text:
researching neural correlates of clinical disorders or cognitive functions. However, there is no consensus between methods used to identify relevant boundaries of a given region of interest (ROI) on MRIs, and those used may bear little relation to each other or the underlying structural, functional and connective architecture. This presents challenges for the analysis and synthesis of such results. We therefore performed a systematic literature review to highlight variations in the anatomical boundaries used to measure frontal regions, contextualised by up-to-date evidence from histology, hodology and neuropsychology. We searched EMBASE and MEDLINE for studies in English reporting three-dimensional boundaries for manually delineating the brain's frontal lobe or sub-regional ROIs from MRIs. Exclusion criteria were: exclusive use of co-ordinate grid systems; insufficient detail to allow method replication; publication in grey literature only. Papers were assessed on quality criteria relating to bias, reproducibility and protocol rationale. There was a large degree of variability in the three-dimensional boundaries of all regions used by the 208 eligible papers. Half of the reports did not justify their rationale for boundary selection, and each paper met on average only three quarters of quality criteria. For the frontal lobe and each subregion (frontal pole, anterior cingulate, dorsolateral, inferior-lateral, and orbitofrontal) we identified reproducible methods for a biologically plausible target ROI. It is hoped that this synthesis will guide the design of future volumetric studies of cerebral structure.},
	Author = {Cox, Simon R and Ferguson, Karen J and Royle, Natalie A and Shenkin, Susan D and MacPherson, Sarah E and MacLullich, Alasdair M J and Deary, Ian J and Wardlaw, Joanna M},
	Date-Added = {2017-04-26 10:46:45 +0000},
	Date-Modified = {2017-04-26 10:46:45 +0000},
	Doi = {10.1007/s00429-013-0527-5},
	Journal = {Brain Struct Funct},
	Journal-Full = {Brain structure \& function},
	Mesh = {Animals; Brain Mapping; Databases, Bibliographic; Frontal Lobe; Humans; Image Processing, Computer-Assisted; Magnetic Resonance Imaging},
	Month = {Jan},
	Number = {1},
	Pages = {1-22},
	Pmid = {23474540},
	Pst = {ppublish},
	Title = {A systematic review of brain frontal lobe parcellation techniques in magnetic resonance imaging},
	Volume = {219},
	Year = {2014},
	Bdsk-Url-1 = {http://dx.doi.org/10.1007/s00429-013-0527-5}}

@article{Craddock_HBM_2012,
	Abstract = {Connectivity analyses and computational modeling of human brain function from fMRI data frequently require the specification of regions of interests (ROIs). Several analyses have relied on atlases derived from anatomical or cyto-architectonic boundaries to specify these ROIs, yet the suitability of atlases for resting state functional connectivity (FC) studies has yet to be established. This article introduces a data-driven method for generating an ROI atlas by parcellating whole brain resting-state fMRI data into spatially coherent regions of homogeneous FC. Several clustering statistics are used to compare methodological trade-offs as well as determine an adequate number of clusters. Additionally, we evaluate the suitability of the parcellation atlas against four ROI atlases (Talairach and Tournoux, Harvard-Oxford, Eickoff-Zilles, and Automatic Anatomical Labeling) and a random parcellation approach. The evaluated anatomical atlases exhibit poor ROI homogeneity and do not accurately reproduce FC patterns present at the voxel scale. In general, the proposed functional and random parcellations perform equivalently for most of the metrics evaluated. ROI size and hence the number of ROIs in a parcellation had the greatest impact on their suitability for FC analysis. With 200 or fewer ROIs, the resulting parcellations consist of ROIs with anatomic homology, and thus offer increased interpretability. Parcellation results containing higher numbers of ROIs (600 or 1,000) most accurately represent FC patterns present at the voxel scale and are preferable when interpretability can be sacrificed for accuracy. The resulting atlases and clustering software have been made publicly available at: http://www.nitrc.org/projects/cluster_roi/.},
	Author = {Craddock, R Cameron and James, G Andrew and Holtzheimer, 3rd, Paul E and Hu, Xiaoping P and Mayberg, Helen S},
	Date-Added = {2017-04-26 10:46:45 +0000},
	Date-Modified = {2017-08-22 12:52:52 +0000},
	Journal = {Hum Brain Mapp},
	Pages = {1914-1928},
	Title = {A whole brain {fMRI} atlas generated via spatially constrained spectral clustering},
	Volume = {33},
	Year = {2012}}

@article{Daunizeau_NeIm_2012,
	Author = {Daunizeau, J and Stephan, KE and Friston, KJ},
	Date-Added = {2017-04-26 10:46:45 +0000},
	Date-Modified = {2017-08-22 12:52:46 +0000},
	Journal = {Neuroimage},
	Pages = {464-481},
	Title = {Stochastic dynamic causal modelling of {fMRI} data: should we care about neural noise?},
	Volume = {62},
	Year = {2012}}

@article{Deco_JNS_2013,
	Author = {Deco, G and Ponce-Alvarez, A and Mantini, D and Romani, GL and Hagmann, P and Corbetta, M},
	Date-Added = {2017-04-26 10:46:45 +0000},
	Date-Modified = {2017-04-26 10:46:45 +0000},
	Journal = {J Neurosci},
	Pages = {11239-11252},
	Title = {Resting-state functional connectivity emerges from structurally and dynamically shaped slow linear fluctuations},
	Volume = {33},
	Year = {2013}}

@article{Deco_JNS_2014,
	Author = {Deco, G and Ponce-Alvarez, A and Hagmann, P and Romani, GL and Mantini, D and Corbetta, M},
	Date-Added = {2017-04-26 10:46:45 +0000},
	Date-Modified = {2017-04-26 10:46:45 +0000},
	Journal = {J Neurosci},
	Pages = {7886-7898},
	Title = {How local excitation-inhibition ratio impacts the whole brain dynamics},
	Volume = {34},
	Year = {2014}}

@article{Deco_NatRev_2011,
	Author = {Deco, G and Jirsa, VK and McIntosh, AR},
	Date-Added = {2017-04-26 10:46:45 +0000},
	Date-Modified = {2017-04-26 10:46:45 +0000},
	Journal = {Nat Rev Neurosci},
	Pages = {43-56},
	Title = {Emerging concepts for the dynamical organization of resting-state activity in the brain},
	Volume = {12},
	Year = {2011}}

@article{Eickhoff_HBM_2015,
	Abstract = {Regional specialization and functional integration are often viewed as two fundamental principles of human brain organization. They are closely intertwined because each functionally specialized brain region is probably characterized by a distinct set of long-range connections. This notion has prompted the quickly developing family of connectivity-based parcellation (CBP) methods in neuroimaging research. CBP assumes that there is a latent structure of parcels in a region of interest (ROI). First, connectivity strengths are computed to other parts of the brain for each voxel/vertex within the ROI. These features are then used to identify functionally distinct groups of ROI voxels/vertices. CBP enjoys increasing popularity for the in-vivo mapping of regional specialization in the human brain. Due to the requirements of different applications and datasets, CBP has diverged into a heterogeneous family of methods. This broad overview critically discusses the current state as well as the commonalities and idiosyncrasies of the main CBP methods. We target frequent concerns faced by novices and veterans to provide a reference for the investigation and review of CBP studies.},
	Author = {Eickhoff, Simon B and Thirion, Bertrand and Varoquaux, Ga{\"e}l and Bzdok, Danilo},
	Date-Added = {2017-04-26 10:46:45 +0000},
	Date-Modified = {2017-04-26 10:46:45 +0000},
	Journal = {Hum Brain Mapp},
	Pages = {4771-4792},
	Title = {Connectivity-based parcellation: Critique and implications},
	Volume = {36},
	Year = {2015},
	Bdsk-Url-1 = {http://dx.doi.org/10.1002/hbm.22933}}

@article{Ekstrom_BRR_2010,
	Author = {Ekstrom, A},
	Date-Added = {2017-04-26 10:46:45 +0000},
	Date-Modified = {2017-08-22 12:52:24 +0000},
	Journal = {Brain Res Rev},
	Pages = {233-244},
	Title = {How and when the {fMRI} {BOLD} signal relates to underlying neural activity: the danger in dissociation},
	Volume = {62},
	Year = {2010}}

@article{Fan_CeCo_2016,
	Abstract = {The human brain atlases that allow correlating brain anatomy with psychological and cognitive functions are in transition from ex vivo histology-based printed atlases to digital brain maps providing multimodal in vivo information. Many current human brain atlases cover only specific structures, lack fine-grained parcellations, and fail to provide functionally important connectivity information. Using noninvasive multimodal neuroimaging techniques, we designed a connectivity-based parcellation framework that identifies the subdivisions of the entire human brain, revealing the in vivo connectivity architecture. The resulting human Brainnetome Atlas, with 210 cortical and 36 subcortical subregions, provides a fine-grained, cross-validated atlas and contains information on both anatomical and functional connections. Additionally, we further mapped the delineated structures to mental processes by reference to the BrainMap database. It thus provides an objective and stable starting point from which to explore the complex relationships between structure, connectivity, and function, and eventually improves understanding of how the human brain works. The human Brainnetome Atlas will be made freely available for download at http://atlas.brainnetome.org, so that whole brain parcellations, connections, and functional data will be readily available for researchers to use in their investigations into healthy and pathological states.},
	Author = {Fan, Lingzhong and Li, Hai and Zhuo, Junjie and Zhang, Yu and Wang, Jiaojian and Chen, Liangfu and Yang, Zhengyi and Chu, Congying and Xie, Sangma and Laird, Angela R and Fox, Peter T and Eickhoff, Simon B and Yu, Chunshui and Jiang, Tianzi},
	Date-Added = {2017-04-26 10:46:45 +0000},
	Date-Modified = {2017-04-26 10:46:45 +0000},
	Journal = {Cereb Cortex},
	Pages = {3508-3526},
	Title = {The Human Brainnetome Atlas: A New Brain Atlas Based on Connectional Architecture},
	Volume = {26},
	Year = {2016},
	Bdsk-Url-1 = {http://dx.doi.org/10.1093/cercor/bhw157}}

@article{Fornito_NeIm_2013,
	Author = {Fornito, A and Zalesky, A and Breakspear, M},
	Date-Added = {2017-04-26 10:46:45 +0000},
	Date-Modified = {2017-04-26 10:46:45 +0000},
	Journal = {Neuroimage},
	Pages = {426-444},
	Title = {Graph analysis of the human connectome: Promise, progress, and pitfalls},
	Volume = {80},
	Year = {2013}}

@article{Fox_NatRev_2007,
	Abstract = {The majority of functional neuroscience studies have focused on the brain's response to a task or stimulus. However, the brain is very active even in the absence of explicit input or output. In this Article we review recent studies examining spontaneous fluctuations in the blood oxygen level dependent (BOLD) signal of functional magnetic resonance imaging as a potentially important and revealing manifestation of spontaneous neuronal activity. Although several challenges remain, these studies have provided insight into the intrinsic functional architecture of the brain, variability in behaviour and potential physiological correlates of neurological and psychiatric disease.},
	Author = {Fox, MD and Raichle, ME},
	Date-Added = {2017-04-26 10:46:45 +0000},
	Date-Modified = {2017-04-26 10:46:45 +0000},
	Journal = {Nat Rev Neurosci},
	Pages = {700-711},
	Title = {Spontaneous fluctuations in brain activity observed with functional magnetic resonance imaging},
	Volume = {8},
	Year = {2007},
	Bdsk-Url-1 = {http://dx.doi.org/10.1038/nrn2201}}

@article{Friston_BCon_2011,
	Author = {Friston, K},
	Date-Added = {2017-04-26 10:46:45 +0000},
	Date-Modified = {2017-04-26 10:46:45 +0000},
	Journal = {Brain Connect},
	Pages = {8},
	Title = {Functional and effective connectivity: A review},
	Volume = {1},
	Year = {2011}}

@article{Friston_PB_2009,
	Author = {Friston, K},
	Date-Added = {2017-04-26 10:46:45 +0000},
	Date-Modified = {2017-04-26 10:46:45 +0000},
	Journal = {PLoS Biol},
	Pages = {e33},
	Title = {Causal modelling and brain connectivity in functional magnetic resonance imaging},
	Volume = {7},
	Year = {2009}}

@article{Friston_NeIm_2000,
	Abstract = {There is a growing appreciation of the importance of nonlinearities in evoked responses in fMRI, particularly with the advent of event-related fMRI. These nonlinearities are commonly expressed as interactions among stimuli that can lead to the suppression and increased latency of responses to a stimulus that are incurred by a preceding stimulus. We have presented previously a model-free characterization of these effects using generic techniques from nonlinear system identification, namely a Volterra series formulation. At the same time Buxton et al. (1998) described a plausible and compelling dynamical model of hemodynamic signal transduction in fMRI. Subsequent work by Mandeville et al. (1999) provided important theoretical and empirical constraints on the form of the dynamic relationship between blood flow and volume that underpins the evolution of the fMRI signal. In this paper we combine these system identification and model-based approaches and ask whether the Balloon model is sufficient to account for the nonlinear behaviors observed in real time series. We conclude that it can, and furthermore the model parameters that ensue are biologically plausible. This conclusion is based on the observation that the Balloon model can produce Volterra kernels that emulate empirical kernels. To enable this evaluation we had to embed the Balloon model in a hemodynamic input-state-output model that included the dynamics of perfusion changes that are contingent on underlying synaptic activation. This paper presents (i) the full hemodynamic model (ii), how its associated Volterra kernels can be derived, and (iii) addresses the model's validity in relation to empirical nonlinear characterizations of evoked responses in fMRI and other neurophysiological constraints.},
	Author = {Friston, KJ and Mechelli, A and Turner, R and Price, CJ},
	Date-Added = {2017-04-26 10:46:45 +0000},
	Date-Modified = {2017-08-22 12:51:46 +0000},
	Journal = {Neuroimage},
	Pages = {466-477},
	Title = {Nonlinear responses in {fMRI}: the Balloon model, Volterra kernels, and other hemodynamics},
	Volume = {12},
	Year = {2000},
	Bdsk-Url-1 = {http://dx.doi.org/10.1006/nimg.2000.0630}}

@article{Garrett_NBR_2013,
	Author = {Garrett, DD and Samanez-Larkin, GL and MacDonald, SWS and Lindenberger, U and McIntosh, AR and Grady, CL},
	Date-Added = {2017-04-26 10:46:45 +0000},
	Date-Modified = {2017-04-26 10:46:45 +0000},
	Journal = {Neurosci Biobehav Rev},
	Pages = {610-624},
	Title = {Moment-to-moment brain signal variability: A next frontier in brain mapping?},
	Volume = {37},
	Year = {2013}}

@article{Gerhard_FCN_2011,
	Author = {Gerhard, F and Pipa, G and Lima, B and Neuenschwander, S and Gerstner, W},
	Date-Added = {2017-04-26 10:46:45 +0000},
	Date-Modified = {2017-04-26 10:46:45 +0000},
	Journal = {Front Comput Neurosci},
	Pages = {4},
	Title = {Extraction of network topology from multi-electrode recordings: is there a small-world effect?},
	Volume = {5},
	Year = {2011}}

@article{Gerhard_PCB_2013,
	Author = {Gerhard, F and Kispersky, T and Gutierrez, GJ and Marder, E and Kramer, M and Eden, U},
	Date-Added = {2017-04-26 10:46:45 +0000},
	Date-Modified = {2017-04-26 10:46:45 +0000},
	Journal = {PLoS Comput Biol},
	Pages = {e1003138},
	Title = {Successful reconstruction of a physiological circuit with known connectivity from spiking activity alone},
	Volume = {9},
	Year = {2013}}

@article{Gilson_PCB_2016,
	Author = {Gilson, Matthieu and Moreno-Bote, Ruben and Ponce-Alvarez, Adri{\'a}n and Ritter, Petra and Deco, Gustavo},
	Date-Added = {2017-04-26 10:46:45 +0000},
	Date-Modified = {2017-08-22 12:51:21 +0000},
	Journal = {PLoS Comput Biol},
	Pages = {e1004762},
	Title = {Estimation of Directed Effective Connectivity from {fMRI} Functional Connectivity Hints at Asymmetries of Cortical Connectome},
	Volume = {12},
	Year = {2016}}

@article{Glasser_Nat_2016,
	Abstract = {Understanding the amazingly complex human cerebral cortex requires a map (or parcellation) of its major subdivisions, known as cortical areas. Making an accurate areal map has been a century-old objective in neuroscience. Using multi-modal magnetic resonance images from the Human Connectome Project (HCP) and an objective semi-automated neuroanatomical approach, we delineated 180 areas per hemisphere bounded by sharp changes in cortical architecture, function, connectivity, and/or topography in a precisely aligned group average of 210 healthy young adults. We characterized 97 new areas and 83 areas previously reported using post-mortem microscopy or other specialized study-specific approaches. To enable automated delineation and identification of these areas in new HCP subjects and in future studies, we trained a machine-learning classifier to recognize the multi-modal 'fingerprint' of each cortical area. This classifier detected the presence of 96.6% of the cortical areas in new subjects, replicated the group parcellation, and could correctly locate areas in individuals with atypical parcellations. The freely available parcellation and classifier will enable substantially improved neuroanatomical precision for studies of the structural and functional organization of human cerebral cortex and its variation across individuals and in development, aging, and disease.},
	Author = {Glasser, Matthew F and Coalson, Timothy S and Robinson, Emma C and Hacker, Carl D and Harwell, John and Yacoub, Essa and Ugurbil, Kamil and Andersson, Jesper and Beckmann, Christian F and Jenkinson, Mark and Smith, Stephen M and Van Essen, David C},
	Date-Added = {2017-04-26 10:46:45 +0000},
	Date-Modified = {2017-04-26 10:46:45 +0000},
	Journal = {Nature},
	Pages = {171-178},
	Title = {A multi-modal parcellation of human cerebral cortex},
	Volume = {536},
	Year = {2016},
	Bdsk-Url-1 = {http://dx.doi.org/10.1038/nature18933}}

@article{Goebel_MRI_2003,
	Author = {Goebel, R and Roebroeck, A and Kim, DS and Formisano, E},
	Date-Added = {2017-04-26 10:46:45 +0000},
	Date-Modified = {2017-08-22 12:55:19 +0000},
	Journal = {Magn Reson Imaging},
	Pages = {1251-1261},
	Title = {Investigating directed cortical interactions in time-resolved {fMRI} data using vector autoregressive modeling and {Granger} causality mapping},
	Volume = {21},
	Year = {2003}}

@article{Gotts_CogNS_2012,
	Author = {Gotts, SJ and Chow, CC and Martin, A},
	Date-Added = {2017-04-26 10:46:45 +0000},
	Date-Modified = {2017-04-26 10:46:45 +0000},
	Journal = {Cogn Neurosci},
	Pages = {227--237},
	Title = {Repetition priming and repetition suppression: A case for enhanced efficiency through neural synchronization},
	Volume = {3},
	Year = {2012}}

@article{Grystkyy_Front_2013,
	Author = {Grytskyy, D and Tetzlaff, T and Diesmann, M and Helias, M},
	Date-Added = {2017-04-26 10:46:45 +0000},
	Date-Modified = {2017-04-26 10:46:45 +0000},
	Journal = {Front Comput Neurosci},
	Pages = {131},
	Title = {A unified view on weakly correlated recurrent networks},
	Volume = {18},
	Year = {2013}}

@article{Hansen_NeIm_2015,
	Abstract = {Functional connectivity (FC) sheds light on the interactions between different brain regions. Besides basic research, it is clinically relevant for applications in Alzheimer's disease, schizophrenia, presurgical planning, epilepsy, and traumatic brain injury. Simulations of whole-brain mean-field computational models with realistic connectivity determined by tractography studies enable us to reproduce with accuracy aspects of average FC in the resting state. Most computational studies, however, did not address the prominent non-stationarity in resting state FC, which may result in large intra- and inter-subject variability and thus preclude an accurate individual predictability. Here we show that this non-stationarity reveals a rich structure, characterized by rapid transitions switching between a few discrete FC states. We also show that computational models optimized to fit time-averaged FC do not reproduce these spontaneous state transitions and, thus, are not qualitatively superior to simplified linear stochastic models, which account for the effects of structure alone. We then demonstrate that a slight enhancement of the non-linearity of the network nodes is sufficient to broaden the repertoire of possible network behaviors, leading to modes of fluctuations, reminiscent of some of the most frequently observed Resting State Networks. Because of the noise-driven exploration of this repertoire, the dynamics of FC qualitatively change now and display non-stationary switching similar to empirical resting state recordings (Functional Connectivity Dynamics (FCD)). Thus FCD bear promise to serve as a better biomarker of resting state neural activity and of its pathologic alterations.},
	Author = {Hansen, ECA and Battaglia, D and Spiegler, A and Deco, G and Jirsa, VK},
	Date-Added = {2017-04-26 10:46:45 +0000},
	Date-Modified = {2017-04-26 10:46:45 +0000},
	Journal = {Neuroimage},
	Pages = {525-535},
	Title = {Functional connectivity dynamics: modeling the switching behavior of the resting state},
	Volume = {105},
	Year = {2015},
	Bdsk-Url-1 = {http://dx.doi.org/10.1016/j.neuroimage.2014.11.001}}

@article{Harriger_PONE_2008,
	Author = {Harriger, L and van den Heuvel, MP and Sporns, O},
	Date-Added = {2017-04-26 10:46:45 +0000},
	Date-Modified = {2017-04-26 10:46:45 +0000},
	Journal = {PLoS ONE},
	Pages = {e46497},
	Title = {Rich club organization of macaque cerebral cortex and its role in network communication},
	Volume = {7},
	Year = {2012}}

@article{Heeger_NatRev_2002,
	Author = {Heeger, DJ and Ress, D},
	Date-Added = {2017-04-26 10:46:45 +0000},
	Date-Modified = {2017-08-22 12:51:02 +0000},
	Journal = {Nat Rev Neurosci},
	Pages = {142-151},
	Title = {What does {fMRI} tell us about neuronal activity?},
	Volume = {3},
	Year = {2002}}

@article{Honey_NeIm_2010,
	Abstract = {Over the past decade, scientific interest in the properties of large-scale spontaneous neural dynamics has intensified. Concurrently, novel technologies have been developed for characterizing the connective anatomy of intra-regional circuits and inter-regional fiber pathways. It will soon be possible to build computational models that incorporate these newly detailed structural network measurements to make predictions of neural dynamics at multiple scales. Here, we review the practicality and the value of these efforts, while at the same time considering in which cases and to what extent structure does determine neural function. Studies of the healthy brain, of neural development, and of pathology all yield examples of direct correspondences between structural linkage and dynamical correlation. Theoretical arguments further support the notion that brain network topology and spatial embedding should strongly influence network dynamics. Although future models will need to be tested more quantitatively and against a wider range of empirical neurodynamic features, our present large-scale models can already predict the macroscopic pattern of dynamic correlation across the brain. We conclude that as neuroscience grapples with datasets of increasing completeness and complexity, and attempts to relate the structural and functional architectures discovered at different neural scales, the value of computational modeling will continue to grow.},
	Author = {Honey, CJ and Thivierge, J-P and Sporns, O},
	Date-Added = {2017-04-26 10:46:45 +0000},
	Date-Modified = {2017-04-26 10:46:45 +0000},
	Journal = {Neuroimage},
	Pages = {766-776},
	Title = {Can structure predict function in the human brain?},
	Volume = {52},
	Year = {2010},
	Bdsk-Url-1 = {http://dx.doi.org/10.1016/j.neuroimage.2010.01.071}}

@article{James_NeIm_2009,
	Author = {James, GA and Kelley, ME and Craddock, RC and Holtzheimer, PE and Dunlop, B and Nemeroff, C and Mayberg, HS and Hu, XP},
	Date-Added = {2017-04-26 10:46:45 +0000},
	Date-Modified = {2017-08-22 12:50:40 +0000},
	Journal = {Neuroimage},
	Pages = {778-787},
	Title = {Exploratory structural equation modeling of resting-state {fMRI}: applicability of group models to individual subjects},
	Volume = {45},
	Year = {2009}}

@article{Ji_MICCAI_2009,
	Abstract = {Inter-subject parcellation of functional Magnetic Resonance Imaging (fMRI) data based on a standard General Linear Model (GLM) and spectral clustering was recently proposed as a means to alleviate the issues associated with spatial normalization in fMRI. However, for all its appeal, a GLM-based parcellation approach introduces its own biases, in the form of a priori knowledge about the shape of Hemodynamic Response Function (HRF) and task-related signal changes, or about the subject behaviour during the task. In this paper, we introduce a data-driven version of the spectral clustering parcellation, based on Independent Component Analysis (ICA) and Partial Least Squares (PLS) instead of the GLM. First, a number of independent components are automatically selected. Seed voxels are then obtained from the associated ICA maps and we compute the PLS latent variables between the fMRI signal of the seed voxels (which covers regional variations of the HRF) and the principal components of the signal across all voxels. Finally, we parcellate all subjects data with a spectral clustering of the PLS latent variables. We present results of the application of the proposed method on both single-subject and multi-subject fMRI datasets. Preliminary experimental results, evaluated with intra-parcel variance of GLM t-values and PLS derived t-values, indicate that this data-driven approach offers improvement in terms of parcellation accuracy over GLM based techniques.},
	Author = {Ji, Yongnan and Herv{\'e}, Pierre-Yves and Aickelin, Uwe and Pitiot, Alain},
	Date-Added = {2017-04-26 10:46:45 +0000},
	Date-Modified = {2017-08-22 12:50:32 +0000},
	Journal = {Med Image Comput Comput Assist Interv},
	Pages = {984-991},
	Title = {Parcellation of {fMRI} datasets with {ICA} and {PLS}--a data driven approach},
	Volume = {12},
	Year = {2009}}

@article{Jirsa_Neuroinfo_2004,
	Author = {Jirsa, VK},
	Date-Added = {2017-04-26 10:46:45 +0000},
	Date-Modified = {2017-04-26 10:46:45 +0000},
	Journal = {Neuroinformatics},
	Pages = {183-204},
	Title = {Connectivity and dynamics of neural information processing},
	Volume = {2},
	Year = {2004}}

@article{Koetter_NeInf_2004,
	Author = {K{\"o}tter, R},
	Date-Added = {2017-04-26 10:46:45 +0000},
	Date-Modified = {2017-08-22 12:50:05 +0000},
	Journal = {Neuroinformatics},
	Pages = {127-144},
	Title = {Online retrieval, processing, and visualization of primate connectivity data from the {CoCoMac} Database},
	Volume = {2},
	Year = {2004}}

@book{Kaplan_2008,
	Author = {Kaplan, D},
	Date-Added = {2017-04-26 10:46:45 +0000},
	Date-Modified = {2017-04-26 10:46:45 +0000},
	Isbn = {1412916240},
	Publisher = {Sage Publications (CA)},
	Title = {Structural equation modeling: foundations and extensions (2nd revised edition)},
	Year = {2008}}

@article{Kim_PCB_2011,
	Author = {Kim, S and Putrino, D and Ghosh, S and Brown, EN},
	Date-Added = {2017-04-26 10:46:45 +0000},
	Date-Modified = {2017-08-22 12:55:26 +0000},
	Journal = {PLoS Comput Biol},
	Pages = {e1001110},
	Title = {A {Granger} causality measure for point process models of ensemble neural spiking activity},
	Volume = {7},
	Year = {2011}}

@article{Laird_NeIm_2013,
	Abstract = {Recent progress in neuroimaging informatics and meta-analytic techniques has enabled a novel domain of human brain connectomics research that focuses on task-dependent co-activation patterns across behavioral tasks and cognitive domains. Here, we review studies utilizing the BrainMap database to investigate data trends in the activation literature using methods such as meta-analytic connectivity modeling (MACM), connectivity-based parcellation (CPB), and independent component analysis (ICA). We give examples of how these methods are being applied to learn more about the functional connectivity of areas such as the amygdala, the default mode network, and visual area V5. Methods for analyzing the behavioral metadata corresponding to regions of interest and to their intrinsically connected networks are described as a tool for local functional decoding. We finally discuss the relation of observed co-activation connectivity results to resting state connectivity patterns, and provide implications for future work in this domain.},
	Author = {Laird, Angela R and Eickhoff, Simon B and Rottschy, Claudia and Bzdok, Danilo and Ray, Kimberly L and Fox, Peter T},
	Date-Added = {2017-04-26 10:46:45 +0000},
	Date-Modified = {2017-04-26 10:46:45 +0000},
	Journal = {Neuroimage},
	Pages = {505-514},
	Title = {Networks of task co-activations},
	Volume = {80},
	Year = {2013},
	Bdsk-Url-1 = {http://dx.doi.org/10.1016/j.neuroimage.2013.04.073}}

@mastersthesis{Luensmann_2015,
	Author = {Benedict Luensmann},
	Date-Added = {2017-04-26 10:46:45 +0000},
	Date-Modified = {2017-04-26 10:46:45 +0000},
	School = {University of G\"ottingen},
	Title = {Reconstruction of physical interactions in stationary stochastic network dynamics},
	Type = {MSc Thesis},
	Url = {http://www.nld.ds.mpg.de/publications/mastersthesisreference.2015-06-10.5857567117/at_download/publication_pdf},
	Year = {2015},
	Bdsk-Url-1 = {http://www.nld.ds.mpg.de/publications/mastersthesisreference.2015-06-10.5857567117/at_download/publication_pdf}}

@article{Mantini_PNAS_2007,
	Author = {Mantini, D and Perrucci, MG and Del Gratta, C and Romani, GL and Corbetta, M},
	Date-Added = {2017-04-26 10:46:45 +0000},
	Date-Modified = {2017-04-26 10:46:45 +0000},
	Journal = {Proc Natl Acad Sci},
	Pages = {13170-13175},
	Title = {Electrophysiological signatures of resting state networks in the human brain},
	Volume = {104},
	Year = {2007}}

@article{McIntosh_JNS_1994,
	Author = {McIntosh, AR and Grady, CL and Ungerleider, LG and Haxby, JV and Rapoport, SI and Horwitz, B},
	Date-Added = {2017-04-26 10:46:45 +0000},
	Date-Modified = {2017-08-22 12:49:44 +0000},
	Journal = {J Neurosci},
	Pages = {655-666},
	Title = {Network analysis of cortical visual pathways mapped with {PET}},
	Volume = {14},
	Year = {1994}}

@article{Messe_PCB_2014,
	Author = {Mess\'e, A and Rudrauf, D and Benali, H and Marrelec, G},
	Date-Added = {2017-04-26 10:46:45 +0000},
	Date-Modified = {2017-04-26 10:46:45 +0000},
	Journal = {PLoS COmput Biol},
	Pages = {e1003530},
	Title = {Relating structure and function in the human brain: relative contributions of anatomy, stationary dynamics, and non-stationarities},
	Volume = {10},
	Year = {2014}}

@article{Mitra_EL_2015,
	Abstract = {Propagation of slow intrinsic brain activity has been widely observed in electrophysiogical studies of slow wave sleep (SWS). However, in human resting state fMRI (rs-fMRI), intrinsic activity has been understood predominantly in terms of zero-lag temporal synchrony (functional connectivity) within systems known as resting state networks (RSNs). Prior rs-fMRI studies have found that RSNs are generally preserved across wake and sleep. Here, we use a recently developed analysis technique to study propagation of infra-slow intrinsic blood oxygen level dependent (BOLD) signals in normal adults during wake and SWS. This analysis reveals marked changes in propagation patterns in SWS vs. wake. Broadly, ordered propagation is preserved within traditionally defined RSNs but lost between RSNs. Additionally, propagation between cerebral cortex and subcortical structures reverses directions, and intra-cortical propagation becomes reorganized, especially in visual and sensorimotor cortices. These findings show that propagated rs-fMRI activity informs theoretical accounts of the neural functions of sleep.},
	Author = {Mitra, A and Snyder, A Z and Tagliazucchi, E and Laufs, H and Raichle, ME},
	Date-Added = {2017-04-26 10:46:45 +0000},
	Date-Modified = {2017-04-26 10:46:45 +0000},
	Journal = {Elife},
	Title = {Propagated infra-slow intrinsic brain activity reorganizes across wake and slow wave sleep},
	Volume = {4},
	Year = {2015},
	Bdsk-Url-1 = {http://dx.doi.org/10.7554/eLife.10781}}

@article{Mitra_JNP_2014,
	Abstract = {The discovery that spontaneous fluctuations in blood oxygen level-dependent (BOLD) signals contain information about the functional organization of the brain has caused a paradigm shift in neuroimaging. It is now well established that intrinsic brain activity is organized into spatially segregated resting-state networks (RSNs). Less is known regarding how spatially segregated networks are integrated by the propagation of intrinsic activity over time. To explore this question, we examined the latency structure of spontaneous fluctuations in the fMRI BOLD signal. Our data reveal that intrinsic activity propagates through and across networks on a timescale of ∼1 s. Variations in the latency structure of this activity resulting from sensory state manipulation (eyes open vs. closed), antecedent motor task (button press) performance, and time of day (morning vs. evening) suggest that BOLD signal lags reflect neuronal processes rather than hemodynamic delay. Our results emphasize the importance of the temporal structure of the brain's spontaneous activity.},
	Author = {Mitra, A and Snyder, AZ and Hacker, CD and Raichle, ME},
	Date-Added = {2017-04-26 10:46:45 +0000},
	Date-Modified = {2017-08-22 12:49:34 +0000},
	Journal = {J Neurophysiol},
	Pages = {2374-2391},
	Title = {Lag structure in resting-state {fMRI}},
	Volume = {111},
	Year = {2014},
	Bdsk-Url-1 = {http://dx.doi.org/10.1152/jn.00804.2013}}

@article{Nir_NatNeu_2008,
	Author = {Nir, Y and Mukamel, R and Dinstein, I and Privman, E and Harel, M and Fisch, L and Gelbard-Sagiv, H and Kipervasser, S and Andelman, F and Neufeld, MY and Kramer, U and Arieli, A and Fried, I and Malach, R},
	Date-Added = {2017-04-26 10:46:45 +0000},
	Date-Modified = {2017-04-26 10:46:45 +0000},
	Journal = {Nat Neurosci},
	Pages = {1100-1108},
	Title = {Interhemispheric correlations of slow spontaneous neuronal fluctuations revealed in human sensory cortex},
	Volume = {11},
	Year = {2008}}

@article{Ostojic_JNS_2009,
	Author = {Ostojic, S and Brunel, N and Hakim, V},
	Date-Added = {2017-04-26 10:46:45 +0000},
	Date-Modified = {2017-04-26 10:46:45 +0000},
	Journal = {J Neurosci},
	Pages = {10234-10253},
	Title = {How connectivity, background activity, and synaptic properties shape the cross-correlation between spike trains},
	Volume = {29},
	Year = {2009}}

@article{Park_Sci_2013,
	Abstract = {How rich functionality emerges from the invariant structural architecture of the brain remains a major mystery in neuroscience. Recent applications of network theory and theoretical neuroscience to large-scale brain networks have started to dissolve this mystery. Network analyses suggest that hierarchical modular brain networks are particularly suited to facilitate local (segregated) neuronal operations and the global integration of segregated functions. Although functional networks are constrained by structural connections, context-sensitive integration during cognition tasks necessarily entails a divergence between structural and functional networks. This degenerate (many-to-one) function-structure mapping is crucial for understanding the nature of brain networks. The emergence of dynamic functional networks from static structural connections calls for a formal (computational) approach to neuronal information processing that may resolve this dialectic between structure and function.},
	Author = {Park, H-J and Friston, K},
	Date-Added = {2017-04-26 10:46:45 +0000},
	Date-Modified = {2017-04-26 10:46:45 +0000},
	Journal = {Science},
	Pages = {1238411},
	Title = {Structural and functional brain networks: from connections to cognition},
	Volume = {342},
	Year = {2013},
	Bdsk-Url-1 = {http://dx.doi.org/10.1126/science.1238411}}

@article{Patel_HBM_2006,
	Author = {Patel, RS and Bowman, FD and Rilling, JK},
	Date-Added = {2017-04-26 10:46:45 +0000},
	Date-Modified = {2017-04-26 10:46:45 +0000},
	Journal = {Hum Brain Mapp},
	Pages = {267--276},
	Title = {A Bayesian approach to determining connectivity of the human brain},
	Volume = {27},
	Year = {2006}}

@article{Pernice_JSM_2013,
	Author = {Pernice, V and Rotter, S},
	Date-Added = {2017-04-26 10:46:45 +0000},
	Date-Modified = {2017-04-26 10:46:45 +0000},
	Journal = {J Stat Mech},
	Pages = {P03008},
	Title = {Reconstruction of sparse connectivity in neural networks from spike train covariances},
	Volume = {2013},
	Year = {2013}}

@article{Pillow_NC_2011,
	Author = {Pillow, JW and Ahmadian, Y and Paninski, L},
	Date-Added = {2017-04-26 10:46:45 +0000},
	Date-Modified = {2017-04-26 10:46:45 +0000},
	Journal = {Neural Comput},
	Pages = {1-45},
	Title = {Model-based decoding, information estimation, and change-point detection techniques for multi-neuron spike trains},
	Volume = {23},
	Year = {2011}}

@article{Ren_PRL_2010,
	Author = {Ren, J and Wang, W-X and Li, B and Lai, Y-C},
	Date-Added = {2017-04-26 10:46:45 +0000},
	Date-Modified = {2017-04-26 10:46:45 +0000},
	Journal = {Phys Rev Lett},
	Pages = {058701},
	Title = {noise bridges dynamical correlation and topology in coupled oscillator networks},
	Volume = {104},
	Year = {2010}}

@article{Reus_NeIm_2013,
	Abstract = {The human connectome is an intricate system of interconnected elements, providing the basis for integrative brain function. An essential step in the macroscopic mapping and examination of this network of structural and functional interactions is the subdivision of the brain into large-scale regions. Parcellation approaches used for the formation of macroscopic brain networks include application of predefined anatomical templates, randomly generated templates and voxel-based divisions. In this review, we discuss the use of such parcellation approaches for the examination of connectome characteristics. We specifically address the impact of the choice of parcellation scheme and resolution on the estimation of the brain's topological and spatial network features. Although organizational principles of functional and structural brain networks appear to be largely independent of the adopted parcellation approach, quantitative measures of these principles may be significantly modulated. Future parcellation-based connectome studies might benefit from the adoption of novel network tools and promising advances in connectivity-based parcellation approaches.},
	Author = {de Reus, MA and van den Heuvel, MP},
	Date-Added = {2017-04-26 10:46:45 +0000},
	Date-Modified = {2017-04-26 10:46:45 +0000},
	Journal = {Neuroimage},
	Pages = {397-404},
	Title = {The parcellation-based connectome: limitations and extensions},
	Volume = {80},
	Year = {2013},
	Bdsk-Url-1 = {http://dx.doi.org/10.1016/j.neuroimage.2013.03.053}}

@article{Ritter_BCon_2013,
	Author = {Ritter, P and Schirner, M and McIntosh, AR and Jirsa, VK},
	Date-Added = {2017-04-26 10:46:45 +0000},
	Date-Modified = {2017-04-26 10:46:45 +0000},
	Journal = {Brain Connect},
	Pages = {121-145},
	Title = {The virtual brain integrates computational modeling and multimodal neuroimaging},
	Volume = {3},
	Year = {2013}}

@article{Ritter_NBR_2006,
	Author = {Ritter, P and Villringer, A},
	Date-Added = {2017-04-26 10:46:45 +0000},
	Date-Modified = {2017-08-22 12:49:08 +0000},
	Journal = {Neurosci Biobehav Rev},
	Pages = {823-838},
	Title = {Simultaneous {EEG}-{fMRI}},
	Volume = {30},
	Year = {2006}}

@article{Robinson_PRE_2014,
	Author = {Robinson, PA and Sarkar, S and Pandejee, GM and Henderson, JA},
	Date-Added = {2017-04-26 10:46:45 +0000},
	Date-Modified = {2017-04-26 10:46:45 +0000},
	Journal = {Phys Rev E},
	Pages = {012707},
	Title = {Determination of effective brain connectivity from functional connectivity with application to resting state connectivities},
	Volume = {90},
	Year = {2014}}

@article{RocheLabarbe_HBM_2008,
	Abstract = {Although Electroencephalography (EEG) source localization is being widely used in adults, this promising technique has not yet been applied to newborns because of technical difficulties, such as lack of data concerning the newborn skull conductivity, thickness, and homogeneity. Using a new type of EEG headcap molded on each baby's head, we aimed to determine whether this technique could be adapted to neonates, and to evaluate the importance of these technical difficulties. We carried out EEG source reconstruction of the recordings of five neonates using dipole fit algorithm. We used four different head models for each neonate, obtained from individual MRI scans: normal skull thickness and conductivity of 0.0042 S/m; normal thickness and conductivity of 0.33 S/m; increased thickness and conductivity of 0.0042 S/m; and normal thickness and conductivity with a modeled bregma fontanel. Dipole locations were consistent with MRI and clinical data. The mean difference between the dipole locations in the 0.0042 and the 0.33 S/m skull layer models was 11.6 +/- 2.5 mm, with an average 29.7% decrease in magnitude for the 0.33 S/m model but no significant changes for the dipoles orientation. Skull layer thickness had a large influence on magnitude, but no significant effect on position and orientation. The mean difference between the dipole locations induced by the modeled fontanel was 2.0 +/- 2.1 mm, with an average 2.1% increase in magnitude. Our results show that EEG source localization is feasible in neonates. With further development, the technique may prove useful for neurological evaluation of neonates.},
	Author = {Roche-Labarbe, N and Aarabi, A and Kongolo, G and Gondry-Jouet, C and D{\"u}mpelmann, M and Grebe, R and Wallois, F},
	Date-Added = {2017-04-26 10:46:45 +0000},
	Date-Modified = {2017-04-26 10:46:45 +0000},
	Journal = {Hum Brain Mapp},
	Pages = {167-176},
	Title = {High-resolution electroencephalography and source localization in neonates},
	Volume = {29},
	Year = {2008},
	Bdsk-Url-1 = {http://dx.doi.org/10.1002/hbm.20376}}

@article{Rodriguez_PRE_1996,
	Author = {Rodriguez, R and Tuckwell, H},
	Date-Added = {2017-04-26 10:46:45 +0000},
	Date-Modified = {2017-04-26 10:46:45 +0000},
	Journal = {Phys Rev E},
	Pages = {5585-5590},
	Title = {Statistical properties of stochastic nonlinear dynamical models of single spiking neurons and neural networks},
	Volume = {54},
	Year = {1996}}

@article{Roebroeck_NeIm_2011,
	Author = {Roebroeck, A and Formisano, E and Goebel, R},
	Date-Added = {2017-04-26 10:46:45 +0000},
	Date-Modified = {2017-08-22 12:48:55 +0000},
	Journal = {Neuroimage},
	Pages = {296-302},
	Title = {The identification of interacting networks in the brain using {fMRI}: Model selection, causality and deconvolution},
	Volume = {58},
	Year = {2011}}

@article{SanzLeon_FN_2013,
	Abstract = {We present The Virtual Brain (TVB), a neuroinformatics platform for full brain network simulations using biologically realistic connectivity. This simulation environment enables the model-based inference of neurophysiological mechanisms across different brain scales that underlie the generation of macroscopic neuroimaging signals including functional MRI (fMRI), EEG and MEG. Researchers from different backgrounds can benefit from an integrative software platform including a supporting framework for data management (generation, organization, storage, integration and sharing) and a simulation core written in Python. TVB allows the reproduction and evaluation of personalized configurations of the brain by using individual subject data. This personalization facilitates an exploration of the consequences of pathological changes in the system, permitting to investigate potential ways to counteract such unfavorable processes. The architecture of TVB supports interaction with MATLAB packages, for example, the well known Brain Connectivity Toolbox. TVB can be used in a client-server configuration, such that it can be remotely accessed through the Internet thanks to its web-based HTML5, JS, and WebGL graphical user interface. TVB is also accessible as a standalone cross-platform Python library and application, and users can interact with the scientific core through the scripting interface IDLE, enabling easy modeling, development and debugging of the scientific kernel. This second interface makes TVB extensible by combining it with other libraries and modules developed by the Python scientific community. In this article, we describe the theoretical background and foundations that led to the development of TVB, the architecture and features of its major software components as well as potential neuroscience applications.},
	Author = {Sanz Leon, P and Knock, SA and Woodman, MM and Domide, L and Mersmann, J and McIntosh, AR and Jirsa, V},
	Date-Added = {2017-04-26 10:46:45 +0000},
	Date-Modified = {2017-04-26 10:46:45 +0000},
	Journal = {Front Neuroinform},
	Pages = {10},
	Title = {The Virtual Brain: a simulator of primate brain network dynamics},
	Volume = {7},
	Year = {2013},
	Bdsk-Url-1 = {http://dx.doi.org/10.3389/fninf.2013.00010}}

@article{Schelter_PRL_2006,
	Author = {Schelter, B and Winterhalder, M and Dahlhaus, R and Kurths, J and Timmer, J},
	Date-Added = {2017-04-26 10:46:45 +0000},
	Date-Modified = {2017-04-26 10:46:45 +0000},
	Journal = {Phys Rev Lett},
	Pages = {208103},
	Title = {Partial phase synchronization for multivariate synchronizing systems},
	Volume = {96},
	Year = {2006}}

@article{Schirner_NeIm_2015,
	Author = {Schirner, M and Rothmeier, S and Jirsa, V and McIntosh, AR and Ritter, P},
	Date-Added = {2017-04-26 10:46:45 +0000},
	Date-Modified = {2017-04-26 10:46:45 +0000},
	Journal = {Neuroimage},
	Pages = {343-357},
	Title = {An automated pipeline for constructing personalised virtual brains from multimodal neuroimaging data},
	Volume = {117},
	Year = {2015}}

@article{Schultze_ProgBio_2011,
	Author = {Schultze-Kraft, M and Becker, R and Breakspear, M and Ritter, P},
	Date-Added = {2017-04-26 10:46:45 +0000},
	Date-Modified = {2017-08-22 12:48:26 +0000},
	Journal = {Prog Biophys Mol Biol},
	Pages = {67-79},
	Title = {Exploiting the potential of three dimensional spatial wavelet analysis to explore the nesting of oscillations and spatial variance in simultaneous {EEG}-{fMRI} data},
	Volume = {105},
	Year = {2011}}

@article{Smith_NeIm_2011,
	Author = {Smith, SM and Miller, KL and Salimi-Khorshidi, G and Webster, M and Beckmann, CF and Nichols, TE and Ramsey, JD and Woolrich, MW},
	Date-Added = {2017-04-26 10:46:45 +0000},
	Date-Modified = {2017-08-22 12:48:15 +0000},
	Journal = {NeuroImage},
	Pages = {875--891},
	Title = {Network modelling methods for {fMRI}},
	Volume = {54},
	Year = {2011}}

@article{Sporns_NeIm_2013,
	Abstract = {The human connectome refers to a map of the brain's structural connections, rendered as a connection matrix or network. This article attempts to trace some of the historical origins of the connectome, in the process clarifying its definition and scope, as well as its putative role in illuminating brain function. Current efforts to map the connectome face a number of significant challenges, including the issue of capturing network connectivity across multiple spatial scales, accounting for individual variability and structural plasticity, as well as clarifying the role of the connectome in shaping brain dynamics. Throughout, the article argues that these challenges require the development of new approaches for the statistical analysis and computational modeling of brain network data, and greater collaboration across disciplinary boundaries, especially with researchers in complex systems and network science.},
	Author = {Sporns, Olaf},
	Date-Added = {2017-04-26 10:46:45 +0000},
	Date-Modified = {2017-04-26 10:46:45 +0000},
	Journal = {Neuroimage},
	Pages = {53-61},
	Title = {The human connectome: origins and challenges},
	Volume = {80},
	Year = {2013},
	Bdsk-Url-1 = {http://dx.doi.org/10.1016/j.neuroimage.2013.03.023}}

@article{Stetter_PCB_2012,
	Author = {Stetter, O and Battaglia, D and Soriano, J and Geisel, T},
	Date-Added = {2017-04-26 10:46:45 +0000},
	Date-Modified = {2017-04-26 10:46:45 +0000},
	Journal = {PLoS Comput Biol},
	Pages = {e1002653},
	Title = {Model-free reconstruction of excitatory neuronal connectivity from calcium imaging signals},
	Volume = {8},
	Year = {2012}}

@incollection{Storkey_NIPS_2007,
	Author = {Storkey, AJ and Simonotto, E and Whalley, H and Lawrie, S and Murray, L and Mcgonigle, D},
	Booktitle = {Advances in Neural Information Processing Systems 19},
	Date-Added = {2017-04-26 10:46:45 +0000},
	Date-Modified = {2017-08-22 12:47:49 +0000},
	Editor = {Sch\"{o}lkopf, B and Platt, JC and Hoffman, T},
	Pages = {1329-1336},
	Publisher = {MIT Press},
	Title = {Learning Structural Equation Models for {fMRI}},
	Year = {2007}}

@article{Tewarie_NeIm_2016,
	Abstract = {Neuronal oscillations exist across a broad frequency spectrum, and are thought to provide a mechanism of interaction between spatially separated brain regions. Since ongoing mental activity necessitates the simultaneous formation of multiple networks, it seems likely that the brain employs interactions within multiple frequency bands, as well as cross-frequency coupling, to support such networks. Here, we propose a multi-layer network framework that elucidates this pan-spectral picture of network interactions. Our network consists of multiple layers (frequency-band specific networks) that influence each other via inter-layer (cross-frequency) coupling. Applying this model to MEG resting-state data and using envelope correlations as connectivity metric, we demonstrate strong dependency between within layer structure and inter-layer coupling, indicating that networks obtained in different frequency bands do not act as independent entities. More specifically, our results suggest that frequency band specific networks are characterised by a common structure seen across all layers, superimposed by layer specific connectivity, and inter-layer coupling is most strongly associated with this common mode. Finally, using a biophysical model, we demonstrate that there are two regimes of multi-layer network behaviour; one in which different layers are independent and a second in which they operate highly dependent. Results suggest that the healthy human brain operates at the transition point between these regimes, allowing for integration and segregation between layers. Overall, our observations show that a complete picture of global brain network connectivity requires integration of connectivity patterns across the full frequency spectrum.},
	Author = {Tewarie, P and Hillebrand, A and van Dijk, BW and Stam, CJ and O'Neill, GC and Van Mieghem, P and Meier, Jil M and Woolrich, Mark W and Morris, PG and Brookes, MJ},
	Date-Added = {2017-04-26 10:46:45 +0000},
	Date-Modified = {2017-08-22 12:47:24 +0000},
	Journal = {Neuroimage},
	Pages = {324-336},
	Title = {Integrating cross-frequency and within band functional networks in resting-state {MEG}: A multi-layer network approach},
	Volume = {142},
	Year = {2016},
	Bdsk-Url-1 = {http://dx.doi.org/10.1016/j.neuroimage.2016.07.057}}

@article{Timme_JPA_2014,
	Author = {Timme, M and Casadiego, J},
	Date-Added = {2017-04-26 10:46:45 +0000},
	Date-Modified = {2017-04-26 10:46:45 +0000},
	Journal = {J Phys A Math Gen},
	Pages = {343001},
	Title = {Revealing networks from dynamics: An introduction},
	Volume = {47},
	Year = {2014}}

@article{Trousdale_PCB_2012,
	Author = {Trousdale, J and Hu, Y and Shea-Brown, E and Jo\'sic, K},
	Date-Added = {2017-04-26 10:46:45 +0000},
	Date-Modified = {2017-04-26 10:46:45 +0000},
	Journal = {PLoS Comput Biol},
	Pages = {e1002408},
	Title = {Impact of network structure and cellular response on spike time correlations},
	Volume = {8},
	Year = {2012}}

@article{ValdesSosa_NeIm_2011,
	Author = {Valdes-Sosa, PA and Roebroeck, A and Daunizeau, J and Friston, K},
	Date-Added = {2017-04-26 10:46:45 +0000},
	Date-Modified = {2017-04-26 10:46:45 +0000},
	Journal = {Neuroimage},
	Pages = {339-361},
	Title = {Effective connectivity: Influence, causality and biophysical modeling},
	Volume = {58},
	Year = {2011}}

@article{Wilson_Kyb_1973,
	Author = {Wilson, HR and Cowan, JD},
	Date-Added = {2017-04-26 10:46:45 +0000},
	Date-Modified = {2017-04-26 10:46:45 +0000},
	Journal = {Kybernetik},
	Pages = {55-80},
	Title = {A mathematical theory of the functional dynamics of nervous tissue},
	Volume = {13},
	Year = {1973}}

@article{Zhang_BSF_2015,
	Abstract = {Resting-state fMRI (rs-fMRI) has been widely used to segregate the brain into individual modules based on the presence of distinct connectivity patterns. Many parcellation methods have been proposed for brain parcellation using rs-fMRI, but their results have been somewhat inconsistent, potentially due to various types of noise. In this study, we provide a robust parcellation method for rs-fMRI-based brain parcellation, which constructs a sparse similarity graph based on the sparse representation coefficients of each seed voxel and then uses spectral clustering to identify distinct modules. Both the local time-varying BOLD signals and whole-brain connectivity patterns may be used as features and yield similar parcellation results. The robustness of our method was tested on both simulated and real rs-fMRI datasets. In particular, on simulated rs-fMRI data, sparse representation achieved good performance across different noise levels, including high accuracy of parcellation and high robustness to noise. On real rs-fMRI data, stable parcellation of the medial frontal cortex (MFC) and parietal operculum (OP) were achieved on three different datasets, with high reproducibility within each dataset and high consistency across these results. Besides, the parcellation of MFC was little influenced by the degrees of spatial smoothing. Furthermore, the consistent parcellation of OP was also well corresponding to cytoarchitectonic subdivisions and known somatotopic organizations. Our results demonstrate a new promising approach to robust brain parcellation using resting-state fMRI by sparse representation.},
	Author = {Zhang, Yu and Caspers, Svenja and Fan, Lingzhong and Fan, Yong and Song, Ming and Liu, Cirong and Mo, Yin and Roski, Christian and Eickhoff, Simon and Amunts, Katrin and Jiang, Tianzi},
	Date-Added = {2017-04-26 10:46:45 +0000},
	Date-Modified = {2017-08-22 12:47:01 +0000},
	Journal = {Brain Struct Funct},
	Pages = {3565-3579},
	Title = {Robust brain parcellation using sparse representation on resting-state {fMRI}},
	Volume = {220},
	Year = {2015}}

license: MIT

license_type: permissive

branch_name: refs/heads/master

extension: bib


text:
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set(LIBDIR ${OUTDIR}/lib)
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set(CMAKE_LIBRARY_OUTPUT_DIRECTORY ${LIBDIR})
set(CMAKE_RUNTIME_OUTPUT_DIRECTORY ${BINDIR})

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  if (WIN32)
    set(WINDOWS TRUE)

    # Visual Studio creates configuration sub-folders in the output
    foreach( OUTPUTCONFIG ${CMAKE_CONFIGURATION_TYPES} )
      string( TOUPPER ${OUTPUTCONFIG} OUTPUTCONFIG )
      set( CMAKE_ARCHIVE_OUTPUT_DIRECTORY_${OUTPUTCONFIG} ${LIBDIR} )
      set( CMAKE_LIBRARY_OUTPUT_DIRECTORY_${OUTPUTCONFIG} ${LIBDIR} )
      set( CMAKE_RUNTIME_OUTPUT_DIRECTORY_${OUTPUTCONFIG} ${BINDIR} )
    endforeach( OUTPUTCONFIG ${CMAKE_CONFIGURATION_TYPES} )
  else()
    set(LINUX TRUE)
  endif (WIN32)
endif (${CMAKE_SYSTEM_NAME} MATCHES "Darwin")

# submodules

if (NOT DEFINED ROOT_LIBQUIC)
  set(ROOT_LIBQUIC ${ROOT}/src/libquic CACHE PATH "libquic directory" FORCE)
endif (NOT DEFINED ROOT_LIBQUIC)

if (NOT DEFINED ROOT_JSONCPP)
  set(ROOT_JSONCPP ${ROOT}/src/jsoncpp CACHE PATH "jsoncpp directory" FORCE)
endif (NOT DEFINED ROOT_JSONCPP)

# C++
include(${ROOT}/Cpp.cmake)

repository: aeres-io/aeres

repo_name: aeres-io/aeres

extension: cmake


text:
.nivo-controlNav { position:absolute; left:47%; bottom:-30px; }
.nivo-controlNav a { display:block; width:10px; height:10px; top: -10px; background:url(bullets.png) no-repeat; text-indent:-9999px; border:0; margin-right:3px; float:left; }
.nivo-controlNav a.active { background-position:-10px 0; }
.nivo-directionNav a { display:block; width:30px; height:30px; background:url(arrows.png) no-repeat; text-indent:-9999px; border:0; }
a.nivo-nextNav { background-position:-30px 0; right:10px; }
a.nivo-prevNav { left:10px; }

repository: hybrida/hybridaweb

repo_name: hybrida/hybridaweb

extension: css


text:
@inproceedings{Du2019ImplicitGA,
  title={Implicit Generation and Generalization in Energy-Based Models},
  author={Yilun Du and Igor Mordatch},
  year={2019}
}

url: https://raw.githubusercontent.com/Hugh-Shi/DL-NLP-Readings/924e4d189e5660c5b5d4854278f3df755ae2231d/Bibtex/Implicit Generation and Generalization in Energy-Based Models.bib

repository: Hugh-Shi/DL-NLP-Readings

repo_name: Hugh-Shi/DL-NLP-Readings

gha_license_id: MIT


text:
require ${PN}.inc

PR = "r1"

SRC_URI = "http://sources.openembedded.org/opie-1.2.4-split_noncore_decorations_polished.tar.bz2;name=split_noncore_decorations_polished"
SRC_URI[split_noncore_decorations_polished.md5sum] = "0704552e7ffd05d7daba7921c7eb97fc"
SRC_URI[split_noncore_decorations_polished.sha256sum] = "0ff6ce60f085984d038d4c74281e7cf1796ccf479d4141ae1d1adc0936eb6033"

repository: overo/overo-oe-natty

repo_name: overo/overo-oe-natty

branch_name: HEAD

extension: bb


text:
require ${PN}.inc

PR = "r0"

SRC_URI = "http://sources.openembedded.org/opie-1.2.5-split_inputmethods_unikeyboard.tar.bz2;name=split_inputmethods_unikeyboard"
SRC_URI[split_inputmethods_unikeyboard.md5sum] = "ec4037ab5076d196b0a65cb232a9a7aa"
SRC_URI[split_inputmethods_unikeyboard.sha256sum] = "3262bf997a9e0f99a07c9b5fdaa387bdcb07506b1a629977c6eae2f215e6758e"


text:
require postfix.inc

SRC_URI[md5sum] = "61caffae689c11d09b4c972a394ae3b1"
SRC_URI[sha256sum] = "401e46ec3450569dcce60d1d8ca22a19ab1f7f817b0cc730cdf4875ba608ac02"

repository: reMarkable/meta-openembedded

repo_name: reMarkable/meta-openembedded

branch_name: refs/heads/zero-gravitas

gha_language: BitBake


text:
DESCRIPTION = "OPKG Package Manager Utilities"
SECTION = "base"
HOMEPAGE = "http://wiki.openmoko.org/wiki/Opkg"
PRIORITY = "optional"
RDEPENDS_${PN} = "python"
RDEPENDS_${PN}_virtclass-native = ""
SRCREV = "4747"
PV = "0.1.8+svnr${SRCPV}"
PR = "r6"

SRC_URI = "svn://svn.openmoko.org/trunk/src/host/;module=opkg-utils;proto=http \
           file://index-ignore-filenotfound.patch \
           file://mtime-int.patch"

S = "${WORKDIR}/opkg-utils"

# Avoid circular dependencies from package_ipk.bbclass
PACKAGES_virtclass-native = ""

do_install() {
        oe_runmake PREFIX=${prefix} DESTDIR=${D} install
}

BBCLASSEXTEND = "native"
TARGET_CC_ARCH += "${LDFLAGS}"


text:
* {
  margin: 0;
  padding: 0;
  box-sizing: border-box;
  outline: none;
}

.btn-change{
  display: inline-block;
  position: relative;
  height: 45px;
  line-height: 41px;
  text-transform: uppercase;
  font-family: 'Montserrat';
  font-size: 9px;
  font-weight: 400;
  color: #111111;
  padding: 0 50px;
  letter-spacing: 0.15em;
  border: 2px solid #d9d9d9;
  border-radius: 100px;
  background: transparent;
  cursor: pointer;
  transition: all 0.3s ease;

background: rgb(86, 104, 103);

box-shadow: 0 0 1px #ccc;
-webkit-transition: all 0.5s ease-in-out;
border: 0px;
color: #fff;
}
.btn-change:first-child{
margin-right: 10px;
}
.btn-change:hover{
-webkit-transform: scale(1.1);
background: #5aaeca;
}

.navigation {
text-align:center;margin-top: 40px;margin-bottom: 30px;
}

.filter-name {
  background: #795548;
  padding: 2px;
  display: inline-block;
  color: white;
  width: 100px;
  border-radius: 3px;
  text-align: center;
}

.filter-buttons button:hover {
  background-color: #2384c6;
    border-color: #217dbb;
}

.filter-group {
  display: inline-block;
  margin: 5px 15px;
}

body {
  text-align: center;
  margin: 20px auto;
  font-family: "Lato";
}

h1 {
  margin: 10px 0 0 0;
  font-size: 2em;
}

canvas {
  margin-bottom: 40px;
  background: #ececec;
  width: 600px;
  max-width: 100%;
}

.filter-buttons button,
.editor-buttons button {
  margin: 4px;
  background: black;
  border: none;
  color: white;
  font-family: "Lato";
  padding: 8px 12px;
  border-radius: 4px;
  font-size: 1em;
}

.filter-buttons button.disabled {
  background: #aaa;
}

.filter-buttons button {
  padding: 2px 10px;
  margin: 0;
  cursor: pointer;
  color: #fff;
    background-color: #217dbb;
    border-color: #1f76b0;
}

.editor-buttons #reset-btn {
  background: #f44336;
}

.editor-buttons #download-btn {
  background: #009688;
}

.editor-buttons button:hover {
  background: #795548;
  cursor: pointer;
}

.editor-buttons {
  display: inline-block;
  margin-top: 10px;
}

[type="file"] {
  display: none;
}

[type="file"] + label {
  margin: 10px 4px;
  background: #673ab7;
  border: none;
  color: white;
  cursor: pointer;
  font-family: "Lato";
  padding: 8px 12px;
  border-radius: 4px;
  display: inline-block;
}

[type="file"] + label:hover {
  background: #3399ff;
}

@media  (min-width: 1200px){
.container {
    max-width: 1140px;
}}

@media (min-width: 992px)
{.container {
    max-width: 960px;
}}
@media (min-width: 768px)
{.container {
    max-width: 720px;
}}
@media (min-width: 576px)
{.container {
    max-width: 540px;
}}
.container {
    width: 50%;
    padding-right: 15px;
    padding-left: 15px;
    margin-right: auto;
    margin-left: auto;
}
.card {
    position: relative;
    display: -ms-flexbox;
    display: flex;
    -ms-flex-direction: column;
    flex-direction: column;
    min-width: 0;
    word-wrap: break-word;
    background-color: #fff;
    background-clip: border-box;
    border: 1px solid rgba(0,0,0,.125);
    border-radius: .25rem;
}

.h1, .h2, .h3, .h4, .h5, .h6, h1, h2, h3, h4, h5, h6 {
    margin-bottom: .5rem;
    font-family: inherit;
    font-weight: 500;
    line-height: 1.2;
    color: inherit;
}

/* app 14 */
canvas {
  margin: auto;
  background: #ececec;
  width: 100%;
}

.btn-disabled {
  color: #fff !important;
}

repository: nickvaldev/freewebtools

repo_name: nickvaldev/freewebtools


text:
@InProceedings{yuan12,
  title = {Forward Basis Selection for Sparse Approximation over Dictionary},
  author = {Xiaotong Yuan and Shuicheng Yan},
  pages = {1377--1388},
  abstract = {Recently, forward greedy selection method has been successfully applied to approximately solve sparse learning problems, characterized by a trade-off between sparsity and accuracy. In this paper, we generalize this method to the setup of sparse approximation over a pre-fixed dictionary. A fully corrective forward selection algorithm is proposed along with convergence analysis. The per-iteration computational overhead of the proposed algorithm is dominated by a subproblem of linear optimization over the dictionary and a subproblem to optimally adjust the aggregation weights. The former is cheaper in several applications than the Euclidean projection while the latter is typically an unconstrained optimization problem which is relatively easy to solve. Furthermore, we extend the proposed algorithm to the setting of non-negative/convex sparse approximation over a dictionary.Applications of our algorithms to several concrete learning problems are explored with efficiency validated on benchmark data sets.},
  pdf = {http://jmlr.org/proceedings/papers/v22/yuan12/yuan12.pdf},
}

repository: mreid/papersite

repo_name: mreid/papersite

gha_language: TeX


text:
require ${BPN}.inc

SRC_URI = "\
    ${E_MIRROR}/${SRCNAME}-${SRCVER}.tar.gz \
"

SRC_URI[md5sum] = "954fe8e40fec6a561190ff0fb75b6bdd"
SRC_URI[sha256sum] = "a05be096c911e0d66d4bdc497ebb935a04ad23696de9084aed9959b5172a593e"

PNBLACKLIST[expedite] ?= "Depends on blacklisted eet"

PNBLACKLIST[expedite] ?= "Depends on blacklisted evas"

PNBLACKLIST[expedite] ?= "Runtime depends on blacklisted evas-loader-png"

PNBLACKLIST[expedite] ?= "Runtime depends on blacklisted expedite"

PNBLACKLIST[expedite] ?= "Runtime depends on blacklisted expedite-themes"

PNBLACKLIST[expedite] ?= "Runtime depends on blacklisted evas-engine-gl-x11"

PNBLACKLIST[expedite] ?= "Runtime depends on blacklisted evas-engine-software-generic"

PNBLACKLIST[expedite] ?= "Runtime depends on blacklisted expedite-dev"

PNBLACKLIST[expedite] ?= "Runtime depends on blacklisted evas-engine-software-x11"

PNBLACKLIST[expedite] ?= "Runtime depends on blacklisted evas-engine-fb"

repository: cooleels/meta-openembedded

repo_name: cooleels/meta-openembedded


text:
# Get the relative path to our message directory
GET_FILENAME_COMPONENT(message_include_dir "${PROJECT_SOURCE_DIR}/${NUCLEAR_MESSAGE_DIR}/.." ABSOLUTE)
FILE(RELATIVE_PATH message_include_dir ${PROJECT_SOURCE_DIR} ${message_include_dir})

# Get our two include directories for message
SET(message_source_include_dir "${PROJECT_SOURCE_DIR}/${message_include_dir}")
SET(message_binary_include_dir "${PROJECT_BINARY_DIR}/${message_include_dir}")

# Make our message include directories variable
SET(NUCLEAR_MESSAGE_INCLUDE_DIRS
    ${CMAKE_CURRENT_SOURCE_DIR}/include
    ${message_source_include_dir}
    ${message_binary_include_dir}
    CACHE INTERNAL "Include directories for the message folder and generated sources")

# Include our message directories
INCLUDE_DIRECTORIES(${NUCLEAR_MESSAGE_INCLUDE_DIRS})

# Get our source and binary directories for message
SET(message_source_dir "${PROJECT_SOURCE_DIR}/${NUCLEAR_MESSAGE_DIR}")
SET(message_binary_dir "${PROJECT_BINARY_DIR}/${NUCLEAR_MESSAGE_DIR}")

# We need protobuf and python to generate the neutron messages
FIND_PACKAGE(Protobuf REQUIRED)
FIND_PACKAGE(PythonInterp 3 REQUIRED)

INCLUDE_DIRECTORIES(SYSTEM ${Protobuf_INCLUDE_DIRS})

# If we have the package pybind11 we can use to go generate python bindings
#FIND_PACKAGE(pybind11)

# We need Eigen3
FIND_PACKAGE(Eigen3 REQUIRED)
INCLUDE_DIRECTORIES(SYSTEM ${Eigen3_INCLUDE_DIRS})

# We need Eigen3
FIND_PACKAGE(Eigen3 REQUIRED)
INCLUDE_DIRECTORIES(SYSTEM ${Eigen3_INCLUDE_DIRS})

# If we found pybind11 include its directories
IF(pybind11_FOUND)
    FIND_PACKAGE(PythonLibsNew 3 REQUIRED)

    INCLUDE_DIRECTORIES(SYSTEM ${pybind11_INCLUDE_DIRS})
    INCLUDE_DIRECTORIES(SYSTEM ${PYTHON_INCLUDE_DIRS})
ENDIF()

# Build our builtin protobuf classes
FILE(GLOB_RECURSE builtin "${CMAKE_CURRENT_SOURCE_DIR}/proto/**.proto")
FOREACH(proto ${builtin})

    # Get the file without the extension
    GET_FILENAME_COMPONENT(file_we ${proto} NAME_WE)

    # Run the protocol buffer compiler on the builtin protocol buffers
    ADD_CUSTOM_COMMAND(
        OUTPUT "${message_binary_include_dir}/${file_we}.pb.cc"
               "${message_binary_include_dir}/${file_we}.pb.h"
               "${message_binary_include_dir}/${file_we}_pb2.py"
        COMMAND ${PROTOBUF_PROTOC_EXECUTABLE}
        ARGS --cpp_out=lite:${message_binary_include_dir}
             --python_out=${message_binary_include_dir}
             -I${CMAKE_CURRENT_SOURCE_DIR}/proto
             "${CMAKE_CURRENT_SOURCE_DIR}/proto/${file_we}.proto"
        DEPENDS "${CMAKE_CURRENT_SOURCE_DIR}/proto/${file_we}.proto"
        COMMENT "Compiling protocol buffer ${proto}")

    SET(src ${src}
            "${message_binary_include_dir}/${file_we}.pb.cc"
            "${message_binary_include_dir}/${file_we}.pb.h"
            "${message_binary_include_dir}/${file_we}_pb2.py")

    # Prevent Effective C++ and unused parameter error checks being performed on generated files.
    SET_SOURCE_FILES_PROPERTIES("${message_binary_include_dir}/${file_we}.pb"
                                "${message_binary_include_dir}/${file_we}.proto"
                                "${message_binary_include_dir}/${file_we}.pb.cc"
                                "${message_binary_include_dir}/${file_we}.pb.h"
                                "${message_binary_include_dir}/${file_we}.cpp"
                                "${message_binary_include_dir}/${file_we}.py.cpp"
                                "${message_binary_include_dir}/${file_we}.h"
                                 PROPERTIES COMPILE_FLAGS "-Wno-unused-parameter -Wno-error=unused-parameter -Wno-error")

ENDFOREACH(proto)

# Get our dependency files for our message class generator
FILE(GLOB_RECURSE message_class_generator_depends "${CMAKE_CURRENT_SOURCE_DIR}/generator/**.py")

UNSET(message_dependencies)

# Build all of our normal messages
FILE(GLOB_RECURSE protobufs "${message_source_dir}/**.proto")
FOREACH(proto ${protobufs})

    # Get the file without the extension
    GET_FILENAME_COMPONENT(file_we ${proto} NAME_WE)

    # Calculate the Output Directory
    FILE(RELATIVE_PATH message_rel_path ${message_source_dir} ${proto})
    GET_FILENAME_COMPONENT(outputpath ${message_rel_path} PATH)
    SET(outputpath "${message_binary_dir}/${outputpath}")

    # Create the output directory
    FILE(MAKE_DIRECTORY ${outputpath})

    # Get the dependencies on this protobuf so we can recompile on changes
    # If they change you will need to re cmake
    EXECUTE_PROCESS(COMMAND ${PROTOBUF_PROTOC_EXECUTABLE}
                            --dependency_out=${CMAKE_CURRENT_BINARY_DIR}/temp1
                            --descriptor_set_out=${CMAKE_CURRENT_BINARY_DIR}/temp2
                            -I${message_source_include_dir}
                            -I${CMAKE_CURRENT_SOURCE_DIR}/proto
                            ${proto})

    FILE(READ "${CMAKE_CURRENT_BINARY_DIR}/temp1" dependencies)
    STRING(REGEX REPLACE "\\\\\n" ";" dependencies ${dependencies})
    FILE(REMOVE "${CMAKE_CURRENT_BINARY_DIR}/temp1" "${CMAKE_CURRENT_BINARY_DIR}/temp2")

    UNSET(source_depends)
    UNSET(binary_depends)

    # Clean our dependency list
    FOREACH(depend ${dependencies})
        STRING(STRIP ${depend} depend)
        FILE(RELATIVE_PATH depend_rel ${message_source_dir} ${depend})

        # Absolute dependencies
        IF(depend_rel MATCHES "^\\.\\.")
            SET(source_depends ${source_depends} ${depend})
            SET(binary_depends ${binary_depends} ${depend})
        # Relative dependencies
        ELSE()
            SET(source_depends ${source_depends} "${message_source_dir}/${depend_rel}")
            SET(binary_depends ${binary_depends} "${message_binary_dir}/${depend_rel}")
        ENDIF()
    ENDFOREACH()

    # Extract the protocol buffer information so we can generate code off it
    ADD_CUSTOM_COMMAND(
        OUTPUT "${outputpath}/${file_we}.pb"
               "${outputpath}/${file_we}.dep"
        COMMAND ${PROTOBUF_PROTOC_EXECUTABLE}
        ARGS --descriptor_set_out="${outputpath}/${file_we}.pb"
             --dependency_out="${outputpath}/${file_we}.dep"
             -I${message_source_include_dir}
             -I${CMAKE_CURRENT_SOURCE_DIR}/proto
             ${proto}
        DEPENDS ${source_depends}
        COMMENT "Extracting protocol buffer information from ${proto}")

    # Gather the dependencies for each message.
    SET(message_dependencies ${message_dependencies} "${outputpath}/${file_we}.dep")

    # Repackage our protocol buffers so they don't collide with the actual classes
    # when we make our c++ protobuf classes by adding protobuf to the package
    ADD_CUSTOM_COMMAND(
        OUTPUT "${outputpath}/${file_we}.proto"
        COMMAND ${PYTHON_EXECUTABLE}
        ARGS "${CMAKE_CURRENT_SOURCE_DIR}/repackage_message.py" ${proto} ${outputpath}
        DEPENDS "${CMAKE_CURRENT_SOURCE_DIR}/repackage_message.py"
                ${source_depends}
        COMMENT "Repackaging protobuf ${proto}")

    # Run the protocol buffer compiler on these new protobufs
    ADD_CUSTOM_COMMAND(
        OUTPUT "${outputpath}/${file_we}.pb.cc"
               "${outputpath}/${file_we}.pb.h"
               "${outputpath}/${file_we}_pb2.py"
        COMMAND ${PROTOBUF_PROTOC_EXECUTABLE}
        ARGS --cpp_out=lite:${message_binary_include_dir}
             --python_out=${message_binary_include_dir}
             -I${message_binary_include_dir}
             -I${CMAKE_CURRENT_SOURCE_DIR}/proto
             "${outputpath}/${file_we}.proto"
        DEPENDS ${binary_depends}
        COMMENT "Compiling protocol buffer ${proto}")

    # Build our c++ class from the extracted information
    ADD_CUSTOM_COMMAND(
        OUTPUT "${outputpath}/${file_we}.cpp"
               "${outputpath}/${file_we}.py.cpp"
               "${outputpath}/${file_we}.h"
        COMMAND ${PYTHON_EXECUTABLE}
        ARGS "${CMAKE_CURRENT_SOURCE_DIR}/build_message_class.py" "${outputpath}/${file_we}" ${outputpath}
        WORKING_DIRECTORY ${message_binary_dir}
        DEPENDS "${CMAKE_CURRENT_SOURCE_DIR}/build_message_class.py"
                ${message_class_generator_depends}
                "${message_binary_include_dir}/Neutron_pb2.py"
                "${outputpath}/${file_we}.pb"
        COMMENT "Building classes for ${proto}")

    # The protobuf descriptions are generated
    SET_SOURCE_FILES_PROPERTIES("${outputpath}/${file_we}.pb"
                                "${outputpath}/${file_we}.proto"
                                "${outputpath}/${file_we}.pb.cc"
                                "${outputpath}/${file_we}.pb.h"
                                "${outputpath}/${file_we}.cpp"
                                "${outputpath}/${file_we}.py.cpp"
                                "${outputpath}/${file_we}.h"
                                PROPERTIES GENERATED TRUE
                                # Prevent Effective C++ and unused parameter error checks being performed on generated files.
                                COMPILE_FLAGS "-Wno-unused-parameter -Wno-error=unused-parameter -Wno-error")

    # Add the generated files to our list
    SET(src ${src}
            "${outputpath}/${file_we}.pb.cc"
            "${outputpath}/${file_we}.pb.h"
            "${outputpath}/${file_we}.cpp"
            "${outputpath}/${file_we}.h")

    # If we have pybind11 also add the python bindings
    IF(pybind11_FOUND)
        SET(src ${src} "${outputpath}/${file_we}.py.cpp")
    ENDIF()

ENDFOREACH(proto)

# If we have pybind11 we need to generate our final binding class
IF(pybind11_FOUND)
    # Build our outer python binding wrapper class
    ADD_CUSTOM_COMMAND(
        OUTPUT "${CMAKE_CURRENT_BINARY_DIR}/outer_python_binding.cpp"
        COMMAND ${PYTHON_EXECUTABLE}
        ARGS "${CMAKE_CURRENT_SOURCE_DIR}/build_outer_python_binding.py"
             "${CMAKE_CURRENT_BINARY_DIR}/outer_python_binding.cpp"
             "${PROJECT_SOURCE_DIR}/${NUCLEAR_MESSAGE_DIR}"
              ${message_dependencies}
        WORKING_DIRECTORY ${message_binary_dir}
        DEPENDS "${CMAKE_CURRENT_SOURCE_DIR}/build_outer_python_binding.py" ${message_dependencies}
        COMMENT "Building outer python message binding")

    SET(src ${src} "${CMAKE_CURRENT_BINARY_DIR}/outer_python_binding.cpp")
ENDIF()

IF(src)
    # Build a library from these files
    ADD_LIBRARY(nuclear_message SHARED ${protobufs} ${src})
    SET_PROPERTY(TARGET nuclear_message PROPERTY LIBRARY_OUTPUT_DIRECTORY "${PROJECT_BINARY_DIR}/bin/lib")

    # The library uses protocol buffers
    TARGET_LINK_LIBRARIES(nuclear_message ${PROTOBUF_LIBRARIES})
    TARGET_LINK_LIBRARIES(nuclear_message ${NUClear_LIBRARIES})

    # If we have pybind11 we need to make this a python library too
    IF(pybind11_FOUND)
        TARGET_LINK_LIBRARIES(nuclear_message ${PYTHON_LIBRARIES})

        # Work out what python expects the name of the library to be
        SET(python_module_path "${PYTHON_MODULE_PREFIX}message${PYTHON_MODULE_EXTENSION}")

        # Make our NUClear python directory for including
        FILE(MAKE_DIRECTORY "${PROJECT_BINARY_DIR}/python/nuclear")

        # Create symlinks to the files
        ADD_CUSTOM_COMMAND(TARGET nuclear_message POST_BUILD
            COMMAND ${CMAKE_COMMAND} -E copy $<TARGET_FILE:nuclear_message> "${PROJECT_BINARY_DIR}/python/nuclear/${python_module_path}"
            COMMENT "Copying messages lib into python file format"
        )

        ADD_CUSTOM_COMMAND(TARGET nuclear_message POST_BUILD
            COMMAND ${CMAKE_COMMAND} -E copy "${NUCLEAR_ROLES_DIR}/module/python/nuclear.py" "${PROJECT_BINARY_DIR}/python/nuclear/nuclear.py"
            DEPENDS "${NUCLEAR_ROLES_DIR}/module/python/nuclear.py"
            COMMENT "Copying nuclear.py to python build directory"
        )

    ENDIF()

    # Add to our list of NUClear message libraries
    SET(NUCLEAR_MESSAGE_LIBRARIES nuclear_message CACHE INTERNAL "List of libraries that are built as messages" FORCE)

    # Put it in an IDE group for shared
    SET_PROPERTY(TARGET nuclear_message PROPERTY FOLDER "shared/")
ENDIF()

repository: Fastcode/NUClearExample

repo_name: Fastcode/NUClearExample


text:
DESCRIPTION = "A script that sets up WiFi on the raspberry pi"

LICENSE = "MIT"
LIC_FILES_CHKSUM = "file://${COMMON_LICENSE_DIR}/MIT;md5=0835ade698e0bcf8506ecda2f7b4f302"

S = "${WORKDIR}"

PR = "r3"

SRC_URI = "file://wlan-setup.sh"

RDEPENDS_${PN} += "bash"

do_install() {
	install -d -m 0755 ${D}/${sysconfdir}/scripts
	install -m 0755 wlan-setup.sh ${D}/${sysconfdir}/scripts/wlan-setup.sh
}

FILES_${PN} += " \
	${sysconfdir}/scripts/wlan-setup.sh \
	"


text:
SUMMARY = "Simple helloworld application"
SECTION = "examples"
LICENSE = "BSD-2-Clause"
LIC_FILES_CHKSUM = "file://${COMMON_LICENSE_DIR}/BSD-2-Clause;md5=cb641bc04cda31daea161b1bc15da69f"

SRC_URI = "file://simple-hello-world.c;subdir=sources"

S = "${WORKDIR}/sources"

DEPENDS += "codechecker-native"

EXTRANATIVEPATH += "python3-native"

inherit codechecker
CODECHECKER_ENABLED ?= "1"
CODECHECKER_REPORT_HTML ?= "1"
#CODECHECKER_REPORT_STORE ?= "1"
#CODECHECKER_REPORT_HOST ?= "http://localhost:8001/Default"

do_compile() {
    ${CC} -w simple-hello-world.c ${CFLAGS} ${LDFLAGS} -o simple-hello-world
}

do_install() {
    install -d ${D}${bindir}
    install -m 0755 simple-hello-world ${D}${bindir}
}

repository: foxtron-ch/meta-codechecker

repo_name: foxtron-ch/meta-codechecker


text:
DESCRIPTION = "Core packages required for a basic installation"
MAINTAINER = "Phil Blundell <pb@handhelds.org>"
PR = "r25"

do_package[nostamp]="1"
do_populate_staging[nostamp]="1"
do_build[nostamp]="1"

# The BOOTSTRAP_EXTRA_ variables are often manipulated by the
# MACHINE .conf files, so adjust PACKAGE_ARCH accordingly.
PACKAGE_ARCH = "${MACHINE_ARCH}"

ALLOW_EMPTY = 1
PACKAGES = "${PN}"

MODUTILS ?= "24 26"

INHIBIT_IPK_UPLOAD = "1"

def bootstrap_modutils_rdepends(d):
    import bb
    m = bb.data.getVar('MODUTILS', d, 1)
    r = []
    if '24' in m:
        r.append('modutils-depmod')
    if '26' in m:
        r.append('module-init-tools-depmod')
    return ' '.join(r)

HOTPLUG ?= "linux-hotplug"

HOTPLUG_shasta = ""
HOTPLUG_acs6000 = ""
HOTPLUG_pmhd = ""

RDEPENDS = 'base-files base-passwd busybox \
	initscripts \
	netbase sysvinit sysvinit-pidof \
	modutils-initscripts setserial\
	${HOTPLUG} \
	${BOOTSTRAP_EXTRA_RDEPENDS} \
	${@bootstrap_modutils_rdepends(d)}'

RDEPENDS_acs6000 = 'base-files base-passwd busybox \
	initscripts \
	netbase sysvinit sysvinit-pidof \
	setserial \
	${BOOTSTRAP_EXTRA_RDEPENDS} \
	'

RDEPENDS_pmhd = 'base-files base-passwd busybox \
	initscripts \
	netbase sysvinit sysvinit-pidof \
	${BOOTSTRAP_EXTRA_RDEPENDS} \
	'

RRECOMMENDS_amazon = 'portmap \
	${BOOTSTRAP_EXTRA_RRECOMMENDS}'

RRECOMMENDS = 'dropbear portmap \
	${BOOTSTRAP_EXTRA_RRECOMMENDS}'

RRECOMMENDS_tauri = 'portmap \
	${BOOTSTRAP_EXTRA_RRECOMMENDS}'

RRECOMMENDS_caesar = 'portmap \
	${BOOTSTRAP_EXTRA_RRECOMMENDS}'

RRECOMMENDS_acs6000 = 'portmap \
	${BOOTSTRAP_EXTRA_RRECOMMENDS}'

RRECOMMENDS_pmhd = 'portmap \
	${BOOTSTRAP_EXTRA_RRECOMMENDS}'

RRECOMMENDS_shasta = 'portmap \
	${BOOTSTRAP_EXTRA_RRECOMMENDS}'

LICENSE = MIT


text:
@Article{gasparini_2018,
	Author = {Gasparini, Alessandro},
	Title = {{rsimsum}: Summarise results from {Monte Carlo} simulation studies},
 	Journal = {Journal of Open Source Software},
	Volume = {3},
	Number = {26},
	Pages = {739},
	Doi = {10.21105/joss.00739},
	Url = {https://doi.org/10.21105/joss.00739},
}

repository: ellessenne/uol-thesis

repo_name: ellessenne/uol-thesis


text:
file(REMOVE_RECURSE
  "CMakeFiles/DeleteNodeInABST.dir/main.cpp.o"
  "DeleteNodeInABST.pdb"
  "DeleteNodeInABST"
)

# Per-language clean rules from dependency scanning.
foreach(lang CXX)
  include(CMakeFiles/DeleteNodeInABST.dir/cmake_clean_${lang}.cmake OPTIONAL)
endforeach()


text:
inherit pypi setuptools3

SUMMARY = "Jupyter notebook"
LICENSE = "BSD"
LIC_FILES_CHKSUM = "file://PKG-INFO;beginline=8;endline=8;md5=e910b35b0ef4e1f665b9a75d6afb7709"

SRC_URI[md5sum] = "25142b08e2ad7142b6f920bc8cc8dfeb"
SRC_URI[sha256sum] = "3e1f86076bbb7c8c207829390305a2b1fe836d471ed54be66a3b8c41e7f46cc7"

PYPI_PACKAGE_EXT = "zip"

DEPENDS += " \
        ${PYTHON_PN}-ipykernel \
        ${PYTHON_PN}-jupyter-console \
        ${PYTHON_PN}-qtconsole \
        ${PYTHON_PN}-notebook \
        ${PYTHON_PN}-ipywidgets \
        ${PYTHON_PN}-nbconvert \
        "

RDEPENDS_${PN} += " \
        ${PYTHON_PN}-ipykernel \
        ${PYTHON_PN}-jupyter-console \
        ${PYTHON_PN}-qtconsole \
        ${PYTHON_PN}-notebook \
        ${PYTHON_PN}-ipywidgets \
        ${PYTHON_PN}-nbconvert \
        ${PYTHON_PN}-sqlite3 \
        "

repository: shlomow/meta-jupyter

repo_name: shlomow/meta-jupyter


text:
@online{cloud,
  author = {Scott Moser},
  title = {Using Ubuntu cloud-images without a cloud},
  year = 2011,
  howpublished = "\url{http://ubuntu-smoser.blogspot.com/2013/02/using-ubuntu-cloud-images-without-cloud.html}",
  url = {http://ubuntu-smoser.blogspot.com/2013/02/using-ubuntu-cloud-images-without-cloud.html},
  urldate = {2011-02-11}
  note = "[Online; accessed 12-March-2018]"
}

@online{debug,
  title = {Debugging kernel and modules via gdb},
  year = 2018,
  howpublished = "\url{https://01.org/linuxgraphics/gfx-docs/drm/dev-tools/gdb-kernel-debugging.html}",
}

@online{cpu,
  title = {A brief introduction to per-cpu variables},
  year = 2014,
  howpublished = "\url{http://thinkiii.blogspot.com/2014/05/a-brief-introduction-to-per-cpu.html}",
}

@online{lab0,
  title = {CS380L: Advanced Operating Systems Lab 0},
  year = 2018,
  howpublished = "\url{https://www.cs.utexas.edu/~rossbach/380L/lab/lab0.html\#debug-config}",
}

@online{man,
  title = {urandom(4) - Linux man page},
  howpublished = "\url{https://linux.die.net/man/4/urandom}",
}

@online{urandom,
  title = {Myths about /dev/urandom},
  howpublished = "\url{https://www.2uo.de/myths-about-urandom/}",
}

@book{Bovet:2000:ULK:557706,
	author = {Bovet, Daniel Pierre and Cassetti, Marco},
	editor = {Oram, Andy},
	title = {Understanding the Linux Kernel},
	year = {2000},
	isbn = {0596000022},
	publisher = {O'Reilly \& Associates, Inc.},
	address = {Sebastopol, CA, USA},
	chapter = 9,
}

repository: xxks-kkk/Code-for-blog

repo_name: xxks-kkk/Code-for-blog

gha_language: MATLAB


text:
@article{smith_formularies_2015,
 author = {Smith, Innocent},
 journal = {Studia Liturgica},
 language = {English},
 pages = {1--15},
 title = {The formularies «Pro unitate Christianorum» in the 2002 Missale Romanum},
 volume = {45},
 year = {2015}
}

repository: innocentop/innocentsmith

repo_name: innocentop/innocentsmith


text:
@article{tao2019cancerphylo,
  title = {Improving Personalized Prediction of Cancer Prognoses with Clonal Evolution Models},
  author = {Tao, Yifeng  and  Rajaraman, Ashok  and  Cui, Xiaoyue  and  Cui, Ziyi  and  Eaton, Jesse  and  Kim, Hannah  and  Ma, Jian  and  Schwartz, Russell},
  journal = {bioRxiv},
  elocation-id = {761510},
  doi = {10.1101/761510},
  publisher = {Cold Spring Harbor Laboratory},
  URL = {https://www.biorxiv.org/content/early/2019/09/18/761510},
  eprint = {https://www.biorxiv.org/content/early/2019/09/18/761510.full.pdf},
  year = {2019},
}


text:
@article{doi:10.1021/jacs.1c04082,
author = {CHANG, WEIHAU and Tsai, IKuen  and Lin, HsinHung and Huang, Shih-Hsin and CHUNG, SZUCHI and  and  TU, IPing and Yu, Steve and Chan, Sunney I.},
title = {Copper Centers in the Cryo-EM Structure of Particulate Methane Monooxygenase Reveal the Catalytic Machinery of Methane Oxidation},
journal = {Journal of the American Chemical Society},
volume = {143},
number = {26},
pages = {9922-9932},
year = {2021},
doi = {10.1021/jacs.1c04082},
    note ={PMID: 34170126},

URL = {
        https://doi.org/10.1021/jacs.1c04082

},
eprint = {
        https://doi.org/10.1021/jacs.1c04082

}

}


text:
# Locate Lua library
# This module defines
#  LUAJIT_FOUND, if false, do not try to link to Lua
#  LUAJIT_LIBRARIES
#  LUAJIT_INCLUDE_DIR, where to find lua.h
#
# Note that the expected include convention is
#  #include "lua.h"
# and not
#  #include <lua/lua.h>
# This is because, the lua location is not standardized and may exist
# in locations other than lua/

#=============================================================================
# Copyright 2007-2009 Kitware, Inc.
#
# Distributed under the OSI-approved BSD License (the "License");
# see accompanying file Copyright.txt for details.
#
# This software is distributed WITHOUT ANY WARRANTY; without even the
# implied warranty of MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE.
# See the License for more information.
#=============================================================================
# (To distributed this file outside of CMake, substitute the full
#  License text for the above reference.)
#
# ################
# 2010 - modified for cronkite to find luajit instead of lua, as it was before.
#

FIND_PATH(LUAJIT_INCLUDE_DIR lua.h
  HINTS
  $ENV{LUAJIT_DIR}
  PATH_SUFFIXES luajit-2.0 luajit2.0 luajit luajit-2.1
  PATHS
  ~/Library/Frameworks
  /Library/Frameworks
  /usr/local
  /usr
  /sw # Fink
  /opt/local # DarwinPorts
  /opt/csw # Blastwave
  /opt
)

FIND_LIBRARY(LUAJIT_LIBRARY
  NAMES libluajit-51.a libluajit-5.1.a libluajit.a libluajit-5.1.so
  HINTS
  $ENV{LUAJIT_DIR}
  PATH_SUFFIXES lib64 lib
  PATHS
  ~/Library/Frameworks
  /Library/Frameworks
  /usr/local
  /usr
  /sw
  /opt/local
  /opt/csw
  /opt
)

IF(LUAJIT_LIBRARY)
  IF(UNIX AND NOT APPLE)
    FIND_LIBRARY(LUAJIT_MATH_LIBRARY m)
	FIND_LIBRARY(LUAJIT_DL_LIBRARY dl)
	SET( LUAJIT_LIBRARIES "${LUAJIT_LIBRARY};${LUAJIT_DL_LIBRARY};${LUAJIT_MATH_LIBRARY}" CACHE STRING "Lua Libraries")
  ELSE(UNIX AND NOT APPLE)
    SET( LUAJIT_LIBRARIES "${LUAJIT_LIBRARY}" CACHE STRING "Lua Libraries")
  ENDIF(UNIX AND NOT APPLE)
ENDIF(LUAJIT_LIBRARY)

INCLUDE(FindPackageHandleStandardArgs)
# handle the QUIETLY and REQUIRED arguments and set LUAJIT_FOUND to TRUE if
# all listed variables are TRUE
FIND_PACKAGE_HANDLE_STANDARD_ARGS(LuaJIT  DEFAULT_MSG  LUAJIT_LIBRARIES LUAJIT_INCLUDE_DIR)

MARK_AS_ADVANCED(LUAJIT_INCLUDE_DIR LUAJIT_LIBRARIES LUAJIT_LIBRARY LUAJIT_MATH_LIBRARY)

repository: iovisor/bcc

repo_name: iovisor/bcc

gha_language: C


text:
@article{CC-LAC2020a,
 address = {England},
 article-pii = {5901737},
 author = {CC-LAC},
 corporate = {Cohorts Consortium of Latin America and the Caribbean (CC-LAC)},
 doi = {10.1093/ije/dyaa073},
 electronic-issn = {1464-3685},
 electronic-publication = {20200905},
 history = {2020/09/06 06:00 [medline]},
 journal = {International journal of epidemiology},
 language = {eng},
 linking-issn = {0300-5771},
 location-id = {10.1093/ije/dyaa073 [doi]},
 month = {Sep},
 nlm-unique-id = {7802871},
 owner = {NLM},
 publication-status = {aheadofprint},
 revised = {20200905},
 source = {Int J Epidemiol. 2020 Sep 5:dyaa073. doi: 10.1093/ije/dyaa073.},
 status = {Publisher},
 subset = {IM},
 title = {Cohort profile: The Cohorts Consortium of Latin America and the Caribbean (CC-LAC).},
 title-abbreviation = {Int J Epidemiol},
 year = {2020}
}


text:
.flex {
    display: flex;
    justify-content: center;
    min-width: 0;
    flex-wrap: wrap;
}

.smallSlider {
    display: flex;
    align-items: center;
    border-radius: 20px;
    background-color: white;
    /* margin: 4px; */
}

repository: ychsue/media-dot-paper

repo_name: ychsue/media-dot-paper


text:
SUMMARY = "Asynchronous I/O readiness notification library"
DESCRIPTION = "\
	The ivykis library is a thin, portable wrapper around OS-provided \
	mechanisms such as epoll(4), kqueue(2) and poll(2). It was mainly \
	designed for building high-performance network applications, but can \
	be used in any event-driver application that uses pollable file \
	descriptors as its event sources"
HOMEPAGE = "http://libivykis.sourceforge.net/"

PR = "r0"
inherit debian-package

LICENSE = "LGPL-2.1+"
LIC_FILES_CHKSUM = "file://COPYING;md5=4fbd65380cdd255951079008b364516c"

# source format is 3.0 (quilt) but there is no debian patch
DEBIAN_QUILT_PATCHES = ""

inherit autotools

#install follow Debian jessie
do_install_append() {
	rm ${D}${libdir}/libivykis.la
	LINKLIB=$(basename $(readlink ${D}${libdir}/libivykis.so))
	chmod 0644 ${D}${libdir}/${LINKLIB}
}
#correct the sub-package name
DEBIANNAME_${PN}-dbg = "libivykis0-dbg"

repository: nghiaht-tsdv/meta-debian

repo_name: nghiaht-tsdv/meta-debian

branch_name: refs/heads/jethro


text:
@online{haskell-for-all_lenses,
	url = {http://www.haskellforall.com/2012/01/haskell-for-mainstream-programmers_28.html},
	title = Lenses,
	urldate = {2019-09-08}
}
@online{hackage-lens,
	url = {https://hackage.haskell.org/package/lens},
	title = lens,
	urldate = {2019-09-09}
}

repository: neosimsim/merkdas

repo_name: neosimsim/merkdas


text:
SUMMARY = "RISC-V Proxy Kernel"
DESCRIPTION = "RISC-V Proxy Kernel"
LICENSE = "GPLv2+"

LIC_FILES_CHKSUM = "file://${COREBASE}/meta/COPYING.GPLv2;md5=751419260aa954499f7abaabaa882bbe"

SRCREV = "2bbd8e1a1bccae13ec87882baf423abfc6ef76fd"
SRC_URI = "git://github.com/riscv/riscv-pk.git \
           file://0001-add-acinclude.m4.patch \
          "

LDFLAGS_append = " -Wl,--build-id=none"

inherit autotools

EXTRA_OECONF += "--enable-logo --with-payload=${DEPLOY_DIR_IMAGE}/${RISCV_BBL_PAYLOAD}"

INHIBIT_PACKAGE_STRIP = "1"

S = "${WORKDIR}/git"

DEPENDS = "virtual/kernel"

do_install_prepend () {
        install -d ${D}${datadir}/riscv-pk
        install -m 755 ${WORKDIR}/build/bbl ${D}${datadir}/riscv-pk
}

do_install_append() {
        rm -rf ${D}${exec_prefix}/riscv64-unknown-elf
}

do_install_append_freedom-u540() {
        ${OBJCOPY} -S -O binary --change-addresses -0x80000000 \
               ${WORKDIR}/build/bbl ${WORKDIR}/build/bbl.bin
}

do_deploy () {
        install -d ${DEPLOY_DIR_IMAGE}
        install -m 755 ${WORKDIR}/build/bbl ${DEPLOY_DIR_IMAGE}/bbl
}

do_deploy_append_freedom-u540() {
        install -m 755 ${WORKDIR}/build/bbl.bin ${DEPLOY_DIR_IMAGE}/bbl.bin
}
addtask deploy before do_build after do_install

SECURITY_CFLAGS = "${SECURITY_NOPIE_CFLAGS}"
SECURITY_LDFLAGS = ""

repository: mfischer/meta-riscv

repo_name: mfischer/meta-riscv


text:
@inproceedings{citeulike:1090639,
    abstract = {{Most current designs of information technology are based on the notion of supporting distinct tasks such as document production, email usage, and voice communication. In this paper we present empirical results that suggest that people organize their work in terms of much larger and thematically connected units of work. We present results of fieldwork observation of information workers in three different roles: analysts, software developers, and managers. We discovered that all of these types of workers experience a high level of discontinuity in the execution of their activities. People average about three minutes on a task and somewhat more than two minutes using any electronic tool or paper document before switching tasks. We introduce the concept of working spheres to explain the inherent way in which individuals conceptualize and organize their basic units of work. People worked in an average of ten different working spheres. Working spheres are also fragmented; people spend about 12 minutes in a working sphere before they switch to another. We argue that design of information technology needs to support people's continual switching between working spheres.}},
    address = {New York, NY, USA},
    author = {Gonz\'{a}lez, Victor M. and Mark, Gloria},
    booktitle = {Proceedings of the SIGCHI conference on Human factors in computing systems},
    citeulike-article-id = {1090639},
    citeulike-linkout-0 = {http://portal.acm.org/citation.cfm?id=985692.985707},
    citeulike-linkout-1 = {http://dx.doi.org/10.1145/985692.985707},
    comment = {Referenced as "shadowing with detailed recording of informant actions" - Kaptelinin, V. and Nardi, B; Acting With Technology, p. 257

* --

"...people are acting in many contexts beyond work...To manage all these activities, people deploy a diverse assemblage of both digital and nondigital technologies in offices, homes, public spaces, and mobile contexts. ...the conceptual apparatus of activity theory currently does not provide an elaborated set of concepts for the analysis of multiple activities." p. 256

"There is a need to develop analytical tools that can help understand why people at a certain moment select a particular goal and carry out a particular action, why and how they move from one action to another, how they coordinate multiple actions, why they abandon certain goals, and how they develop new objects. It is hardly possible to provide adequate technological support for the coordination, flexibility, and mobility required in modern work and nonwork activities without such an understanding. To meet this challenge of dealing with complex real-life processes, activity theory requires some new concepts. A move is needed from a preoccupation with separate activities to a focus on the complex interplay of several dynamically coordinated activities. Research methodologies that make it possible to study multiple activities empirically are also needed." [this paper amongst others] might be points of departure for the development of such a methodology. p. 257},
    doi = {10.1145/985692.985707},
    isbn = {1-58113-702-8},
    keywords = {methods, user\_research},
    location = {Vienna, Austria},
    pages = {113--120},
    posted-at = {2007-05-04 17:26:56},
    priority = {0},
    publisher = {ACM},
    series = {CHI '04},
    title = {{"Constant, constant, multi-tasking craziness": managing multiple working spheres}},
    url = {http://dx.doi.org/10.1145/985692.985707},
    year = {2004}
}

repository: FMCalisto/master-project

repo_name: FMCalisto/master-project


text:
@incollection{lio_approximation_2019,
 abstract = {This book presents outstanding contributions in an exciting, new and multidisciplinary research area: the application of formal, automated reasoning techniques to analyse complex models in systems biology and systems medicine. Automated reasoning is a field of computer science devoted to the development of algorithms that yield trustworthy answers, providing a basis of sound logical reasoning. For example, in the semiconductor industry formal verification is instrumental to ensuring that chip designs are free of defects (or "bugs"). Over the past 15 years, systems biology and systems medicine have been introduced in an attempt to understand the enormous complexity of life from a computational point of view. This has generated a wealth of new knowledge in the form of computational models, whose staggering complexity makes manual analysis methods infeasible. Sound, trusted, and automated means of analysing the models are thus required in order to be able to trust their conclusions. Above all, this is crucial to engineering safe biomedical devices and to reducing our reliance on wet-lab experiments and clinical trials, which will in turn produce lower economic and societal costs. Some examples of the questions addressed here include: Can we automatically adjust medications for patients with multiple chronic conditions? Can we verify that an artificial pancreas system delivers insulin in a way that ensures Type 1 diabetic patients never suffer from hyperglycaemia or hypoglycaemia? And lastly, can we predict what kind of mutations a cancer cell is likely to undergo? This book brings together leading researchers from a number of highly interdisciplinary areas, including: - Parameter inference from time series - Model selection - Network structure identification - Machine learning - Systems medicine - Hypothesis generation from experimental data - Systems biology, systems medicine, and digital pathology - Verification of biomedical devices "This book presents a comprehensive spectrum of model-focused analysis techniques for biological systems ...an essential resource for tracking the developments of a fast moving field that promises to revolutionize biology and medicine by the automated analysis of models and data." Prof Luca Cardelli FRS, University of Oxford},
 author = {Neupane, T. and Zhang, Z. and Madsen, C. and Zheng, H. and Myers, C.},
 booktitle = {Automated Reasoning for Systems Biology and Medicine},
 date = {2019},
 doi = {10.1007/978-3-030-17297-8},
 editor = {Liò, Pietro and Zuliani, Paolo},
 isbn = {978-3-030-17296-1},
 keywords = {stochastic logic, search},
 langid = {english},
 pages = {327--348},
 publisher = {Springer International Publishing},
 series = {Computational Biology},
 title = {Approximation techniques for stochastic analysis of biological systems},
 url = {https://www.springer.com/gp/book/9783030172961},
 urldate = {2020-09-18}
}


text:
include opie-securityplugin-blueping.inc

PV = "${OPIE_CVS_PV}"

SRC_URI = "${HANDHELDS_CVS};module=opie/noncore/securityplugins/blueping \
           ${HANDHELDS_CVS};module=opie/pics "

repository: BackupTheBerlios/openslug-svn

repo_name: BackupTheBerlios/openslug-svn