽  
...  
Keyword(s):  
Information System ◽  
Data Model ◽  
Large Scale ◽  
Heterogeneous Data ◽  
Multiple Sources ◽  
Heterogeneous Data Sources ◽  
Different Types ◽  
Software Sensors ◽  
The City

Providing citizens with the ability to move around in an accessible way is a requirement for all cities today. However, modeling city infrastructures so that accessible routes can be computed is a challenge because it involves collecting information from multiple, large-scale and heterogeneous data sources. In this paper, we propose and validate the architecture of an information system that creates an accessibility data model for cities by ingesting data from different types of sources and provides an application that can be used by people with different abilities to compute accessible routes. The article describes the processes that allow building a network of pedestrian infrastructures from the OpenStreetMap information (i.e., sidewalks and pedestrian crossings), improving the network with information extracted obtained from mobile-sensed LiDAR data (i.e., ramps, steps, and pedestrian crossings), detecting obstacles using volunteered information collected from the hardware sensors of the mobile devices of the citizens (i.e., ramps and steps), and detecting accessibility problems with software sensors in social networks (i.e., Twitter). The information system is validated through its application in a case study in the city of Vigo (Spain).

scholarly journals PEDRERA. Positive Energy District Renovation Model for Large Scale Actions

Energies ◽  
2021 ◽  
Vol 14 (10) ◽  
pp. 2833
Author(s):  
Paolo Civiero ◽  
Jordi Pascual ◽  
Joaquim Arcas Abella ◽  
Ander Bilbao Figuero ◽  
Jaume Salom
Keyword(s):  
Simulation Model ◽  
Performance Indicators ◽  
Large Scale ◽  
Key Performance Indicators ◽  
Positive Energy ◽  
Design Phase ◽  
Multiple Sources ◽  
Reliable Prediction ◽  
Sensitive Analysis ◽  
Web Platform

In this paper, we provide a view of the ongoing PEDRERA project, whose main scope is to design a district simulation model able to set and analyze a reliable prediction of potential business scenarios on large scale retrofitting actions, and to evaluate the overall co-benefits resulting from the renovation process of a cluster of buildings. According to this purpose and to a Positive Energy Districts (PEDs) approach, the model combines systemized data—at both building and district scale—from multiple sources and domains. A sensitive analysis of 200 scenarios provided a quick perception on how results will change once inputs are defined, and how attended results will answer to stakeholders’ requirements. In order to enable a clever input analysis and to appraise wide-ranging ranks of Key Performance Indicators (KPIs) suited to each stakeholder and design phase targets, the model is currently under the implementation in the urbanZEB tool’s web platform.

scholarly journals Developing a vocabulary and ontology for modeling insect natural history data: example data, use cases, and competency questions

2019 ◽  
Vol 7 ◽  
Author(s):  
Brian Stucky ◽  
James Balhoff ◽  
Narayani Barve ◽  
Vijay Barve ◽  
Laura Brenskelle ◽  
...  
Keyword(s):  
Natural History ◽  
Large Scale ◽  
Use Cases ◽  
Data Systems ◽  
Multiple Sources ◽  
History Data ◽  
Insect Ecology ◽  
Many Sources ◽  
Multicellular Organisms ◽  
Initial Results

Insects are possibly the most taxonomically and ecologically diverse class of multicellular organisms on Earth. Consequently, they provide nearly unlimited opportunities to develop and test ecological and evolutionary hypotheses. Currently, however, large-scale studies of insect ecology, behavior, and trait evolution are impeded by the difficulty in obtaining and analyzing data derived from natural history observations of insects. These data are typically highly heterogeneous and widely scattered among many sources, which makes developing robust information systems to aggregate and disseminate them a significant challenge. As a step towards this goal, we report initial results of a new effort to develop a standardized vocabulary and ontology for insect natural history data. In particular, we describe a new database of representative insect natural history data derived from multiple sources (but focused on data from specimens in biological collections), an analysis of the abstract conceptual areas required for a comprehensive ontology of insect natural history data, and a database of use cases and competency questions to guide the development of data systems for insect natural history data. We also discuss data modeling and technology-related challenges that must be overcome to implement robust integration of insect natural history data.

Efficient and High-Quality Seeded Graph Matching: Employing Higher-order Structural Information

2021 ◽  
Vol 15 (3) ◽  
pp. 1-31
Author(s):  
Haida Zhang ◽  
Zengfeng Huang ◽  
Xuemin Lin ◽  
Zhe Lin ◽  
Wenjie Zhang ◽  
...  
Keyword(s):  
Large Scale ◽  
Graph Matching ◽  
Structural Information ◽  
Experimental Studies ◽  
Higher Order ◽  
Personalized Pagerank ◽  
Matching Accuracy ◽  
Approximation Techniques ◽  
Order Of Magnitude ◽  
Matching Score

Driven by many real applications, we study the problem of seeded graph matching. Given two graphs and , and a small set of pre-matched node pairs where and , the problem is to identify a matching between and growing from , such that each pair in the matching corresponds to the same underlying entity. Recent studies on efficient and effective seeded graph matching have drawn a great deal of attention and many popular methods are largely based on exploring the similarity between local structures to identify matching pairs. While these recent techniques work provably well on random graphs, their accuracy is low over many real networks. In this work, we propose to utilize higher-order neighboring information to improve the matching accuracy and efficiency. As a result, a new framework of seeded graph matching is proposed, which employs Personalized PageRank (PPR) to quantify the matching score of each node pair. To further boost the matching accuracy, we propose a novel postponing strategy, which postpones the selection of pairs that have competitors with similar matching scores. We show that the postpone strategy indeed significantly improves the matching accuracy. To improve the scalability of matching large graphs, we also propose efficient approximation techniques based on algorithms for computing PPR heavy hitters. Our comprehensive experimental studies on large-scale real datasets demonstrate that, compared with state-of-the-art approaches, our framework not only increases the precision and recall both by a significant margin but also achieves speed-up up to more than one order of magnitude.

Approximate Solution of Nonclassically Damped Systems

1991 ◽  
Author(s):  
F. Ma ◽  
J. H. Hwang
Keyword(s):  
Approximate Solution ◽  
Large Scale ◽  
Computational Effort ◽  
Frequency Separation ◽  
Common Procedure ◽  
Damping Matrix ◽  
Complex Modes ◽  
Modal Coupling ◽  
Order Of Magnitude ◽  
Damped Systems

Abstract In analyzing a nonclassically damped linear system, one common procedure is to neglect those damping terms which are nonclassical, and retain the classical ones. This approach is termed the method of approximate decoupling. For large-scale systems, the computational effort at adopting approximate decoupling is at least an order of magnitude smaller than the method of complex modes. In this paper, the error introduced by approximate decoupling is evaluated. A tight error bound, which can be computed with relative ease, is given for this method of approximate solution. The role that modal coupling plays in the control of error is clarified. If the normalized damping matrix is strongly diagonally dominant, it is shown that adequate frequency separation is not necessary to ensure small errors.

Digital Mega-Studies as a New Research Paradigm: Governing the Health Research of the Future

2021 ◽  
pp. 155626462110414
Author(s):  
Jessica Bell ◽  
Megan Prictor ◽  
Lauren Davenport ◽  
Lynda O’Brien ◽  
Melissa Wake
Keyword(s):  
Large Scale ◽  
Group Processes ◽  
Research Paradigm ◽  
Multiple Sources ◽  
Individual Level ◽  
Key Characteristics ◽  
Governance Challenges ◽  
Technological Developments ◽  
Multi Stakeholder ◽  
New Research

‘Digital Mega-Studies’ are entirely or extensively digitised, longitudinal, population-scale initiatives, collecting, storing, and making available individual-level research data of different types and from multiple sources, shaped by technological developments and unforeseeable risks over time. The Australian ‘Gen V’ project exemplifies this new research paradigm. In 2019, we undertook a multidisciplinary, multi-stakeholder process to map Digital Mega-Studies’ key characteristics, legal and governance challenges and likely solutions. We conducted large and small group processes within a one-day symposium and directed online synthesis and group prioritisation over subsequent weeks. We present our methods (including elicitation, affinity mapping and prioritisation processes) and findings, proposing six priority governance principles across three areas—data, participation, trust—to support future high-quality, large-scale digital research in health.

Toward functional proteomics of alveolar macrophages

2005 ◽  
Vol 288 (4) ◽  
pp. L585-L595 ◽  
Author(s):  
Haifeng M. Wu ◽  
Ming Jin ◽  
Clay B. Marsh
Keyword(s):  
Alveolar Macrophages ◽  
Large Scale ◽  
Cell Types ◽  
Mononuclear Phagocyte ◽  
Detection Sensitivity ◽  
Current Status ◽  
Blood Monocytes ◽  
Protein Patterns ◽  
Additional Information ◽  
The Impact

Alveolar macrophages (AM) belong to a phenotype of macrophages with distinct biological functions and important pathophysiological roles in lung health and disease. The molecular details determining AM differentiation from blood monocytes and AM roles in lung homeostasis are largely unknown. With the use of different technological platforms, advances in the field of proteomics have made it possible to search for differences in protein expression between AM and their precursor monocytes. Proteome features of each cell type provide new clues into understanding mononuclear phagocyte biology. In-depth analyses using subproteomics and subcellular proteomics offer additional information by providing greater protein resolution and detection sensitivity. With the use of proteomic techniques, large-scale mapping of phosphorylation differences between the cell types have become possible. Furthermore, two-dimensional gel proteomics can detect germline protein variants and evaluate the impact of protein polymorphisms on an individual's susceptibility to disease. Finally, surface-enhanced laser desorption and ionization (SELDI) time-of-flight mass spectrometry offers an alternative method to recognizing differences in protein patterns between AM and monocytes or between AM under different pathological conditions. This review details the current status of this field and outlines future directions in functional proteomic analyses of AM and monocytes. Furthermore, this review presents viewpoints of integrating proteomics with translational topics in lung diseases to define the mechanisms of disease and to uncover new diagnostic and therapeutic targets.

scholarly journals Psychometric properties of the Adelaide Diagnostic Learning Inventory - Brief (ADLIB)

2019 ◽  
Author(s):  
Harry Minas
Keyword(s):  
Academic Performance ◽  
Learning Styles ◽  
University Students ◽  
Psychometric Properties ◽  
Factor Structure ◽  
Life Stress ◽  
Large Scale ◽  
Well Being ◽  
Factor Congruence ◽  
Order Of Magnitude

Abstract Objective: There has been increased attention in recent years to mental health, quality of life, stress and academic performance among university students, and the possible influence of learning styles. Brief reliable questionnaires are useful in large-scale multivariate research designs, such as the largely survey-based research on well-being and academic performance of university students. The objective of this study was to examine the psychometric properties of a briefer version of the 39-item Adelaide Diagnostic Learning Inventory. Results: In two survey samples - medical and physiotherapy students - a 21-item version Adelaide Diagnostic Learning Inventory - Brief (ADLIB) was shown to have the same factor structure as the parent instrument, and the factor structure of the brief instrument was found to generalise across students of medicine and physiotherapy. Sub-scale reliability estimations were in the order of magnitude of the parent instrument. Sub-scale inter-correlations, inter-factor congruence coefficients, and correlations between ADLIB sub-scale scores and several external measures provide support support for the construct and criterion validity of the instrument.

scholarly journals Best-first Enumeration Based on Bounding Conflicts, and its Application to Large-scale Hybrid Estimation (Extended Abstract)

2020 ◽  
Author(s):  
Eric Timmons ◽  
Brian C. Williams
Keyword(s):  
Large Scale ◽  
Search Space ◽  
Estimation Methods ◽  
Directed Search ◽  
Hybrid Estimation ◽  
Continuous State ◽  
Order Of Magnitude ◽  
State Models ◽  
The Cost ◽  
Belief States

State estimation methods based on hybrid discrete and continuous state models have emerged as a method of precisely computing belief states for real world systems, however they have difficulty scaling to systems with more than a handful of components. Classical, consistency based diagnosis methods scale to this level by combining best-first enumeration and conflict-directed search. While best-first methods have been developed for hybrid estimation, conflict-directed methods have thus far been elusive as conflicts summarize constraint violations, but probabilistic hybrid estimation is relatively unconstrained. In this paper we present an approach (A*BC) that unifies best-first enumeration and conflict-directed search in relatively unconstrained problems through the concept of "bounding" conflicts, an extension of conflicts that represent tighter bounds on the cost of regions of the search space. Experiments show that an A*BC powered state estimator produces estimates up to an order of magnitude faster than the current state of the art, particularly on large systems.

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