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49 results on '"Satrajit Ghosh"'

1. 'Tamizha, Tamizha'

Author

V. Michelle Michael and Satrajit Ghosh Chowdhury

Abstract

This chapter examined Tamil film music as a vehicle for nationalist message production and intersectional ethnic discrimination. The authors sampled the history of Tamil nationalism in India and Sri Lanka separately and found evidence that converges such territorial nationalistic content on the grounds of a common identity marker—the Tamil language. Taking notes from Tamil film music, the authors critiqued the messages promoted through this medium and applied them to the socio-political state of the ethnic conflict brewing both in the Indian state of Tamil Nadu and Sri Lanka. Further, the authors also identified prevalent themes of Tamil nationalism, refugee plight of Eelam Tamils, irredentism, and othering in Tamil film music that contribute towards a broader scholarship of music as an effective medium for political messages.

Published
2023
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2. Neurodesk: An accessible, flexible, and portable data analysis environment for reproducible neuroimaging

Author

Steffen Bollmann, Angela Renton, Thuy Dao, Tom Johnstone, Oren Civier, Ryan Sullivan, David White, Paris Lyons, Benjamin Slade, David Abbott, Toluwani Amos, Saskia Bollmann, Andy Botting, Megan Campbell, Jeryn Chang, Thomas Close, Korbinian Eckstein, Gary Egan, Stefanie Evas, Guillaume Flandin, Kelly Garner, Marta Garrido, Satrajit Ghosh, Martin Grignard, Anthony Hannan, Laurentius (Renzo) Huber, Jakub Kaczmarzyk, Lars Kasper, Levin Kuhlmann, Kexin Lou, Yorguin-Jose Mantilla-Ramos, Jason Mattingley, Jo Morris, Akshaiy Narayanan, Franco Pestilli, Aina Puce, Fernanda Ribeiro, Nigel Rogasch, Chris Rorden, Mark Schira, Thomas Shaw, Paul Sowman, Gershon Spitz, Ashley Stewart, Xincheng Ye, Judy Zhu, Matthew Hughes, and Aswin Narayanan

Abstract

Neuroimaging data analysis often requires purpose-built software, which can be challenging to install and may produce different results across computing environments. Beyond being a roadblock to neuroscientists, these issues of accessibility and portability can hamper the reproducibility of neuroimaging data analysis pipelines. Here, we introduce the Neurodesk platform, which harnesses software containers to support a comprehensive and growing suite of neuroimaging software (https://www.neurodesk.org/). Neurodesk includes a browser-accessible virtual desktop environment and a command line interface, mediating access to containerized neuroimaging software libraries on various computing platforms, including personal and high-performance computers, cloud computing and Jupyter Notebooks. This community-oriented, open-source platform enables a paradigm shift for neuroimaging data analysis, allowing for accessible, flexible, fully reproducible, and portable data analysis pipelines.

Published
2023
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3. Dissociating COVID-19 from other respiratory infections based on acoustic, motor coordination, and phonemic patterns

Author

Tanya Talkar, Daniel M. Low, Andrew J. Simpkin, Satrajit Ghosh, Derek T. O’Keeffe, and Thomas F. Quatieri

Subjects
Multidisciplinary
Abstract

In the face of the global pandemic caused by the disease COVID-19, researchers have increasingly turned to simple measures to detect and monitor the presence of the disease in individuals at home. We sought to determine if measures of neuromotor coordination, derived from acoustic time series, as well as phoneme-based and standard acoustic features extracted from recordings of simple speech tasks could aid in detecting the presence of COVID-19. We further hypothesized that these features would aid in characterizing the effect of COVID-19 on speech production systems. A protocol, consisting of a variety of speech tasks, was administered to 12 individuals with COVID-19 and 15 individuals with other viral infections at University Hospital Galway. From these recordings, we extracted a set of acoustic time series representative of speech production subsystems, as well as their univariate statistics. The time series were further utilized to derive correlation-based features, a proxy for speech production motor coordination. We additionally extracted phoneme-based features. These features were used to create machine learning models to distinguish between the COVID-19 positive and other viral infection groups, with respiratory- and laryngeal-based features resulting in the highest performance. Coordination-based features derived from harmonic-to-noise ratio time series from read speech discriminated between the two groups with an area under the ROC curve (AUC) of 0.94. A longitudinal case study of two subjects, one from each group, revealed differences in laryngeal based acoustic features, consistent with observed physiological differences between the two groups. The results from this analysis highlight the promise of using nonintrusive sensing through simple speech recordings for early warning and tracking of COVID-19.

Published
2023
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4. Brain growth charts of 'clinical controls' for quantitative analysis of clinically acquired brain MRI

Author

Jenna M. Schabdach, J. Eric Schmitt, Susan Sotardi, Arastoo Vossough, Savvas Andronikou, Timothy P. Roberts, Hao Huang, Viveknarayanan Padmanabhan, Alfredo Oritz-Rosa, Margaret Gardner, Sydney Covitz, Saashi A. Bedford, Ayan Mandal, Barbara H. Chaiyachati, Simon R. White, Ed Bullmore, Richard A.I. Bethlehem, Russell T. Shinohara, Benjamin Billot, J. Eugenio Iglesias, Satrajit Ghosh, Raquel E. Gur, Theodore D. Satterthwaite, David Roalf, Jakob Seidlitz, and Aaron Alexander-Bloch

Abstract

BackgroundBrain MRIs acquired in clinical settings represent a valuable and underutilized scientific resource for investigating neurodevelopment. Utilization of these clinical scans has been limited because of their clinical acquisition and technical heterogeneity. These barriers have curtailed the interpretability and scientific value of retrospective studies of clinically acquired brain MRIs, compared to studies of prospectively acquired research quality brain MRIs.PurposeTo develop a scalable and rigorous approach to generate clinical brain growth chart models, to benchmark neuroanatomical differences in clinical MRIs, and to validate clinically-derived brain growth charts against those derived from large-scale research studies.Materials and MethodsWe curated a set of clinical MRIScans withLimitedImagingPathology (SLIP) – so-called “clinical controls” – from an urban pediatric healthcare system acquired between 2005 and 2020. The curation process included manual review of signed radiology reports, as well as automated and manual quality review of images without gross pathology. We measured global and regional volumetric imaging phenotypes in the SLIP sample using two alternative, advanced image processing pipelines, and quantitatively compared clinical brain growth charts to research brain growth charts derived from >123,000 MRIs.ResultsThe curated SLIP dataset included 372 patients scanned between the ages of 28 days post-birth and 22.2 years across nine 3T MRI scanners. Clinical brain growth charts were highly similar to growth charts derived from large-scale research datasets, in terms of the normative developmental trajectories predicted by the models. The clinical indication of the scans did not significantly bias the output of clinical brain charts. Tens of thousands of additional healthcare system scans meet inclusion criteria to be included in future brain growth charts.ConclusionBrain charts derived from clinical-controls are highly similar to brain charts from research-controls, suggesting that curated clinical scans could be used to supplement research datasets.Summary StatementBrain growth charts of pediatric clinical MRIs with limited imaging pathology (N=372) are highly correlated with charts from a large aggregated set of research controls (N>120,000).Key ResultsA cohort of brain MRI scans with limited reported imaging pathology (N=372, 186 female; ages 0.07 - 22.2 years, median = 10.2) were identified using signed radiology reports and processed using two segmentation pipelines. Growth charts generated from these scans are highly correlated with growth charts from a large aggregated set of research controls (r range 0.990 - 0.999). There was no evidence of bias due to the reason for each scan.

Published
2023
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6. The BRAIN Initiative Cell Census Network Data Ecosystem: A User’s Guide

Author

Maryann Martone, Satrajit Ghosh, Giorgio Ascoli, Patrick Hof, Rongxin Fang, David Osumi-Sutherland, Jesse Gillis, and Jan G. Bjaalie

Abstract

Characterizing cellular diversity at different levels of biological organization across data modalities is a prerequisite to understanding the function of cell types in the brain. Classification of neurons is also required to manipulate cell types in controlled ways, and to understand their variation and vulnerability in brain disorders. TheBRAIN Initiative Cell Census Network (BICCN)is an integrated network of data generating centers, data archives and data standards developers, with the goal of systematic multimodal brain cell type profiling and characterization. Emphasis of the BICCN is on the whole mouse brain and demonstration of prototypes for human and non-human primate (NHP) brains. Here, we provide a guide to the cellular and spatial approaches employed, and to accessing and using the BICCN data and its extensive resources, including theBRAIN Cell Data Center (BCDC)which serves to manage and integrate data across the ecosystem. We illustrate the power of the BICCN data ecosystem through vignettes highlighting several BICCN analysis and visualization tools. Finally, we present emerging standards that have been developed or adopted by the BICCN toward FAIR (Wilkinson et al. 2016a) neuroscience. The combined BICCN ecosystem provides a comprehensive resource for the exploration and analysis of cell types in the brain.

Published
2022
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7. Through the looking glass: Deep interpretable dynamic directed connectivity in resting fMRI

Author

Usman Mahmood, Zening Fu, Satrajit Ghosh, Vince Calhoun, and Sergey Plis

Subjects
Neurology, Cognitive Neuroscience
Abstract

Brain network interactions are commonly assessed via functional (network) connectivity, captured as an undirected matrix of Pearson correlation coefficients. Functional connectivity can represent static and dynamic relations, but often these are modeled using a fixed choice for the data window Alternatively, deep learning models may flexibly learn various representations from the same data based on the model architecture and the training task. However, the representations produced by deep learning models are often difficult to interpret and require additional posthoc methods, e.g., saliency maps. In this work, we integrate the strengths of deep learning and functional connectivity methods while also mitigating their weaknesses. With interpretability in mind, we present a deep learning architecture that exposes a directed graph layer that represents what the model has learned about relevant brain connectivity. A surprising benefit of this architectural interpretability is significantly improved accuracy in discriminating controls and patients with schizophrenia, autism, and dementia, as well as age and gender prediction from functional MRI data. We also resolve the window size selection problem for dynamic directed connectivity estimation as we estimate windowing functions from the data, capturing what is needed to estimate the graph at each time-point. We demonstrate efficacy of our method in comparison with multiple existing models that focus on classification accuracy, unlike our interpretability-focused architecture. Using the same data but training different models on their own discriminative tasks we are able to estimate task-specific directed connectivity matrices for each subject. Results show that the proposed approach is also more robust to confounding factors compared to standard dynamic functional connectivity models. The dynamic patterns captured by our model are naturally interpretable since they highlight the intervals in the signal that are most important for the prediction. The proposed approach reveals that differences in connectivity among sensorimotor networks relative to default-mode networks are an important indicator of dementia and gender. Dysconnectivity between networks, specially sensorimotor and visual, is linked with schizophrenic patients, however schizophrenic patients show increased intra-network default-mode connectivity compared to healthy controls. Sensorimotor connectivity was important for both dementia and schizophrenia prediction, but schizophrenia is more related to dysconnectivity between networks whereas, dementia bio-markers were mostly intra-network connectivity.

Published
2022

8. Deep neural networks allow expert-level brain meningioma segmentation and present potential for improvement of clinical practice

Author

Alessandro Boaro, Jakub R. Kaczmarzyk, Vasileios K. Kavouridis, Maya Harary, Marco Mammi, Hassan Dawood, Alice Shea, Elise Y. Cho, Parikshit Juvekar, Thomas Noh, Aakanksha Rana, Satrajit Ghosh, and Omar Arnaout

Subjects
Computer, Deep Learning, Multidisciplinary, Neural Networks, Meningeal Neoplasms, Brain, Humans, Neural Networks, Computer, Meningioma
Abstract

Accurate brain meningioma segmentation and volumetric assessment are critical for serial patient follow-up, surgical planning and monitoring response to treatment. Current gold standard of manual labeling is a time-consuming process, subject to inter-user variability. Fully-automated algorithms for meningioma segmentation have the potential to bring volumetric analysis into clinical and research workflows by increasing accuracy and efficiency, reducing inter-user variability and saving time. Previous research has focused solely on segmentation tasks without assessment of impact and usability of deep learning solutions in clinical practice. Herein, we demonstrate a three-dimensional convolutional neural network (3D-CNN) that performs expert-level, automated meningioma segmentation and volume estimation on MRI scans. A 3D-CNN was initially trained by segmenting entire brain volumes using a dataset of 10,099 healthy brain MRIs. Using transfer learning, the network was then specifically trained on meningioma segmentation using 806 expert-labeled MRIs. The final model achieved a median performance of 88.2% reaching the spectrum of current inter-expert variability (82.6–91.6%). We demonstrate in a simulated clinical scenario that a deep learning approach to meningioma segmentation is feasible, highly accurate and has the potential to improve current clinical practice.

Published
2022
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9. The Neurodata Without Borders ecosystem for neurophysiological data science

Author

Satrajit Ghosh, Benjamin K Dichter, Ryan Ly, Andrew Tritt, Oliver Rübel, Lawrence Niu, Pamela Baker, Ivan Soltesz, Lydia Ng, Karel Svoboda, Loren Frank, and Kristofer E Bouchard

Subjects
data ecosystem, FAIR data, Metadata, archive, Mouse, General Immunology and Microbiology, General Neuroscience, Data Science, Neurosciences, Neurophysiology, General Medicine, General Biochemistry, Genetics and Molecular Biology, neuroscience, Networking and Information Technology R&D (NITRD), data standard, data language, Rat, Humans, human, Biochemistry and Cell Biology, Ecosystem, Software
Abstract

The neurophysiology of cells and tissues are monitored electrophysiologically and optically in diverse experiments and species, ranging from flies to humans. Understanding the brain requires integration of data across this diversity, and thus these data must be findable, accessible, interoperable, and reusable (FAIR). This requires a standard language for data and metadata that can coevolve with neuroscience. We describe design and implementation principles for a language for neurophysiology data. Our open-source software (Neurodata Without Borders, NWB) defines and modularizes the interdependent, yet separable, components of a data language. We demonstrate NWB's impact through unified description of neurophysiology data across diverse modalities and species. NWB exists in an ecosystem, which includes data management, analysis, visualization, and archive tools. Thus, the NWB data language enables reproduction, interchange, and reuse of diverse neurophysiology data. More broadly, the design principles of NWB are generally applicable to enhance discovery across biology through data FAIRness.The brain is an immensely complex organ which regulates many of the behaviors that animals need to survive. To understand how the brain works, scientists monitor and record brain activity under different conditions using a variety of experimental techniques. These neurophysiological studies are often conducted on multiple types of cells in the brain as well as a variety of species, ranging from mice to flies, or even frogs and worms. Such a range of approaches provides us with highly informative, complementary ‘views’ of the brain. However, to form a complete, coherent picture of how the brain works, scientists need to be able to integrate all the data from these different experiments. For this to happen effectively, neurophysiology data need to meet certain criteria: namely, they must be findable, accessible, interoperable, and re-usable (or FAIR for short). However, the sheer diversity of neurophysiology experiments impedes the ‘FAIR’-ness of the information obtained from them. To overcome this problem, researchers need a standardized way to communicate their experiments and share their results – in other words, a ‘standard language’ to describe neurophysiology data. Rübel, Tritt, Ly, Dichter, Ghosh et al. therefore set out to create such a language that was not only FAIR, but could also co-evolve with neurophysiology research. First, they produced a computer software program (called Neurodata Without Borders, or NWB for short) which generated and defined the different components of the new standard language. Then, other tools for data management were created to expand the NWB platform using the standardized language. This included data analysis and visualization methods, as well as an ‘archive’ to store and access data. Testing the new language and associated tools showed that they indeed allowed researchers to access, analyze, and share information from many different types of experiments, in organisms ranging from flies to humans. The NWB software is open-source, meaning that anyone can obtain a copy and make changes to it. Thus, NWB and its associated resources provide the basis for a collaborative, community-based system for sharing neurophysiology data. Rübel et al. hope that NWB will inspire similar developments across other fields of biology that share similar levels of complexity with neurophysiology.

Published
2022

10. Multivariate and univariate real-time functional imaging (MURFI) user manual v2

Author

Clemens C.C. Bauer, not provided Jiahe Zhang, not provided Francesca Morfini, Aaron Kucyi, not provided Jovicarole Raya, not provided Zoi Urban, Satrajit Ghosh, not provided Oliver Hinds, not provided Randy Aurerbach, not provided David Pagliaccio, and not provided Susan Whitfield-Gabrieli[Northeastern

Abstract

This manual describes the setup and use of multivariate and univariate real-time functional imaging (MURFI) software to deliver real-time functional magnetic resonance imaging neurofeedback (rt-fMRI-nf).

Published
2022
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11. Multivariate and univariate real-time functional imaging (MURFI) user manual v1

Author

Clemens C.C. Bauer, not provided Jiahe Zhang, not provided Francesca Morfini, Aaron Kucyi, not provided Jovicarole Raya, not provided Zoi Urban, Satrajit Ghosh, not provided Oliver Hinds, not provided Randy Aurerbach, not provided David Pagliaccio, and not provided Susan Whitfield-Gabrieli[Northeastern

Abstract

This manual describes the setup and use of multivariate and univariate real-time functional imaging (MURFI) software to deliver real-time functional magnetic resonance imaging neurofeedback (rt-fMRI-nf).

Published
2022
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13. TemplateFlow: FAIR-sharing of multi-scale, multi-species brain models

Author

Rastko Ciric, William Thompson, Romy Lorenz, Mathias Goncalves, Eilidh MacNicol, Christopher Markiewicz, Yaroslav Halchenko, Satrajit Ghosh, Krzysztof Gorgolewski, Russell Poldrack, and Oscar Esteban

Abstract

Reference anatomies of the brain and corresponding atlases play a central role in experimental neuroimaging workflows and are the foundation for reporting standardized results. The choice of such references —i.e., templates— and atlases is one relevant source of methodological variability across studies, which has recently been brought to attention as an important challenge to reproducibility in neuroscience. TemplateFlow is a publicly available framework for human and nonhuman brain models. The framework combines an open database with software for access, management, and vetting, allowing scientists to distribute their resources under FAIR —findable, accessible, interoperable, reusable— principles. TemplateFlow supports a multifaceted insight into brains across species, and enables multiverse analyses testing whether results generalize across standard references, scales, and in the long term, species, thereby contributing to increasing the reliability of neuroimaging results.

Published
2021
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14. Blending of Traditional System and Digital Pedagogy: An Indian Perspective

Author

Ishita De Ghosh and Satrajit Ghosh

Subjects
Blended learning, Formative assessment, Higher education, business.industry, Computer science, Salient, Paradigm shift, Pedagogy, Perspective (graphical), ComputingMilieux_COMPUTERSANDEDUCATION, Computational intelligence, business, Indigenous
Abstract

Proper embedding of digitized facilities in traditional teaching–learning system is a salient characteristics of modern pedagogy. State-of-the-art computing is greatly influenced by computational intelligence. The chapter is a study on changing scenario of teaching–learning in India focusing on basic paradigm shifts. It explores blending of traditional system with digital technology in Indian perspective. At first, it gives a brief survey on blended learning. Then, it presents two teaching–learning models incorporating digital technology. Intelligent School Network for Research (ISNR) is a research-based teaching–learning model for school-level learners. It aims to instil inquisitiveness and research orientation in young minds with the objective that a number of socio-environmental problems can be resolved by indigenous ideas of young scientists. Intelligent Feedback System for Classrooms (IFSC) is a feedback model for formative assessment of learners in classrooms. It thrives on intelligent data analysis from CCTV-camera enabled classrooms. Though both models are designed for school education system, they can easily be extended for higher education systems.

Published
2020
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16. Improved Primitives for MPC over Mixed Arithmetic-Binary Circuits

Author

Peter Scholl, Daniel Escudero, Rahul Rachuri, Satrajit Ghosh, and Marcel Keller

Subjects
050101 languages & linguistics, Computer science, Truncation, Computation, 05 social sciences, Binary number, 02 engineering and technology, Translation (geometry), Domain (software engineering), Binary data, 0202 electrical engineering, electronic engineering, information engineering, Decomposition (computer science), 020201 artificial intelligence & image processing, 0501 psychology and cognitive sciences, Hardware_ARITHMETICANDLOGICSTRUCTURES, Arithmetic, Computer Science::Cryptography and Security, Electronic circuit
Abstract

This work introduces novel techniques to improve the translation between arithmetic and binary data types in secure multi-party computation. We introduce a new approach to performing these conversions using what we call extended doubly-authenticated bits (edaBits), which correspond to shared integers in the arithmetic domain whose bit decomposition is shared in the binary domain. These can be used to considerably increase the efficiency of non-linear operations such as truncation, secure comparison and bit-decomposition.

Published
2020
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17. Tools for FAIR Neuroimaging Experiment Metadata Annotation with NIDM Experiment

Author

David Keator, Karl Helmer, Camille Maumet, Smruti Padhy, Dorota Jarecka, David Kennedy, Satrajit Ghosh, Jean-Baptiste Poline, University of California [Irvine] (UC Irvine), University of California (UC), Massachusetts General Hospital [Boston], Neuroimagerie: méthodes et applications (Empenn), Institut National de la Santé et de la Recherche Médicale (INSERM)-Inria Rennes – Bretagne Atlantique, Institut National de Recherche en Informatique et en Automatique (Inria)-Institut National de Recherche en Informatique et en Automatique (Inria)-SIGNAUX ET IMAGES NUMÉRIQUES, ROBOTIQUE (IRISA-D5), Institut de Recherche en Informatique et Systèmes Aléatoires (IRISA), Université de Rennes (UR)-Institut National des Sciences Appliquées - Rennes (INSA Rennes), Institut National des Sciences Appliquées (INSA)-Institut National des Sciences Appliquées (INSA)-Université de Bretagne Sud (UBS)-École normale supérieure - Rennes (ENS Rennes)-Institut National de Recherche en Informatique et en Automatique (Inria)-CentraleSupélec-Centre National de la Recherche Scientifique (CNRS)-IMT Atlantique (IMT Atlantique), Institut Mines-Télécom [Paris] (IMT)-Institut Mines-Télécom [Paris] (IMT)-Université de Rennes (UR)-Institut National des Sciences Appliquées - Rennes (INSA Rennes), Institut Mines-Télécom [Paris] (IMT)-Institut Mines-Télécom [Paris] (IMT)-Institut de Recherche en Informatique et Systèmes Aléatoires (IRISA), Institut National des Sciences Appliquées (INSA)-Institut National des Sciences Appliquées (INSA)-Université de Bretagne Sud (UBS)-École normale supérieure - Rennes (ENS Rennes)-CentraleSupélec-Centre National de la Recherche Scientifique (CNRS)-IMT Atlantique (IMT Atlantique), Institut Mines-Télécom [Paris] (IMT)-Institut Mines-Télécom [Paris] (IMT), Massachusetts Institute of Technology (MIT), Child and Adolescent NeuroDevelopment Initiative (CANDI), University of Massachusetts Medical School [Worcester] (UMASS), University of Massachusetts System (UMASS)-University of Massachusetts System (UMASS)-Division of Neuroinformatics, McGill University = Université McGill [Montréal, Canada], University of California [Irvine] (UCI), University of California, Empenn, Université de Bretagne Sud (UBS)-Institut National des Sciences Appliquées - Rennes (INSA Rennes), Institut National des Sciences Appliquées (INSA)-Université de Rennes (UNIV-RENNES)-Institut National des Sciences Appliquées (INSA)-Université de Rennes (UNIV-RENNES)-Institut National de Recherche en Informatique et en Automatique (Inria)-École normale supérieure - Rennes (ENS Rennes)-Centre National de la Recherche Scientifique (CNRS)-Université de Rennes 1 (UR1), Université de Rennes (UNIV-RENNES)-CentraleSupélec-IMT Atlantique Bretagne-Pays de la Loire (IMT Atlantique), Institut Mines-Télécom [Paris] (IMT)-Institut Mines-Télécom [Paris] (IMT)-Université de Bretagne Sud (UBS)-Institut National des Sciences Appliquées - Rennes (INSA Rennes), Institut National des Sciences Appliquées (INSA)-Université de Rennes (UNIV-RENNES)-Institut National des Sciences Appliquées (INSA)-Université de Rennes (UNIV-RENNES)-École normale supérieure - Rennes (ENS Rennes)-Centre National de la Recherche Scientifique (CNRS)-Université de Rennes 1 (UR1), and Maumet, Camille

Subjects
[SCCO.NEUR]Cognitive science/Neuroscience, [SCCO.NEUR] Cognitive science/Neuroscience, ComputingMilieux_MISCELLANEOUS
Abstract

International audience

Published
2019

18. Constructing an Ontology of Neuroscience Experiments for the Neuroimaging Data Model (NIDM) Authors: Introduction

Author

Karl Helmer, David Keator, Tibor Auer, Satrajit Ghosh, Camille Maumet, Thomas Nichols, Jean-Baptiste Poline, Massachusetts General Hospital [Boston], University of California [Irvine] (UC Irvine), University of California (UC), Cognition and Brain Sciences Unit (MRC CBU), University of Cambridge [UK] (CAM), Massachusetts Institute of Technology (MIT), Neuroimagerie: méthodes et applications (Empenn), Institut National de la Santé et de la Recherche Médicale (INSERM)-Inria Rennes – Bretagne Atlantique, Institut National de Recherche en Informatique et en Automatique (Inria)-Institut National de Recherche en Informatique et en Automatique (Inria)-SIGNAUX ET IMAGES NUMÉRIQUES, ROBOTIQUE (IRISA-D5), Institut de Recherche en Informatique et Systèmes Aléatoires (IRISA), Université de Rennes (UR)-Institut National des Sciences Appliquées - Rennes (INSA Rennes), Institut National des Sciences Appliquées (INSA)-Institut National des Sciences Appliquées (INSA)-Université de Bretagne Sud (UBS)-École normale supérieure - Rennes (ENS Rennes)-Institut National de Recherche en Informatique et en Automatique (Inria)-CentraleSupélec-Centre National de la Recherche Scientifique (CNRS)-IMT Atlantique (IMT Atlantique), Institut Mines-Télécom [Paris] (IMT)-Institut Mines-Télécom [Paris] (IMT)-Université de Rennes (UR)-Institut National des Sciences Appliquées - Rennes (INSA Rennes), Institut Mines-Télécom [Paris] (IMT)-Institut Mines-Télécom [Paris] (IMT)-Institut de Recherche en Informatique et Systèmes Aléatoires (IRISA), Institut National des Sciences Appliquées (INSA)-Institut National des Sciences Appliquées (INSA)-Université de Bretagne Sud (UBS)-École normale supérieure - Rennes (ENS Rennes)-CentraleSupélec-Centre National de la Recherche Scientifique (CNRS)-IMT Atlantique (IMT Atlantique), Institut Mines-Télécom [Paris] (IMT)-Institut Mines-Télécom [Paris] (IMT), University of Oxford, McGill University = Université McGill [Montréal, Canada], Maumet, Camille, University of California [Irvine] (UCI), University of California, Empenn, Université de Bretagne Sud (UBS)-Institut National des Sciences Appliquées - Rennes (INSA Rennes), Institut National des Sciences Appliquées (INSA)-Université de Rennes (UNIV-RENNES)-Institut National des Sciences Appliquées (INSA)-Université de Rennes (UNIV-RENNES)-Institut National de Recherche en Informatique et en Automatique (Inria)-École normale supérieure - Rennes (ENS Rennes)-Centre National de la Recherche Scientifique (CNRS)-Université de Rennes 1 (UR1), Université de Rennes (UNIV-RENNES)-CentraleSupélec-IMT Atlantique Bretagne-Pays de la Loire (IMT Atlantique), Institut Mines-Télécom [Paris] (IMT)-Institut Mines-Télécom [Paris] (IMT)-Université de Bretagne Sud (UBS)-Institut National des Sciences Appliquées - Rennes (INSA Rennes), Institut National des Sciences Appliquées (INSA)-Université de Rennes (UNIV-RENNES)-Institut National des Sciences Appliquées (INSA)-Université de Rennes (UNIV-RENNES)-École normale supérieure - Rennes (ENS Rennes)-Centre National de la Recherche Scientifique (CNRS)-Université de Rennes 1 (UR1), and University of Oxford [Oxford]

Subjects
[SCCO.NEUR]Cognitive science/Neuroscience, [SCCO.NEUR] Cognitive science/Neuroscience, ComputingMilieux_MISCELLANEOUS
Abstract

International audience

Published
2019

20. The best of both worlds: using semantic web with JSON-LD. An example with NIDM-Results & Datalad

Author

Camille Maumet, Satrajit Ghosh, Yaroslav Halchenko, Dorota Jarecka, Nolan Nichols, Jean-Baptiste Poline, Michael Hanke, Maumet, Camille, Neuroimagerie: méthodes et applications (Empenn), Institut National de la Santé et de la Recherche Médicale (INSERM)-Inria Rennes – Bretagne Atlantique, Institut National de Recherche en Informatique et en Automatique (Inria)-Institut National de Recherche en Informatique et en Automatique (Inria)-SIGNAUX ET IMAGES NUMÉRIQUES, ROBOTIQUE (IRISA-D5), Institut de Recherche en Informatique et Systèmes Aléatoires (IRISA), Université de Rennes (UR)-Institut National des Sciences Appliquées - Rennes (INSA Rennes), Institut National des Sciences Appliquées (INSA)-Institut National des Sciences Appliquées (INSA)-Université de Bretagne Sud (UBS)-École normale supérieure - Rennes (ENS Rennes)-Institut National de Recherche en Informatique et en Automatique (Inria)-CentraleSupélec-Centre National de la Recherche Scientifique (CNRS)-IMT Atlantique (IMT Atlantique), Institut Mines-Télécom [Paris] (IMT)-Institut Mines-Télécom [Paris] (IMT)-Université de Rennes (UR)-Institut National des Sciences Appliquées - Rennes (INSA Rennes), Institut Mines-Télécom [Paris] (IMT)-Institut Mines-Télécom [Paris] (IMT)-Institut de Recherche en Informatique et Systèmes Aléatoires (IRISA), Institut National des Sciences Appliquées (INSA)-Institut National des Sciences Appliquées (INSA)-Université de Bretagne Sud (UBS)-École normale supérieure - Rennes (ENS Rennes)-CentraleSupélec-Centre National de la Recherche Scientifique (CNRS)-IMT Atlantique (IMT Atlantique), Institut Mines-Télécom [Paris] (IMT)-Institut Mines-Télécom [Paris] (IMT), Massachusetts Institute of Technology (MIT), Darmouth College [Hanover, New Hampshire], Genentech, Inc., Genentech, Inc. [San Francisco], McGill University = Université McGill [Montréal, Canada], Lawrence Berkeley National Laboratory [Berkeley] (LBNL), Institute of Neuroscience and Medicine [Jülich] (INM-1), DataLad development was supported by the NSF (CRCNS 1429999), German federal state of Saxony-Anhalt and the European Regional Development Fund (ERDF), Project: Center for Behavioral Brain Sciences, Imaging Platform. JBP was partially funded by NIH-NIBIB P41 EB019936 (ReproNim) NIH-NIMH R01 MH083320 (CANDIShare) and NIH 5U24 DA039832 (NIF), as well as the Canada First Research Excellence Fund, awarded to McGill University for the Healthy Brains for Healthy Lives initiative., Empenn, Université de Bretagne Sud (UBS)-Institut National des Sciences Appliquées - Rennes (INSA Rennes), Institut National des Sciences Appliquées (INSA)-Université de Rennes (UNIV-RENNES)-Institut National des Sciences Appliquées (INSA)-Université de Rennes (UNIV-RENNES)-Institut National de Recherche en Informatique et en Automatique (Inria)-École normale supérieure - Rennes (ENS Rennes)-Centre National de la Recherche Scientifique (CNRS)-Université de Rennes 1 (UR1), Université de Rennes (UNIV-RENNES)-CentraleSupélec-IMT Atlantique Bretagne-Pays de la Loire (IMT Atlantique), Institut Mines-Télécom [Paris] (IMT)-Institut Mines-Télécom [Paris] (IMT)-Université de Bretagne Sud (UBS)-Institut National des Sciences Appliquées - Rennes (INSA Rennes), and Institut National des Sciences Appliquées (INSA)-Université de Rennes (UNIV-RENNES)-Institut National des Sciences Appliquées (INSA)-Université de Rennes (UNIV-RENNES)-École normale supérieure - Rennes (ENS Rennes)-Centre National de la Recherche Scientifique (CNRS)-Université de Rennes 1 (UR1)

Subjects
[SDV.NEU]Life Sciences [q-bio]/Neurons and Cognition [q-bio.NC], [SDV.NEU] Life Sciences [q-bio]/Neurons and Cognition [q-bio.NC]
Abstract

International audience; NIDM-Results provides a harmonised representation for fMRI results reporting using Semantic Web technologies (Maumet et al. 2016). While those technologies are particularly well suited for aggregation across complex datasets, using them can be costly in terms of initial development time to generate and read the corresponding serialisations. While the technology is machine accessible, it can be difficult to comprehend by humans. This hinders adoption by scientific communities and by software developers used to more-lightweight data-exchange formats, such as JSON. JSON-LD: a JSON representation for semantic graphs (“JSON-LD 1.1” n.d.) was created to address this limitation and recent extensions to the specification allow creating JSON-LD documents that are structured more similar to simple JSON. This representation is simultaneously readable by a large number of JSON-based applications and by Semantic Web tools. Here we review our work on building a JSON-LD representation for NIDM-Results data and exposing it to Datalad (Halchenko et al. 2018), a data-management tool suitable for neuroimaging datasets with built-in support for metadata extraction and search.

Published
2019

21. The Communication Complexity of Threshold Private Set Intersection

Author

Satrajit Ghosh, Mark Simkin, Boldyreva, Alexandra, and Micciancio, Daniele

Subjects
Matching (graph theory), Intersection (set theory), Homomorphic encryption, 0102 computer and information sciences, 02 engineering and technology, 01 natural sciences, Upper and lower bounds, Combinatorics, 010201 computation theory & mathematics, 0202 electrical engineering, electronic engineering, information engineering, 020201 artificial intelligence & image processing, Communication complexity, Private set intersection, Computer Science::Cryptography and Security, Mathematics
Abstract

Threshold private set intersection enables Alice and Bob who hold sets (formula presented) and (formula presented) of size n to compute the intersection (formula presented) if the sets do not differ by more than some threshold parameter t. In this work, we investigate the communication complexity of this problem and we establish the first upper and lower bounds. We show that any protocol has to have a communication complexity of (formula presented). We show that an almost matching upper bound of (formula presented) can be obtained via fully homomorphic encryption. We present a computationally more efficient protocol based on weaker assumptions, namely additively homomorphic encryption, with a communication complexity of (formula presented). For applications like biometric authentication, where a given fingerprint has to have a large intersection with a fingerprint from a database, our protocols may result in significant communication savings. Prior to this work, all previous protocols had a communication complexity of (formula presented). Our protocols are the first ones with communication complexities that mainly depend on the threshold parameter t and only logarithmically on the set size n.

Published
2019
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22. Development of an Ontology for the INCF Neuroimaging Data Model (NIDM)

Author

Karl Helmer, Keator David, Tibor Auer, Satrajit Ghosh, Camille Maumet, Nichols, Thomas E., Padhy Smruti, Jean-Baptiste Poline, Massachusetts General Hospital [Boston], Department of Psychiatry and Human Behavior [Irvine], University of California [Irvine] (UCI), University of California-University of California, Cognition and Brain Sciences Unit (MRC CBU), University of Cambridge [UK] (CAM), Massachusetts Institute of Technology (MIT), Vision, Action et Gestion d'informations en Santé (VisAGeS), Institut National de la Santé et de la Recherche Médicale (INSERM)-Inria Rennes – Bretagne Atlantique, Institut National de Recherche en Informatique et en Automatique (Inria)-Institut National de Recherche en Informatique et en Automatique (Inria)-SIGNAUX ET IMAGES NUMÉRIQUES, ROBOTIQUE (IRISA-D5), Institut de Recherche en Informatique et Systèmes Aléatoires (IRISA), Université de Bretagne Sud (UBS)-Institut National des Sciences Appliquées - Rennes (INSA Rennes), Institut National des Sciences Appliquées (INSA)-Université de Rennes (UNIV-RENNES)-Institut National des Sciences Appliquées (INSA)-Université de Rennes (UNIV-RENNES)-Institut National de Recherche en Informatique et en Automatique (Inria)-École normale supérieure - Rennes (ENS Rennes)-Centre National de la Recherche Scientifique (CNRS)-Université de Rennes 1 (UR1), Université de Rennes (UNIV-RENNES)-CentraleSupélec-IMT Atlantique Bretagne-Pays de la Loire (IMT Atlantique), Institut Mines-Télécom [Paris] (IMT)-Institut Mines-Télécom [Paris] (IMT)-Université de Bretagne Sud (UBS)-Institut National des Sciences Appliquées - Rennes (INSA Rennes), Institut Mines-Télécom [Paris] (IMT)-Institut Mines-Télécom [Paris] (IMT)-Institut de Recherche en Informatique et Systèmes Aléatoires (IRISA), Institut National des Sciences Appliquées (INSA)-Université de Rennes (UNIV-RENNES)-Institut National des Sciences Appliquées (INSA)-Université de Rennes (UNIV-RENNES)-École normale supérieure - Rennes (ENS Rennes)-Centre National de la Recherche Scientifique (CNRS)-Université de Rennes 1 (UR1), Institut Mines-Télécom [Paris] (IMT)-Institut Mines-Télécom [Paris] (IMT), Nuffield Department of Medicine [Oxford, UK] (Big Data Institute), University of Oxford [Oxford], Montreal Neurological Institute and Hospital, McGill University = Université McGill [Montréal, Canada], SIGNAUX ET IMAGES NUMÉRIQUES, ROBOTIQUE (IRISA-D5), Université de Rennes 1 (UR1), Université de Rennes (UNIV-RENNES)-Université de Rennes (UNIV-RENNES)-Institut National des Sciences Appliquées - Rennes (INSA Rennes), Institut National des Sciences Appliquées (INSA)-Université de Rennes (UNIV-RENNES)-Institut National des Sciences Appliquées (INSA)-Université de Bretagne Sud (UBS)-École normale supérieure - Rennes (ENS Rennes)-Institut National de Recherche en Informatique et en Automatique (Inria)-CentraleSupélec-Centre National de la Recherche Scientifique (CNRS)-IMT Atlantique Bretagne-Pays de la Loire (IMT Atlantique), Institut Mines-Télécom [Paris] (IMT)-Institut Mines-Télécom [Paris] (IMT)-Université de Rennes 1 (UR1), Institut Mines-Télécom [Paris] (IMT)-Institut Mines-Télécom [Paris] (IMT)-Inria Rennes – Bretagne Atlantique, Institut National de Recherche en Informatique et en Automatique (Inria)-Institut National de la Santé et de la Recherche Médicale (INSERM), University of California [Irvine] (UC Irvine), University of California (UC)-University of California (UC), Université de Rennes (UR)-Institut National des Sciences Appliquées - Rennes (INSA Rennes), Institut National des Sciences Appliquées (INSA)-Institut National des Sciences Appliquées (INSA)-Université de Bretagne Sud (UBS)-École normale supérieure - Rennes (ENS Rennes)-Institut National de Recherche en Informatique et en Automatique (Inria)-CentraleSupélec-Centre National de la Recherche Scientifique (CNRS)-IMT Atlantique (IMT Atlantique), Institut Mines-Télécom [Paris] (IMT)-Institut Mines-Télécom [Paris] (IMT)-Université de Rennes (UR)-Institut National des Sciences Appliquées - Rennes (INSA Rennes), Institut National des Sciences Appliquées (INSA)-Institut National des Sciences Appliquées (INSA)-Université de Bretagne Sud (UBS)-École normale supérieure - Rennes (ENS Rennes)-CentraleSupélec-Centre National de la Recherche Scientifique (CNRS)-IMT Atlantique (IMT Atlantique), University of Oxford, and Maumet, Camille

Subjects
[SDV.NEU]Life Sciences [q-bio]/Neurons and Cognition [q-bio.NC], [SDV.NEU] Life Sciences [q-bio]/Neurons and Cognition [q-bio.NC]
Abstract

International audience; The successful reuse of shared data relies on the existence of easily-available well-described metadata [1]. The metadata, as a rich description of the data, must capture information on how the data was acquired, processed and analyzed. The terms used to describe the data should be chosen with a logical, consistent framework in mind and include definitions to avoid ambiguity. In addition, a lexicon or ontology should reuse terms from existing efforts as much as possible [2].

Published
2018

23. Constructing an Ontology for Neuroimaging Experiments (NIDM-Experiment)

Author

Karl Helmer, Tibor Auer, Satrajit Ghosh, David Keator, Camille Maumet, Nolan Nichols, B., Nichols, Thomas E., Jessica Turner, Jean Baptiste Poline, Massachusetts General Hospital [Boston], Royal Holloway [University of London] (RHUL), Massachusetts Institute of Technology (MIT), University of California [Irvine] (UCI), University of California, Warwick Manufacturing Group [Coventry] (WMG), University of Warwick [Coventry], Silsoe Research Institute (SRI), Biotechnology and Biological Sciences Research Council, Department of Statistics [Warwick], Georgia State University, University System of Georgia (USG), Lawrence Berkeley National Laboratory [Berkeley] (LBNL), Maumet, Camille, University of California [Irvine] (UC Irvine), University of California (UC), and Biotechnology and Biological Sciences Research Council (BBSRC)

Subjects
[SDV.NEU]Life Sciences [q-bio]/Neurons and Cognition [q-bio.NC], [SDV.NEU] Life Sciences [q-bio]/Neurons and Cognition [q-bio.NC], ComputingMilieux_MISCELLANEOUS
Abstract

International audience

Published
2017

24. Simulated Annealing Approach onto VLSI Circuit Partitioning

Author

Sayantan Ghatak, Arijit Bhattacharya, Rajib K. Das, and Satrajit Ghosh

Subjects
Very-large-scale integration, Computer science, business.industry, Parallel computing, Modular design, Reduction (complexity), Simulated annealing, Hardware_INTEGRATEDCIRCUITS, Benchmark (computing), Heuristics, business, Algorithm, Metaheuristic, Electronic circuit
Abstract

Decompositions of inter-connected components, to achieve modular independence, poses the major problem in VLSI circuit partitioning. This problem is intractable in nature, Solutions of these problem in computational science is possible through appropriate heuristics. Reduction of cost that occurs due to interconnectivity between several VLSI components is referred in this paper. Modification of results derived by classical iterative procedures with probabilistic methods is attempted. Verification has been done on ISCAS-85 benchmark circuits. The proposed design tool shows remarkable improvement result in comparison to the traditional one, when applied to the standard benchmark circuits like ISCAS-85.

Published
2014
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25. Toxic & Hegemonic Masculinity, Gender Stereotypes & Resonance: The Problematic Snickers’ 'Get Some Nuts' Ad

Author

Craig Davis, Satrajit Ghosh Chowdhury, and Victoria LaPoe

Subjects
Hegemony, media_common.quotation_subject, Human sexuality, Gender studies, Ethical standards, Framing (social sciences), Advertising campaign, Ethical concerns, General Earth and Planetary Sciences, Sociology, Ideology, Hegemonic masculinity, General Environmental Science, media_common
Abstract

This paper discusses the ethical implications of the Snickers’ ad campaign, “Get Some Nuts”. Through priming, framing, race, gender and sexuality stereotypes, researchers explore cultural resonance and hegemonic ideologies within this ad. In this study, close to 200 students examined separate international ethical standards toward this 30-second commercial starring Mr. T. Student comments collected indicated that implicit racist, sexist and homophobic lenses resonated and caused ethical concerns.

Published
2019
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26. The Healthy Brain Network Serial Scanning Initiative, Session 1 v2

Author

David O'Connor, not provided Natan Vega Potler, not provided Meagan Kovacs, Ting Xu, not provided Lei Ai, not provided John Pellman, not provided Tamara Vanderwal, not provided Lucas Parra, not provided Samantha Cohen, not provided Satrajit Ghosh, not provided Jasmine Escalera, not provided Natalie Grant-Villegas, not provided Yael Osman, not provided Anastasia Bui, not provided R Cameron Craddock, and not provided Michael P Milham

Subjects
Brain network, medicine.medical_specialty, Physical medicine and rehabilitation, Serial scanning, business.industry, medicine, Session (computer science), business
Abstract

This protocol describes MRI and ADHD Quotient Test for Session 1 of the following work: David O'Connor, et. al. (2017) The Healthy Brain Network Serial Scanning Initiative. GigaScience...

Published
2017
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27. The Healthy Brain Network Serial Scanning Initiative, Sessions 2-7 and 9-14 v2

Author

David O'Connor, not provided Natan Vega Potler, not provided Meagan Kovacs, Ting Xu, not provided Lei Ai, not provided John Pellman, not provided Tamara Vanderwal, not provided Lucas Parra, not provided Samantha Cohen, not provided Satrajit Ghosh, not provided Jasmine Escalera, not provided Natalie Grant-Villegas, not provided Yael Osman, not provided Anastasia Bui, not provided R Cameron Craddock, and not provided Michael P Milham

Subjects
Brain network, medicine.medical_specialty, Physical medicine and rehabilitation, Serial scanning, business.industry, medicine, business
Abstract

This protocol describes MRI, voice recoding, and some basic physiological measurements and questionnaires for Sessions 2-7 and 9-14 of the following work: David O'Connor, et. al. (2017) The Healthy Brain Network Serial Scanning Initiative. GigaScience...

Published
2017
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28. The Healthy Brain Network Serial Scanning Initiative, Session 1 v1

Author

David O'Connor, not provided Natan Vega Potler, not provided Meagan Kovacs, Ting Xu, not provided Lei Ai, not provided John Pellman, not provided Tamara Vanderwal, not provided Lucas Parra, not provided Samantha Cohen, not provided Satrajit Ghosh, not provided Jasmine Escalera, not provided Natalie Grant-Villegas, not provided Yael Osman, not provided Anastasia Bui, not provided R Cameron Craddock, and not provided Michael P Milham

Abstract

This protocol describes MRI and ADHD Quotient Test for Session 1 of the following work: David O'Connor, et. al. (2017) The Healthy Brain Network Serial Scanning Initiative. GigaScience...

Published
2017
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29. The Healthy Brain Network Serial Scanning Initiative, Session 8 v1

Author

David O'Connor, not provided Natan Vega Potler, not provided Meagan Kovacs, Ting Xu, not provided Lei Ai, not provided John Pellman, not provided Tamara Vanderwal, not provided Lucas Parra, not provided Samantha Cohen, not provided Satrajit Ghosh, not provided Jasmine Escalera, not provided Natalie Grant-Villegas, not provided Yael Osman, not provided Anastasia Bui, not provided R Cameron Craddock, and not provided Michael P Milham

Abstract

This protocol describes MRI and ADHD Quotient Test for Session 8 of the following work: David O'Connor, et. al. (2017) The Healthy Brain Network Serial Scanning Initiative. GigaScience...

Published
2017
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30. Protocols for The Healthy Brain Network Serial Scanning Initiative (Sessions 1-14) v1

Author

David O'Connor, not provided Natan Vega Potler, not provided Meagan Kovacs, Ting Xu, not provided Lei Ai, not provided John Pellman, not provided Tamara Vanderwal, not provided Lucas Parra, not provided Samantha Cohen, not provided Satrajit Ghosh, not provided Jasmine Escalera, not provided Natalie Grant-Villegas, not provided Yael Osman, not provided Anastasia Bui, not provided R Cameron Craddock, and not provided Michael P Milham

Subjects
Brain network, medicine.medical_specialty, Physical medicine and rehabilitation, Serial scanning, business.industry, Medicine, business
Abstract

These protocols accompany the following publication: David O'Connor, et. al. (2017) The Healthy Brain Network Serial Scanning Initiative. GigaScience...

Published
2017
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31. TinyOLE - Efficient Actively Secure Two-Party Computation from Oblivious Linear Function Evaluation

Author

Roberto Trifiletti, Nico Döttling, Satrajit Ghosh, Tobias Nilges, and Jesper Buus Nielsen

Subjects
Theoretical computer science, Oblivious transfer, Computer science, Boolean circuit, Computation, Field (mathematics), 0102 computer and information sciences, 02 engineering and technology, Function (mathematics), 01 natural sciences, Finite field, 010201 computation theory & mathematics, Secure two-party computation, 0202 electrical engineering, electronic engineering, information engineering, 020201 artificial intelligence & image processing, Multiplication, Communication complexity
Abstract

We introduce a new approach to actively secure two-party computation based on so-called oblivious linear function evaluation (OLE), a natural generalisation of oblivious transfer (OT) and a special case of the notion of oblivious polynomial evaluation introduced by Naor and Pinkas at STOC 1999. OLE works over a finite field F. In an OLE the sender inputs two field elements a ƒ F and b ƒ F, and the receiver inputs a field element x ∈ F and learns only ƒx) = ax + b. Our protocol can evaluate an arithmetic circuit over a finite field F given black-box access to OLE for F. The protocol is unconditionally secure and consumes only a constant number of OLEs per multiplication gate. An OLE over a field F of size O(2κ) be implemented with communication O(κ). This gives a protocol with communication complexity O(C κ) for large enough fields, where C is an arithmetic circuit computing the desired function. This asymptotically matches the best previous protocols, but our protocol at the same time obtains significantly smaller constants hidden by the big-O notation, yielding a highly practical protocol. Conceptually our techniques lift the techniques for basing practical actively secure 2PC of Boolean circuits on OT introduced under the name TinyOT by Nielsen, Nordholt, Orlandi and Burra at Crypto 2012 to the arithmetic setting. In doing so we develop several novel techniques for generating various flavours of OLE and combining these. We believe that the efficiency of our protocols, both in asymptotic and practical terms, establishes OLE and its variants as an important foundation for efficient actively secure 2PC.

Published
2017
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32. Maliciously Secure Oblivious Linear Function Evaluation with Constant Overhead

Author

Satrajit Ghosh, Tobias Nilges, and Jesper Buus Nielsen

Subjects
Linear function (calculus), Polynomial, Theoretical computer science, SIMPLE (military communications protocol), Computer science, 020206 networking & telecommunications, 0102 computer and information sciences, 02 engineering and technology, computer.software_genre, 01 natural sciences, Constant (computer programming), 010201 computation theory & mathematics, 0202 electrical engineering, electronic engineering, information engineering, Overhead (computing), Compiler, Special case, Protocol (object-oriented programming), computer
Abstract

In this work we consider the problem of oblivious linear function evaluation (OLE). OLE is a special case of oblivious polynomial evaluation (OPE) and deals with the oblivious evaluation of a linear function \(f(x)=ax+b\). This problem is non-trivial in the sense that the sender chooses a, b and the receiver x, but the receiver may only learn f(x). We present a highly efficient and UC-secure construction of OLE in the OT-hybrid model that requires only O(1) OTs per OLE. The construction is based on noisy encodings introduced by Naor and Pinkas (STOC’99) and used for passive secure OLEs by Ishai, Prabhakaran and Sahai (TCC’09). A result asymptotically similar to ours is known by applying the IPS compiler to the mentioned passive secure OLE protocol, but our protocol provides better constants and would be considerably simpler to implement. Concretely we use only 16 OTs to generate one active secure OLE, and our protocol achieves active security by adding fairly simple checks to the passive secure protocol. We therefore believe our protocol takes an important step towards basing practical active-secure arithmetic computations on OLEs. Our result requires novel techniques that might be of independent interest. As an application we present the currently most efficient OPE construction.

Published
2017
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33. Extending NI-DM to share the results and provenance of a neuroimaging study: an example with SPM

Author

Camille Maumet, Nichols, Thomas E., Nolan Nichols, B., Guillaume Flandin, Jessica Turner, Karl Helmer, Jason Steffener, Jean-Baptiste Poline, Satrajit Ghosh, David Keator, Maumet, Camille, University of Warwick [Coventry], University of Washington [Seattle], University College of London [London] (UCL), Georgia State University, University System of Georgia (USG), Massachusetts General Hospital [Boston], Columbia University [New York], Lawrence Berkeley National Laboratory [Berkeley] (LBNL), Massachusetts Institute of Technology (MIT), University of California [Irvine] (UC Irvine), University of California (UC), University of California [Irvine] (UCI), and University of California

Subjects
[SDV.NEU]Life Sciences [q-bio]/Neurons and Cognition [q-bio.NC], [SDV.NEU] Life Sciences [q-bio]/Neurons and Cognition [q-bio.NC], ComputingMilieux_MISCELLANEOUS
Abstract

Poster submitted to the 2014 Organization for Human Brain Mapping (OHBM) conference in Hamburg, 8-12 June.

Published
2017
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34. NIDM-Results: Standardized reporting of mass univariate neuroimaging results in SPM, FSL and AFNI

Author

Maumet, Camille, B. Nolan Nichols, Flandin, Guillaume, Helmer, Karl, Auer, Tibor, Bowring, Alexander, Sochat, Vanessa, Das, Samir, Glatard, Tristan, Reynolds, Richard, Cox, Robert W., Chen, Gang, Jenkinson, Mark, Webster, Matthew A., Steffener, Jason, Gorgolewski, Krzysztof J., Turner, Jessica, Nichols, Thomas, Satrajit Ghosh, Jean-Baptiste Poline, Keator, David, Warwick Manufacturing Group [Coventry] (WMG), University of Warwick [Coventry], SRI International [Menlo Park] (SRI), Wellcome Trust Centre for Neuroimaging, University College of London [London] (UCL), Massachusetts General Hospital [Boston], Cognition and Brain Sciences Unit (MRC CBU), University of Cambridge [UK] (CAM), Stanford University, Montreal Neurological Institute and Hospital, McGill University = Université McGill [Montréal, Canada], Centre de Recherche en Acquisition et Traitement de l'Image pour la Santé (CREATIS), Université Jean Monnet [Saint-Étienne] (UJM)-Hospices Civils de Lyon (HCL)-Institut National des Sciences Appliquées de Lyon (INSA Lyon), Université de Lyon-Institut National des Sciences Appliquées (INSA)-Université de Lyon-Institut National des Sciences Appliquées (INSA)-Université Claude Bernard Lyon 1 (UCBL), Université de Lyon-Centre National de la Recherche Scientifique (CNRS)-Institut National de la Santé et de la Recherche Médicale (INSERM), Images et Modèles, Université de Lyon-Centre National de la Recherche Scientifique (CNRS)-Institut National de la Santé et de la Recherche Médicale (INSERM)-Université Jean Monnet [Saint-Étienne] (UJM)-Hospices Civils de Lyon (HCL)-Institut National des Sciences Appliquées de Lyon (INSA Lyon), National Institutes of Health [Bethesda] (NIH), University of Oxford [Oxford], Columbia University [New York], Georgia State University, University System of Georgia (USG), Department of Statistics [Warwick], Massachusetts Institute of Technology (MIT), Henry H. Wheeler Jr. Brain Imaging Center, University of California at Berkeley, University of California [Irvine] (UCI), University of California, Warwick Manufacturing Group [Coventry] ( WMG ), SRI International [Menlo Park] ( SRI ), University College of London [London] ( UCL ), Massachusetts General Hospital, Cognition and Brain Sciences Unit ( MRC CBU ), Medical Research Council, Stanford University [Stanford], Montreal Neurological Institute [Montr??al], McGill University, Centre de Recherche en Acquisition et Traitement de l'Image pour la Sant?? ( CREATIS ), Universit?? Claude Bernard Lyon 1 ( UCBL ), Universit?? de Lyon-Universit?? de Lyon-Institut National des Sciences Appliqu??es de Lyon ( INSA Lyon ), Universit?? de Lyon-Institut National des Sciences Appliqu??es ( INSA ) -Institut National des Sciences Appliqu??es ( INSA ) -Hospices Civils de Lyon ( HCL ) -Universit?? Jean Monnet [Saint-??tienne] ( UJM ) -Institut National de la Sant?? et de la Recherche M??dicale ( INSERM ) -Centre National de la Recherche Scientifique ( CNRS ), National Institutes of Health [Bethesda] ( NIH ), Massachusetts Institute of Technology ( MIT ), University of California [Berkeley], University of California [Irvine] ( UCI ), Université Claude Bernard Lyon 1 (UCBL), Université de Lyon-Université de Lyon-Institut National des Sciences Appliquées de Lyon (INSA Lyon), Université de Lyon-Institut National des Sciences Appliquées (INSA)-Institut National des Sciences Appliquées (INSA)-Hospices Civils de Lyon (HCL)-Université Jean Monnet - Saint-Étienne (UJM)-Institut National de la Santé et de la Recherche Médicale (INSERM)-Centre National de la Recherche Scientifique (CNRS), Université de Lyon-Institut National des Sciences Appliquées (INSA)-Institut National des Sciences Appliquées (INSA)-Hospices Civils de Lyon (HCL)-Université Jean Monnet - Saint-Étienne (UJM)-Institut National de la Santé et de la Recherche Médicale (INSERM)-Centre National de la Recherche Scientifique (CNRS)-Université Claude Bernard Lyon 1 (UCBL), University of Oxford, University of California [Berkeley] (UC Berkeley), University of California (UC), University of California [Irvine] (UC Irvine), Maumet, Camille, and Institut National des Sciences Appliquées (INSA)-Université de Lyon-Institut National des Sciences Appliquées (INSA)-Université de Lyon-Université Claude Bernard Lyon 1 (UCBL)

Subjects
[ SDV.NEU ] Life Sciences [q-bio]/Neurons and Cognition [q-bio.NC], [SDV.NEU]Life Sciences [q-bio]/Neurons and Cognition [q-bio.NC], [SDV.NEU] Life Sciences [q-bio]/Neurons and Cognition [q-bio.NC]
Abstract

International audience; Results of a neuroimaging study are usually shared through the publication of a scientific paper describing the experiment and analysis outcome. While hundreds of gigabytes of data are usually generated as part of an fMRI experiment, in the literature the authors typically report their results as a list of significant local maxima, i.e. locations in the brain defined in a standard space (e.g. MNI) that pass rigorous statistical testing. This practice is unsatisfactory in terms of data re-use as it does not allow for the automatic extraction of acquisition or processing information and it provides only sparse information about the location of the brain activity. While databases have been built to provide manually curated (such as BrainMap ​ [1]​) or automatically-extracted (e.g. NeuroSynth ​ [2]​) meta-data associated with published papers, ideally, these meta-data should be made available by the authors themselves at the time of the publication.

Published
2016

35. The Brain Imaging Data Structure: a format for organizing and describing neuroimaging data

Author

Krzysztof Gorgolewski, Tibor Auer, Calhoun, Vince D., Cameron Craddock, Samir Das, Eugene Duff, Guillaume Flandin, Satrajit Ghosh, Tristan Glatard, Yaroslav Halchenko, Daniel Handwerker, Michael Hanke, David Keator, Xiangrui Li, Zachary Michael, Camille Maumet, Nolan Nichols, B., Nichols, Thomas E., Jean-Baptiste Poline, Ariel Rokem, Gunnar Schaefer, Vanessa Sochat, Jessica Turner, Gaël Varoquaux, Russell Poldrack, Maumet, Camille, Stanford University, Cognition and Brain Sciences Unit (MRC CBU), University of Cambridge [UK] (CAM), Department of Electrical and Computer Engineering [Albuquerque] (ECE Department), The University of New Mexico [Albuquerque], The Mind Research Network, Child Mind Institute, Montreal Neurological Institute and Hospital, McGill University = Université McGill [Montréal, Canada], Oxford Centre for Functional Magnetic Resonance Imaging of the Brain (FMRIB), University of Oxford, Wellcome Trust Centre for Neuroimaging, University College of London [London] (UCL), Massachusetts Institute of Technology (MIT), Images et Modèles, Centre de Recherche en Acquisition et Traitement de l'Image pour la Santé (CREATIS), Université Claude Bernard Lyon 1 (UCBL), Université de Lyon-Université de Lyon-Institut National des Sciences Appliquées de Lyon (INSA Lyon), Université de Lyon-Institut National des Sciences Appliquées (INSA)-Institut National des Sciences Appliquées (INSA)-Hospices Civils de Lyon (HCL)-Université Jean Monnet - Saint-Étienne (UJM)-Institut National de la Santé et de la Recherche Médicale (INSERM)-Centre National de la Recherche Scientifique (CNRS)-Université Claude Bernard Lyon 1 (UCBL), Université de Lyon-Institut National des Sciences Appliquées (INSA)-Institut National des Sciences Appliquées (INSA)-Hospices Civils de Lyon (HCL)-Université Jean Monnet - Saint-Étienne (UJM)-Institut National de la Santé et de la Recherche Médicale (INSERM)-Centre National de la Recherche Scientifique (CNRS), Dartmouth College [Hanover], National Institute of Mental Health (NIMH), Otto-von-Guericke-Universität Magdeburg = Otto-von-Guericke University [Magdeburg] (OVGU), University of California [Irvine] (UC Irvine), University of California (UC), Ohio State University [Columbus] (OSU), Squishymedia [Portland], Warwick Manufacturing Group [Coventry] (WMG), University of Warwick [Coventry], University of Washington [Seattle], Department of Statistics [Warwick], University of California [Berkeley] (UC Berkeley), Georgia State University, University System of Georgia (USG), Service NEUROSPIN (NEUROSPIN), Université Paris-Saclay-Direction de Recherche Fondamentale (CEA) (DRF (CEA)), Commissariat à l'énergie atomique et aux énergies alternatives (CEA)-Commissariat à l'énergie atomique et aux énergies alternatives (CEA), Modelling brain structure, function and variability based on high-field MRI data (PARIETAL), Commissariat à l'énergie atomique et aux énergies alternatives (CEA)-Commissariat à l'énergie atomique et aux énergies alternatives (CEA)-Université Paris-Saclay-Direction de Recherche Fondamentale (CEA) (DRF (CEA)), Commissariat à l'énergie atomique et aux énergies alternatives (CEA)-Commissariat à l'énergie atomique et aux énergies alternatives (CEA)-Inria Saclay - Ile de France, Institut National de Recherche en Informatique et en Automatique (Inria)-Institut National de Recherche en Informatique et en Automatique (Inria), Université de Lyon-Institut National des Sciences Appliquées (INSA)-Institut National des Sciences Appliquées (INSA)-Université Jean Monnet - Saint-Étienne (UJM)-Institut National de la Santé et de la Recherche Médicale (INSERM)-Centre National de la Recherche Scientifique (CNRS)-Université Claude Bernard Lyon 1 (UCBL), Université de Lyon-Institut National des Sciences Appliquées (INSA)-Institut National des Sciences Appliquées (INSA)-Université Jean Monnet - Saint-Étienne (UJM)-Institut National de la Santé et de la Recherche Médicale (INSERM)-Centre National de la Recherche Scientifique (CNRS), University of Oxford [Oxford], Université Jean Monnet [Saint-Étienne] (UJM)-Hospices Civils de Lyon (HCL)-Institut National des Sciences Appliquées de Lyon (INSA Lyon), Institut National des Sciences Appliquées (INSA)-Université de Lyon-Institut National des Sciences Appliquées (INSA)-Université de Lyon-Université Claude Bernard Lyon 1 (UCBL), Université de Lyon-Centre National de la Recherche Scientifique (CNRS)-Institut National de la Santé et de la Recherche Médicale (INSERM)-Université Jean Monnet [Saint-Étienne] (UJM)-Hospices Civils de Lyon (HCL)-Institut National des Sciences Appliquées de Lyon (INSA Lyon), Université de Lyon-Centre National de la Recherche Scientifique (CNRS)-Institut National de la Santé et de la Recherche Médicale (INSERM), Otto-von-Guericke University [Magdeburg] (OVGU), University of California [Irvine] (UCI), University of California, University of California [Berkeley], Direction de Recherche Fondamentale (CEA) (DRF (CEA)), Commissariat à l'énergie atomique et aux énergies alternatives (CEA)-Commissariat à l'énergie atomique et aux énergies alternatives (CEA)-Université Paris-Saclay, Inria Saclay - Ile de France, and Institut National de Recherche en Informatique et en Automatique (Inria)-Institut National de Recherche en Informatique et en Automatique (Inria)-Service NEUROSPIN (NEUROSPIN)

Subjects
[SDV.NEU]Life Sciences [q-bio]/Neurons and Cognition [q-bio.NC], [SDV.NEU] Life Sciences [q-bio]/Neurons and Cognition [q-bio.NC], ComputingMilieux_MISCELLANEOUS
Abstract

International audience

Published
2016

36. NeuroVault.org: new features and a proof of concept analysis

Author

Krzysztof Gorgolewski, Gaël Varoquaux, Gabriel Rivera, Joseph Wexler, Yannick Schwartz, Satrajit Ghosh, Camille Maumet, Nichols, Thomas E., Jean Baptiste Poline, Tal Yarkoni, Daniel Margulies, Russell Poldrack, Stanford University, Service NEUROSPIN (NEUROSPIN), Université Paris-Saclay-Direction de Recherche Fondamentale (CEA) (DRF (CEA)), Commissariat à l'énergie atomique et aux énergies alternatives (CEA)-Commissariat à l'énergie atomique et aux énergies alternatives (CEA), Modelling brain structure, function and variability based on high-field MRI data (PARIETAL), Commissariat à l'énergie atomique et aux énergies alternatives (CEA)-Commissariat à l'énergie atomique et aux énergies alternatives (CEA)-Université Paris-Saclay-Direction de Recherche Fondamentale (CEA) (DRF (CEA)), Commissariat à l'énergie atomique et aux énergies alternatives (CEA)-Commissariat à l'énergie atomique et aux énergies alternatives (CEA)-Inria Saclay - Ile de France, Institut National de Recherche en Informatique et en Automatique (Inria)-Institut National de Recherche en Informatique et en Automatique (Inria), InfoCortex UG [Frankfurt], Massachusetts Institute of Technology (MIT), Warwick Manufacturing Group [Coventry] (WMG), University of Warwick [Coventry], Department of Statistics [Warwick], Lawrence Berkeley National Laboratory [Berkeley] (LBNL), Department of Psychology [Austin] (University of Texas), University of Texas at Austin [Austin], Max Planck Institute for Human Cognitive and Brain Sciences [Leipzig] (IMPNSC), Max-Planck-Gesellschaft, Maumet, Camille, Direction de Recherche Fondamentale (CEA) (DRF (CEA)), Commissariat à l'énergie atomique et aux énergies alternatives (CEA)-Commissariat à l'énergie atomique et aux énergies alternatives (CEA)-Université Paris-Saclay, Inria Saclay - Ile de France, and Institut National de Recherche en Informatique et en Automatique (Inria)-Institut National de Recherche en Informatique et en Automatique (Inria)-Service NEUROSPIN (NEUROSPIN)

Subjects
[SDV.NEU]Life Sciences [q-bio]/Neurons and Cognition [q-bio.NC], [SDV.NEU] Life Sciences [q-bio]/Neurons and Cognition [q-bio.NC], ComputingMilieux_MISCELLANEOUS
Abstract

International audience

Published
2015

37. How to make brain imaging research efficient and reproducible: building software and standards

Author

Jean-Baptiste Poline, Samir Das, David Keator, Krysztof Gorgolewski, Tibor Auer, Gang Chen, Cameron Craddock, Guillaume Flandin, Satrajit Ghosh, Yaroslav Halchenko, Michael Hanke, Christian Haselgrove, Karl Helmer, Mark Jenkinson, Arno Klein, Daniel Marcus, Daniel Margulies, Camille Maumet, Franck Michel, Nolan Nichols, B., Nichols, Thomas E., Russell Poldrack, Richard Reynolds, Ziad Saad, Tanya Schmah, Jason Steffener, Jessica Turner, Theo van Erp, John van Horn, David Kennedy, Henry H. Wheeler Jr. Brain Imaging Center, University of California at Berkeley, Montreal Neurological Institute and Hospital, McGill University = Université McGill [Montréal, Canada], University of California [Irvine] (UCI), University of California, Stanford University, Cognition and Brain Sciences Unit (MRC CBU), University of Cambridge [UK] (CAM), National Institutes of Health [Bethesda] (NIH), Child Mind Institute, Wellcome Trust Centre for Neuroimaging, University College of London [London] (UCL), Massachusetts Institute of Technology (MIT), Psychology and Brain Sciences, Dartmouth College [Hanover], Otto-von-Guericke University [Magdeburg] (OVGU), University of Massachusetts [Amherst] (UMass Amherst), University of Massachusetts System (UMASS), Massachusetts General Hospital [Boston], University of Oxford [Oxford], New York State Psychiatric Institute, Columbia University [New York], Department of Radiology, Washington University School of Medicine, Washington University School of Medicine, Centre de Recherche en Éthique [UdeM - Montréal] (CREUM), Université de Montréal (UdeM), Warwick Manufacturing Group [Coventry] (WMG), University of Warwick [Coventry], COMUE Université Côte d'Azur (2015-2019) (COMUE UCA), Scalable and Pervasive softwARe and Knowledge Systems (Laboratoire I3S - SPARKS), Laboratoire d'Informatique, Signaux, et Systèmes de Sophia Antipolis (I3S), Université Nice Sophia Antipolis (... - 2019) (UNS), COMUE Université Côte d'Azur (2015-2019) (COMUE UCA)-COMUE Université Côte d'Azur (2015-2019) (COMUE UCA)-Centre National de la Recherche Scientifique (CNRS)-Université Côte d'Azur (UCA)-Université Nice Sophia Antipolis (... - 2019) (UNS), COMUE Université Côte d'Azur (2015-2019) (COMUE UCA)-COMUE Université Côte d'Azur (2015-2019) (COMUE UCA)-Centre National de la Recherche Scientifique (CNRS)-Université Côte d'Azur (UCA), University of Washington [Seattle], Department of Statistics [Warwick], California Institute of Technology (CALTECH), Department of Computer Science, University of Toronto, Georgia State University, University System of Georgia (USG), Department of Psychiatry and Human Behavior [Irvine], University of California-University of California, United Kingdom Met Office [Exeter], University of Massachusetts Medical School [Worcester] (UMASS), University of California [Berkeley] (UC Berkeley), University of California (UC), University of California [Irvine] (UC Irvine), Otto-von-Guericke-Universität Magdeburg = Otto-von-Guericke University [Magdeburg] (OVGU), University of Oxford, Université Nice Sophia Antipolis (1965 - 2019) (UNS), COMUE Université Côte d'Azur (2015-2019) (COMUE UCA)-COMUE Université Côte d'Azur (2015-2019) (COMUE UCA)-Centre National de la Recherche Scientifique (CNRS)-Université Côte d'Azur (UCA)-Université Nice Sophia Antipolis (1965 - 2019) (UNS), University of California (UC)-University of California (UC), and Maumet, Camille

Subjects
[SDV.NEU]Life Sciences [q-bio]/Neurons and Cognition [q-bio.NC], [SDV.NEU] Life Sciences [q-bio]/Neurons and Cognition [q-bio.NC], ComputingMilieux_MISCELLANEOUS
Abstract

International audience

Published
2015

38. Standardized reporting of neuroimaging results with NIDM in SPM, FSL and AFNI

Author

Camille Maumet, Nolan Nichols, Guillaume Flandin, Karl Helmer, Tibor Auer, Richard Reynolds, Ziad Saad, Gang Chen, Mark Jenkinson, Matthew Webster, Jason Steffener, Krzysztof Gorgolewski, Jessica Turner, Thomas Nichols, Satrajit Ghosh, Jean-Baptiste Poline, David Keator, Maumet, Camille, Warwick Manufacturing Group [Coventry] (WMG), University of Warwick [Coventry], University of Washington [Seattle], Airbus Group Innovations [Suresnes], Airbus [France], Massachusetts General Hospital [Boston], Cognition and Brain Sciences Unit (MRC CBU), University of Cambridge [UK] (CAM), National Institutes of Health [Bethesda] (NIH), Department of Clinical Neurology [Oxford], University of Oxford-FMRIB Centre- John Radcliffe Hospital [Oxford University Hospital], University of Oxford, Columbia University [New York], Department of Psychology, Stanford University, Georgia State University, University System of Georgia (USG), Massachusetts Institute of Technology (MIT), The Helen Wills Neuroscience Institute (HWNI), University of California [Berkeley] (UC Berkeley), University of California (UC)-University of California (UC), University of California [Irvine] (UC Irvine), University of California (UC), University of Oxford [Oxford]-FMRIB Centre- John Radcliffe Hospital [Oxford University Hospital], University of Oxford [Oxford], University of California [Berkeley], University of California-University of California, University of California [Irvine] (UCI), University of California, Warwick Manufacturing Group [Coventry] ( WMG ), AIRBUS, Massachusetts General Hospital, Cognition and Brain Sciences Unit ( MRC CBU ), Medical Research Council, National Institutes of Health [Bethesda] ( NIH ), Stanford University [Stanford], Massachusetts Institute of Technology ( MIT ), The Helen Wills Neuroscience Institute ( HWNI ), and University of California [Irvine] ( UCI )

Subjects
[ SDV.NEU ] Life Sciences [q-bio]/Neurons and Cognition [q-bio.NC], [SDV.NEU]Life Sciences [q-bio]/Neurons and Cognition [q-bio.NC], [SDV.NEU] Life Sciences [q-bio]/Neurons and Cognition [q-bio.NC]
Abstract

International audience; The growing awareness of underpowered and irreproducible research [1] has highlighted the necessity for data sharing in neuroimaging. Encouragingly, in the past ten years, the number of publicly available datasets has greatly increased, principally thanks to consortiums dedicated to data sharing such as the Human Connectome Project [4], the International Data sharing Initiative [5] or more recently the Consortium for Reliability and Reproducibility [3] and study forrest [10]. But while there is an increasing interest in sharing raw or pre-processed data (e.g. after motion correction, registration to a template), statistical images supporting neuroimaging publications are rarely made available. Furthermore, despite the availability of guidelines [7], ambiguous or incomplete methodological reporting is still commonplace [2] making image-based meta-analysis and reproducibility studies particularly challenging. In [6], we introduced the Neuroimaging Data Model (NIDM), a domain-specific extension of the recently-approved W3C recommendation, PROV-DM [9]. Here, we introduce NIDM-Results, a standardised representation of “mass univariate” neuroimaging results for the three major software analysis packages: SPM, FSL, and AFNI.

Published
2015

39. Post-Quantum Forward-Secure Onion Routing

Author

Aniket Kate and Satrajit Ghosh

Subjects
business.industry, Computer science, ComputerSystemsOrganization_COMPUTER-COMMUNICATIONNETWORKS, Cryptography, 0102 computer and information sciences, Encryption, 01 natural sciences, Authenticated Key Exchange, 03 medical and health sciences, 0302 clinical medicine, 010201 computation theory & mathematics, 030220 oncology & carcinogenesis, Onion routing, business, Learning with errors, Key exchange, Computer network, Forward anonymity, Anonymity
Abstract

The onion routing (OR) network Tor provides anonymity to its users by routing their encrypted traffic through three proxies (or nodes). The key cryptographic challenge, here, is to establish symmetric session keys using a secure key exchange between the anonymous user and the selected nodes. The Tor network currently employs a one-way authenticated key exchange (1W-AKE) protocol \({\mathsf {ntor}}\) for this purpose. Nevertheless, \({\mathsf {ntor}}\) as well as other known 1W-AKE protocols rely solely on some classical Diffie-Hellman (DH) type assumptions for their (forward) security, and privacy of today’s anonymous communication cannot be ensured once quantum computers arrive.

Published
2015
Full Text
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40. Developing Terminologies for the INCF Neuroimaging Data Model (NIDM)

Author

Karl Helmer, Jessica Turner, Camille Maumet, Thomas Nichols, Nolan Nichols, David Keator, Satrajit Ghosh, Tibor Auer, Jean-Baptiste Poline, Maumet, Camille, Massachusetts General Hospital [Boston], Georgia State University, University System of Georgia (USG), Warwick Manufacturing Group [Coventry] (WMG), University of Warwick [Coventry], SRI International [Menlo Park] (SRI), Department of Psychiatry and Behavioral Sciences [Stanford], Stanford Medicine, Stanford University-Stanford University, University of California [Irvine] (UC Irvine), University of California (UC), Massachusetts Institute of Technology (MIT), Cognition and Brain Sciences Unit (MRC CBU), University of Cambridge [UK] (CAM), The Helen Wills Neuroscience Institute (HWNI), University of California [Berkeley] (UC Berkeley), University of California (UC)-University of California (UC), University of California [Irvine] (UCI), University of California, University of California [Berkeley], and University of California-University of California

Subjects
[SDV.NEU]Life Sciences [q-bio]/Neurons and Cognition [q-bio.NC], [SDV.NEU] Life Sciences [q-bio]/Neurons and Cognition [q-bio.NC], ComputingMilieux_MISCELLANEOUS
Abstract

International audience

Published
2015

41. Bijective mapping of arbitrary finite state machine

Author

Rajib K. Das, Satrajit Ghosh, and Biswanath Sen

Subjects
Finite-state machine, Sequential logic, Fredkin gate, Computer science, Computation, Binary number, Toffoli gate, law.invention, Computer Science::Hardware Architecture, Quantum circuit, Entropy (classical thermodynamics), Computer Science::Emerging Technologies, law, Logic gate, Hardware_INTEGRATEDCIRCUITS, Reversible computing, Algorithm, Hardware_LOGICDESIGN
Abstract

Reversible Computation is of interest, because it is associated with ultra low power computing. Design and analysis of a reversible sequential circuit is an interesting research problem. State of the art design decomposes a sequential circuit into smaller modules. Then each module is implemented using known reversible circuits like Toffoli Gate, Peres Gate, Fredkin Gate, Picton Gate, and Rice Gate. This approach is inherently intractable in nature. In this work a more efficient approach is proposed that performs a detail analysis of the State Diagram. Redundancy in a state diagram arises if a “next-state” is attained from more than one “present state”. Irredundant description of such a state diagram is possible, if each state is encoded with a binary number and some ancillary bits are used to distinguish the “identical states”. This is helpful in designing a complex system that involves complex interconnection of a large number of sequential circuits.

Published
2014
Full Text
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42. NeuroVault: a web repository for sharing statistical parametric maps

Author

Krzysztof Gorgolewski, Tal Yarkoni, Satrajit Ghosh, Russel Poldrack, Jean-Baptiste Poline, Yannick Schwartz, Thomas Nichols, Camille Maumet, Daniel Margulies, Max Planck Research Group Neuroanatomy and Connectivity, Max Planck Institute for Human Cognitive and Brain Sciences [Leipzig] (IMPNSC), Max-Planck-Gesellschaft-Max-Planck-Gesellschaft, Department of Psychology, University of Texas at Austin [Austin], Research Laboratory of Electronics, Massachusetts Institute of Technology (MIT), The Helen Wills Neuroscience Institute (HWNI), University of California [Berkeley], University of California-University of California, Modelling brain structure, function and variability based on high-field MRI data (PARIETAL), Inria Saclay - Ile de France, Institut National de Recherche en Informatique et en Automatique (Inria)-Institut National de Recherche en Informatique et en Automatique (Inria)-Service NEUROSPIN (NEUROSPIN), Université Paris-Saclay-Direction de Recherche Fondamentale (CEA) (DRF (CEA)), Commissariat à l'énergie atomique et aux énergies alternatives (CEA)-Commissariat à l'énergie atomique et aux énergies alternatives (CEA)-Université Paris-Saclay-Direction de Recherche Fondamentale (CEA) (DRF (CEA)), Commissariat à l'énergie atomique et aux énergies alternatives (CEA)-Commissariat à l'énergie atomique et aux énergies alternatives (CEA), Warwick Manufacturing Group [Coventry] (WMG), University of Warwick [Coventry], Maumet, Camille, University of California [Berkeley] (UC Berkeley), University of California (UC)-University of California (UC), Service NEUROSPIN (NEUROSPIN), Commissariat à l'énergie atomique et aux énergies alternatives (CEA)-Commissariat à l'énergie atomique et aux énergies alternatives (CEA)-Inria Saclay - Ile de France, and Institut National de Recherche en Informatique et en Automatique (Inria)-Institut National de Recherche en Informatique et en Automatique (Inria)

Subjects
Meta-analysis, Neuroimaging, Data sharing, [SDV.NEU]Life Sciences [q-bio]/Neurons and Cognition [q-bio.NC], [SDV.NEU] Life Sciences [q-bio]/Neurons and Cognition [q-bio.NC], ComputingMilieux_MISCELLANEOUS
Abstract

International audience; na

Published
2014

43. A novel approach for identification of symmetric switching function

Author

Satrajit Ghosh, Subhajit Guha, and Partha Pratim Das

Subjects
Symmetric function, Discrete mathematics, Identification (information), Computational complexity theory, Polarity (physics), Algorithm design, Function (mathematics), Information theory, Topology, Mathematics, Variable (mathematics)
Abstract

A new method for identification of symmetric switching functions is proposed. The proposed method exploits the Shannon's decomposition theory to reduce the space-time complexity in symmetric switching function identification. The method is applicable for literals as well. The set of true min-terms are bi-partitioned into two subsets depending upon the polarity of a particular variable. This reduces the search space for symmetry detection.

Published
2012
Full Text
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44. An Improvement of Linearization-Based Algebraic Attacks

Author

Satrajit Ghosh and Abhijit Das

Subjects
Monomial, business.industry, Computer science, Advanced Encryption Standard, Plaintext, Encryption, Cipher, Linearization, ComputingMethodologies_SYMBOLICANDALGEBRAICMANIPULATION, Computer Science::Multimedia, Ciphertext, business, Algorithm, Computer Science::Cryptography and Security, Block cipher
Abstract

In an algebraic attack on a cipher, one expresses the encryption function as a system (usually overdefined) of multivariate polynomial equations in the bits of the plaintext, the ciphertext and the key, and subsequently solves the system for the unknown key bits from the knowledge of one or more plaintext/ciphertext pairs. The standard eXtended Linearization algorithm (XL) expands the initial system of equations by monomial multiplications. The expanded system is treated as a linear system in the monomials. For most block ciphers (like the Advanced Encryption Standard (AES)), the size of the linearized system turns out to be very large, and consequently, the complexity to solve the system often exceeds the complexity of brute-force search. In this paper, we propose a heuristic strategy XL SGE to reduce the number of linearized equations. This reduction is achieved by applying structured Gaussian elimination before each stage of monomial multiplication. Experimentation on small random systems indicates that XL SGE has the potential to improve the performance of the XL algorithm in terms of the size of the final solvable system. This performance gain is exhibited by our heuristic also in the case of a toy version of AES.

Published
2011
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45. Algorithms for rotation symmetric Boolean functions

Author

Samar Sensarma, Parthasarathi Dasgupta, Satrajit Ghosh, and Subrata Das

Subjects
Combinatorics, Discrete mathematics, Information coding, business.industry, Partition (number theory), Cryptosystem, Cryptography, Invariant (mathematics), business, Boolean function, Algorithm, Mathematics
Abstract

Rotation Symmetric Boolean Functions (RSBFs) are of immense importance as building blocks of cryptosystems. This class of Boolean functions are invariant under circular translation of indices. It is known that, for n-variable RSBF functions, the associated set of all possible input n-bit strings can be divided into a number of subsets (called partitions or orbits), where every element (n-bit string) of a subset can be obtained by simply rotating the string of bits of some other element of the same subset. In this paper, for a given value of n, we propose algorithms for the generation of these partitions of all possible n-bit strings, each partition corresponding to a specific n-variable RSBF and its associated circular translations. These partitions can then be used to generate all possible n-variable RSBFs. The proposed algorithms are implemented for a maximum value of n = 41.

Published
2014
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46. The Addition of Neuroimaging Acquisition, Processing and Analysis Terms to Neurolex

Author

Karl Helmer, Satrajit Ghosh, Willy Wong, David Keator, Camille Maumet, Nichols, B., Nichols Thomas, Jean-Baptiste Poline, Jason Steffener, Jessica Turner, Maryann Martone, Massachusetts General Hospital [Boston], Massachusetts Institute of Technology (MIT), University of California [San Diego] (UC San Diego), University of California, University of California [Irvine] (UCI), University of Warwick [Coventry], University of Washington [Seattle], Lawrence Berkeley National Laboratory [Berkeley] (LBNL), Columbia University [New York], Georgia State University, University System of Georgia (USG), Maumet, Camille, University of California (UC), and University of California [Irvine] (UC Irvine)

Subjects
[SDV.NEU]Life Sciences [q-bio]/Neurons and Cognition [q-bio.NC], [SDV.NEU] Life Sciences [q-bio]/Neurons and Cognition [q-bio.NC], ComputingMilieux_MISCELLANEOUS
Abstract

International audience

47. An Algebraic Approach to Maliciously Secure Private Set Intersection

Author

Satrajit Ghosh and Tobias Nilges

Subjects
Focus (computing), Theoretical computer science, Intersection, 010201 computation theory & mathematics, Computer science, 0202 electrical engineering, electronic engineering, information engineering, 020201 artificial intelligence & image processing, 0102 computer and information sciences, 02 engineering and technology, Algebraic number, 16. Peace & justice, 01 natural sciences, Private set intersection
Abstract

Private set intersection (PSI) is an important area of research and has been the focus of many works over the past decades. It describes the problem of finding an intersection between the input sets of at least two parties without revealing anything about the input sets apart from their intersection.

Full Text
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48. Describing Experiment Metadata with the Neuroimaging Data Model (NIDM)

Author

David Keator, Karl Helmer, Satrajit Ghosh, Tibor Auer, Camille Maumet, Samir Das, Guillaume Flandin, Nichols, Thomas E., Krzysztof Gorgolewski, Jessica Turner, David Kennedy, Jean Baptiste Poline, Nolan Nichols, B., University of California [Irvine] (UCI), University of California, Massachusetts General Hospital [Boston], Massachusetts Institute of Technology (MIT), Royal Holloway [University of London] (RHUL), Warwick Manufacturing Group [Coventry] (WMG), University of Warwick [Coventry], Montreal Neurological Institute and Hospital, McGill University = Université McGill [Montréal, Canada], Wellcome Trust Centre for Neuroimaging, University College of London [London] (UCL), Department of Statistics [Warwick], Stanford University, Georgia State University, University System of Georgia (USG), University of Massachusetts Medical School [Worcester] (UMASS), University of Massachusetts System (UMASS), Henry H. Wheeler Jr. Brain Imaging Center, University of California at Berkeley, University of Washington [Seattle], Maumet, Camille, University of California [Irvine] (UC Irvine), University of California (UC), and University of California [Berkeley] (UC Berkeley)

Subjects
[SDV.NEU]Life Sciences [q-bio]/Neurons and Cognition [q-bio.NC], [SDV.NEU] Life Sciences [q-bio]/Neurons and Cognition [q-bio.NC], ComputingMilieux_MISCELLANEOUS
Abstract

International audience

49. Application of the Neuroimaging Data Model to Represent and Exchange Primary and Derived Data

Author

Nolan Nichols, David Keator, Satrajit Ghosh, Camille Maumet, Guillaume Flandin, Thomas Nichols, Krzysztof Gorgolewski, Halchenko, Y. O., Michael Hanke, Christian Haselgrove, Karl Helmer, Daniel Marcus, Russell Poldrack, Jessica Turner, David Kennedy, Jean-Baptiste Poline, Pohl, K. M., Department of Psychiatry and Behavioral Sciences [Stanford], Stanford Medicine, Stanford University-Stanford University, SRI International [Menlo Park] (SRI), University of California [Irvine] (UCI), University of California, Department of Psychiatry and Human Behavior [Irvine], University of California-University of California, Massachusetts Institute of Technology (MIT), Warwick Manufacturing Group [Coventry] (WMG), University of Warwick [Coventry], Wellcome Trust Centre for Neuroimaging, University College of London [London] (UCL), Department of Statistics [Coventry, UK], Department of Psychology [Stanford], Stanford University, Dartmouth College [Hanover], Otto-von-Guericke University [Magdeburg] (OVGU), University of Massachusetts [Amherst] (UMass Amherst), University of Massachusetts System (UMASS), Massachusetts General Hospital [Boston], Neuroimaging Informatics and Analysis Center [St Louis, USA], Washington University in Saint Louis (WUSTL), Georgia State University, University System of Georgia (USG), Child and Adolescent NeuroDevelopment Initiative (CANDI), University of Massachusetts Medical School [Worcester] (UMASS), University of Massachusetts System (UMASS)-University of Massachusetts System (UMASS)-Division of Neuroinformatics, The Helen Wills Neuroscience Institute (HWNI), University of California [Berkeley], University of California [Irvine] (UC Irvine), University of California (UC), University of California (UC)-University of California (UC), Otto-von-Guericke-Universität Magdeburg = Otto-von-Guericke University [Magdeburg] (OVGU), University of California [Berkeley] (UC Berkeley), and Maumet, Camille

Subjects
[SDV.NEU]Life Sciences [q-bio]/Neurons and Cognition [q-bio.NC], [SDV.NEU] Life Sciences [q-bio]/Neurons and Cognition [q-bio.NC], ComputingMilieux_MISCELLANEOUS
Abstract

International audience

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