A Predictive Linear Regression Algorithm for the Neuroimaging Data Model

The Neuroimaging Data Model (NIDM) was started by an international team of cognitive scientists, computer scientists and statisticians to develop a data format capable of describing all aspects of the data lifecycle, from raw data through analyses and provenance. NIDM was built on top of the PROV standard and consists of three main interconnected specifications: Experiment, Results, and Workflow. These specifications were envisioned to capture information on all aspects of the neuroimaging data lifecycle, using semantic web techniques. They provide a critical capability to aid in reproducibility and replication of studies, as well as data discovery in shared resources. The NIDM-Experiment component has been used to describe publicly-available human neuroimaging datasets (e.g. ABIDE, ADHD200, CoRR, and OpenNeuro datasets) along with providing unambiguous descriptions of the clinical, neuropsychological, and imaging data collected as part of those studies resulting in approximately 4.5 million statements about aspects of these datasets.PyNIDM, a toolbox written in Python, supports the creation, manipulation, and query of NIDM documents. It is an open-source project hosted on GitHub and distributed under the Apache License, Version 2.0. PyNIDM is under active development and testing. Tools have been created to support RESTful SPARQL queries of the NIDM documents in support of users wanting to identify interesting cohorts across datasets in support of evaluating scientific hypotheses and/or replicating results found in the literature. This query functionality, together with the NIDM document semantics, provides a path for investigators to interrogate datasets, understand what data was collected in those studies, and provide sufficiently-annotated data dictionaries of the variables collected to facilitate transformation and combining of data across studies.Beyond querying across NIDM documents, some high-level statistical analysis tools are needed to provide investigators with an opportunity to gain more insight into data they may be interested in combining for a complete scientific investigation. Here we report on one such tool providing linear modeling support for NIDM documents: nidm_linreg.

A Predictive Linear Regression Algorithm for the Neuroimaging Data Model

10.36227/techrxiv.15062865.v1 ◽

2021 ◽

Author(s):

Ashmita Kumar

Keyword(s):

Data Model ◽

Imaging Data ◽

Linear Modeling ◽

Shared Resources ◽

Testing Tools ◽

Computer Scientists ◽

Neuroimaging Data ◽

Version 2.0 ◽

Data Lifecycle ◽

The open diffusion data derivatives, brain data upcycling via integrated publishing of derivatives and reproducible open cloud services

10.31234/osf.io/y82n7 ◽

2018 ◽

Author(s):

Paolo Avesani ◽

Brent McPherson ◽

Soichi Hayashi ◽

Cesar Caiafa ◽

Robert Henschel ◽

...

Keyword(s):

Signal To Noise Ratio ◽

Cloud Services ◽

Digital Object Identifier ◽

Imaging Data ◽

Diffusion Data ◽

Computer Scientists ◽

Neuroimaging Data ◽

Weighted Magnetic Resonance Imaging ◽

Brain Data ◽

Signal Modelling

We describe the Open Diffusion Data Derivatives (O3D) repository: an integrated collection of preserved brain data derivatives and processing pipelines, published together using a single digital-object-identifier. The data derivatives were generated using modern diffusion-weighted magnetic resonance imaging data (dMRI) with diverse properties of resolution and signal-to-noise ratio. In addition to the data, we publish all processing pipelines (also referred to as open cloud services). The pipelines utilize modern methods for neuroimaging data processing (diffusion-signal modelling, fiber tracking, tractography evaluation, white matter segmentation, and structural connectome construction). The O3D open services can allow cognitive and clinical neuroscientists to run the connectome mapping algorithms on new, user-uploaded, data. Open source code implementing all O3D services is also provided to allow computational and computer scientists to reuse and extend the processing methods. Publishing both data-derivatives and integrated processing pipeline promotes practices for scientific reproducibility and data upcycling by providing open access to the research assets for utilization by multiple scientific communities.

Differences in subcortico-cortical interactions identified from connectome and microcircuit models in autism

Nature Communications ◽

10.1038/s41467-021-21732-0 ◽

2021 ◽

Vol 12 (1) ◽

Author(s):

Bo-yong Park ◽

Seok-Jun Hong ◽

Sofie L. Valk ◽

Casey Paquola ◽

Oualid Benkarim ◽

...

Keyword(s):

Data Exchange ◽

Computational Models ◽

Simulation Models ◽

Autism Diagnostic Observation Schedule ◽

Supervised Machine Learning ◽

Global Network ◽

Imaging Data ◽

Phenotypic Data ◽

Severity Scores ◽

AbstractThe pathophysiology of autism has been suggested to involve a combination of both macroscale connectome miswiring and microcircuit anomalies. Here, we combine connectome-wide manifold learning with biophysical simulation models to understand associations between global network perturbations and microcircuit dysfunctions in autism. We studied neuroimaging and phenotypic data in 47 individuals with autism and 37 typically developing controls obtained from the Autism Brain Imaging Data Exchange initiative. Our analysis establishes significant differences in structural connectome organization in individuals with autism relative to controls, with strong between-group effects in low-level somatosensory regions and moderate effects in high-level association cortices. Computational models reveal that the degree of macroscale anomalies is related to atypical increases of recurrent excitation/inhibition, as well as subcortical inputs into cortical microcircuits, especially in sensory and motor areas. Transcriptomic association analysis based on postmortem datasets identifies genes expressed in cortical and thalamic areas from childhood to young adulthood. Finally, supervised machine learning finds that the macroscale perturbations are associated with symptom severity scores on the Autism Diagnostic Observation Schedule. Together, our analyses suggest that atypical subcortico-cortical interactions are associated with both microcircuit and macroscale connectome differences in autism.

Evaluation of Static Mapping for Dynamic Space-Shared Multi-task Processing on FPGAs

Journal of Signal Processing Systems ◽

10.1007/s11265-020-01633-z ◽

2021 ◽

Author(s):

Umar Ibrahim Minhas ◽

Roger Woods ◽

Georgios Karakonstantis

Keyword(s):

High Performance ◽

Design Space Exploration ◽

Design Space ◽

System Throughput ◽

Design Parameters ◽

Temporal Constraints ◽

Shared Resources ◽

Task Processing ◽

High Level ◽

Performance Computing

AbstractWhilst FPGAs have been used in cloud ecosystems, it is still extremely challenging to achieve high compute density when mapping heterogeneous multi-tasks on shared resources at runtime. This work addresses this by treating the FPGA resource as a service and employing multi-task processing at the high level, design space exploration and static off-line partitioning in order to allow more efficient mapping of heterogeneous tasks onto the FPGA. In addition, a new, comprehensive runtime functional simulator is used to evaluate the effect of various spatial and temporal constraints on both the existing and new approaches when varying system design parameters. A comprehensive suite of real high performance computing tasks was implemented on a Nallatech 385 FPGA card and show that our approach can provide on average 2.9 × and 2.3 × higher system throughput for compute and mixed intensity tasks, while 0.2 × lower for memory intensive tasks due to external memory access latency and bandwidth limitations. The work has been extended by introducing a novel scheduling scheme to enhance temporal utilization of resources when using the proposed approach. Additional results for large queues of mixed intensity tasks (compute and memory) show that the proposed partitioning and scheduling approach can provide higher than 3 × system speedup over previous schemes.

Evolution of the ALICE Software Framework for Run 3

EPJ Web of Conferences ◽

10.1051/epjconf/201921405010 ◽

2019 ◽

Vol 214 ◽

pp. 05010 ◽

Author(s):

Giulio Eulisse ◽

Piotr Konopka ◽

Mikolaj Krzewicki ◽

Matthias Richter ◽

David Rohr ◽

...

Keyword(s):

Data Processing ◽

Data Model ◽

Message Passing ◽

Software Framework ◽

Distributed Software ◽

Central Collisions ◽

Modular Software ◽

Level Trigger ◽

Data Throughput ◽

ALICE is one of the four major LHC experiments at CERN. When the accelerator enters the Run 3 data-taking period, starting in 2021, ALICE expects almost 100 times more Pb-Pb central collisions than now, resulting in a large increase of data throughput. In order to cope with this new challenge, the collaboration had to extensively rethink the whole data processing chain, with a tighter integration between Online and Offline computing worlds. Such a system, code-named ALICE O2, is being developed in collaboration with the FAIR experiments at GSI. It is based on the ALFA framework which provides a generalized implementation of the ALICE High Level Trigger approach, designed around distributed software entities coordinating and communicating via message passing. We will highlight our efforts to integrate ALFA within the ALICE O2 environment. We analyze the challenges arising from the different running environments for production and development, and conclude on requirements for a flexible and modular software framework. In particular we will present the ALICE O2 Data Processing Layer which deals with ALICE specific requirements in terms of Data Model. The main goal is to reduce the complexity of development of algorithms and managing a distributed system, and by that leading to a significant simplification for the large majority of the ALICE users.

AN ARCHITECTURE FOR DATA WAREHOUSING SUPPORTING DATA INDEPENDENCE AND INTEROPERABILITY

International Journal of Cooperative Information Systems ◽

10.1142/s0218843001000394 ◽

2001 ◽

Vol 10 (03) ◽

pp. 377-397 ◽

Cited By ~ 8

Author(s):

LUCA CABIBBO ◽

RICCARDO TORLONE

Keyword(s):

Data Warehouse ◽

Data Model ◽

Data Warehousing ◽

Heterogeneous Data ◽

Multidimensional Data ◽

Multidimensional Databases ◽

Level Of Aggregation ◽

High Level ◽

Data Independence ◽

Logical Architecture

We report on the design of a novel architecture for data warehousing based on the introduction of an explicit "logical" layer to the traditional data warehousing framework. This layer serves to guarantee a complete independence of OLAP applications from the physical storage structure of the data warehouse and thus allows users and applications to manipulate multidimensional data ignoring implementation details. For example, it makes possible the modification of the data warehouse organization (e.g. MOLAP or ROLAP implementation, star scheme or snowflake scheme structure) without influencing the high level description of multidimensional data and programs that use the data. Also, it supports the integration of multidimensional data stored in heterogeneous OLAP servers. We propose [Formula: see text], a simple data model for multidimensional databases, as the reference for the logical layer. [Formula: see text] provides an abstract formalism to describe the basic concepts that can be found in any OLAP system (fact, dimension, level of aggregation, and measure). We show that [Formula: see text] databases can be implemented in both relational and multidimensional storage systems. We also show that [Formula: see text] can be profitably used in OLAP applications as front-end. We finally describe the design of a practical system that supports the above logical architecture; this system is used to show in practice how the architecture we propose can hide implementation details and provides a support for interoperability between different and possibly heterogeneous data warehouse applications.

Toward the Blueprint of Campus-Based Ecosystems for Innovation

Engineering Management Research ◽

10.5539/emr.v6n1p84 ◽

2017 ◽

Vol 6 (1) ◽

pp. 84 ◽

Author(s):

A. Georges L. Romme

Keyword(s):

Knowledge Sharing ◽

Value Chain ◽

High Tech ◽

Shared Resources ◽

Budget Cuts ◽

Innovation Ecosystem ◽

Science Park ◽

Dynamic Portfolio ◽

High Level ◽

Innovation Community

The “science park” model has long been showing signs of aging, with many science parks now facing budget cuts by local and regional governments. In this study, we dissect the blueprint of a highly successful campus-based ecosystem, the High Tech Campus Eindhoven (HTCE). As an innovation ecosystem, the HTCE provides its residents (a) access to shared resources and facilities, to facilitate research and product development, and (b) an innovation community that enhances knowledge sharing between people at the campus. The success of the HTCE arises from a deep and inclusive understanding of the conditions in which an ecosystem for research and development can thrive, and the commitment to carefully grow and sustain these conditions. These conditions include: low physical distances between the various buildings, offices and shared facilities; a dynamic portfolio of thematic workshops and meetings stimulate knowledge sharing and informal networking; careful management of the diversity and reputation of the campus; attracting and hosting “connectors” that have the capability to initiate and/or manage collaboration across a newly emerging value chain; and a high level of responsiveness to requests and feedback of residents.

From Event Reports to Protest Analysis

The Internet and Political Protest in Autocracies ◽

10.1093/oso/9780190918309.003.0005 ◽

2019 ◽

pp. 62-76

Author(s):

Nils B. Weidmann ◽

Espen Geelmuyden Rød

Keyword(s):

Research Design ◽

Multilevel Models ◽

Mass Mobilization ◽

Units Of Analysis ◽

Computer Scientists ◽

The Hierarchical Structure ◽

National Unit ◽

Internet Penetration ◽

Mass Protest ◽

This chapter introduces the main elements of the research design for the empirical chapters in the book. Starting with the event reports provided by the Mass Mobilization in Autocracies Database, the chapter develops a research design that studies variation in local Internet penetration and anti-regime protest. The chapter motivates the choice of the sub-national unit of observation (cities), and temporal units of analysis (years, weeks). It introduces a new measure of Internet penetration derived from network measurements, developed in collaboration with computer scientists. The high level of spatial and temporal resolution allows for one of the most detailed analyses so far in the study of mass protest. The chapter also introduces the statistical models used for the analysis. The book relies on Bayesian multilevel models, a framework that takes into account the hierarchical structure of the data and has advantages in the analysis of data with skewed dependent variables.

Threat Representation Methods for Composite Service Process Models

International Journal of Secure Software Engineering ◽

10.4018/jsse.2013040101 ◽

2013 ◽

Vol 4 (2) ◽

pp. 1-18 ◽

Author(s):

Per Håkon Meland ◽

Erlend Andreas Gjære

Keyword(s):

Business Processes ◽

Process Models ◽

Software Systems ◽

Related Information ◽

Process Collaboration ◽

Version 2.0 ◽

Composite Services ◽

Technical Specifications ◽

Business Process Modeling Notation ◽

The Business Process Modeling Notation (BPMN) has become a popular standard for expressing high level business processes as well as technical specifications for software systems. However, the specification does not contain native support to express security information, which should not be overlooked in today’s world where every organization is exposed to threats and has assets to protect. Although a substantial amount of work enhancing BPMN 1.x with security related information already exists, the opportunities provided by version 2.0 have not received much attention in the security community so far. This paper gives an overview of security in BPMN and investigates several possibilities of representing threats in BPMN 2.0, in particular for design-time specification and runtime execution of composite services with dynamic behavior. Enriching BPMN with threat information enables a process-centric threat modeling approach that complements risk assessment and attack scenarios. We have included examples showing the use of error events, escalation events and text annotations for process, collaboration, choreography and conversation diagrams.

Efficient Implementation of Application-Aware Spinlock Control in MPSoCs

International Journal of Embedded and Real-Time Communication Systems ◽

10.4018/jertcs.2013010104 ◽

2013 ◽

Vol 4 (1) ◽

pp. 64-84 ◽

Cited By ~ 3

Author(s):

Diandian Zhang ◽

Li Lu ◽

Jeronimo Castrillon ◽

Torsten Kempf ◽

Gerd Ascheid ◽

...

Keyword(s):

High Efficiency ◽

A Priori ◽

Control Flow ◽

Specific Information ◽

Centralized Control ◽

Shared Resources ◽

Complete Control ◽

Application Aware ◽

High Level ◽

Application Specific

Spinlocks are a common technique in Multi-Processor Systems-on-Chip (MPSoCs) to protect shared resources and prevent data corruption. Without a priori application knowledge, the control of spinlocks is often highly random which can degrade the system performance significantly. To improve this, a centralized control mechanism for spinlocks is proposed in this paper, which utilizes application-specific information during spinlock control. The complete control flow is presented, which starts from integrating high-level user-defined information down to a low-level realization of the control. An Application-Specific Instruction-set Processor (ASIP) called OSIP, which was originally designed for task scheduling and mapping, is extended to support this mechanism. The case studies demonstrate the high efficiency of the proposed approach and at the same time highlight the efficiency and flexibility advantages of using an ASIP as the system controller in MPSoCs.