dataesr/scientific-paragraphs-categorization huggingface dataset:
Machine learning and environment characterization in CR-VANETs Several machine learning approaches have been applied for cognitive radio. However, as discussed in this paper, CR-VANETs can have more stringent requirements in terms of requiring fast algorithms due to fast changing environment. Moreover, there can be different types of redundancies that can be exploited such as redundancy in network statistics at a given location at the same time of a day. This is especially pertinent in the case of public transport such as buses and trains. Thus, machine learning algorithms that are more specific to CR-VANETs should be explored. Moreover, it is also important to design the knowledge database, which is consultable by machine learning algorithms for faster convergence. Some design issues are the following: which statistics to measure and store, how to divide the region into different zones, how to characterize different environments from the point of view of radio propagation (open space vs.
CR-VANETs experiment and simulation platforms Typically, the hardware experiment platforms designed for VANETs and CR-VANETs consist of regular vehicles and software-defined radios  [106, 107]  and it is complex to set up such platforms involving vehicle mechanics integrated with radios. Thus, currently, simulators are used. Using traffic simulators such as SUMO  [108]  and network simulators (NS-2/3 and OMNeT++) together enable http://jwcn. eurasipjournals. com/content/2014/1/49 designing simulators that combine vehicle mobility and communication networks. However, there is a lack of suitable simulators with features such as spectrum database, spectrum sensing and management, power spectral density, wireless interference level, and Doppler effects in order to be able to model and simulate varying topology of CR-VANETs and local wireless networks.
However, recent developments in image acquisition have made it possible to segment the hippocampus into its subfields in a fully automated fashion and this method has now been validated using ultra high resolution MRI  (Van Leemput et al. 2009). A recent small study applied this technique to 15 MCI subjects using conventional 3D T 1 weighted volume imaging and demonstrated that segmenting subfields increased sensitivity in diagnosing MCI  (Hanseeuw et al. 2011). The current study uses an extensive dataset created by combining two large cohorts from the AddNeuroMed and the Alzheimer Disease Neuroimaging Initiative (ADNI) studies to build on and extend this earlier work. In this study we aimed to (1) investigate the differences in hippocampal subfields between subject groups at baseline in a cohort of 1,069 subjects, (2) determine patterns of subfield volume loss in relation to age, gender, education, APOE e4 genotype, and neuropsychological tests from mini mental state exam (MMSE) and Alzheimer disease Assessment Score-1 (ADAS-1) scores, and (3) compare combined subfield volumes using orthogonal partial least squares (OPLS) multivariate analysis to hippocampal volume alone for discriminating between AD and healthy control (HC) subjects and predicting future conversion from MCI to AD at 12 months.
Study Data and Inclusion and Diagnostic Criteria The data used in this study were derived from two large multicentre cohorts, the AddNeuroMed and ADNI cohorts. The AddNeuroMed study is an integrated project funded by the European Union Sixth Framework Program and aims to establish and validate novel biomarkers of disease and treatment based upon in vitro and in vivo human and animal models of AD. Data was collected from six participating sites across Europe: University of Kuopio, Finland; University of Perugia, Italy; Aristotle University of Thessaloniki, Greece; King's College London, United Kingdom; University of Lodz, Poland; and University of Toulouse, France  (Lovestone et al. 2009; Simmons et al. 2009 Simmons et al. , 2011)). Data from the ADNI study was downloaded from the ADNI at the LONI website (www.
MRI Acquisition Standardized MRI data acquisition techniques were in place for AddNeuroMed and ADNI to ensure homogeneity across data acquisition sites. A detailed description of the ADNI data acquisition protocol can be found at www. loni. ucla. edu/ADNI/research/Cores/index. shtml. The imaging protocol included a 1. 5T high resolution T1 weighted sagittal 3D MP-RAGE volumes (voxel size 1. 1 9 1. 1 9 1. 2 mm 3 ), and axial proton density with T2 weighted fast spin echo images. A comprehensive quality control procedure was carried out on all MR images according to the AddNeuroMed quality control framework  (Simmons et al. 2009 (Simmons et al. , 2011)).
Statistical Analysis Statistical analysis was conducted using PASW Statistics (Version 17. 0; SPSS inc. , USA). Categorical variables were inspected using the Chi square test while continuous variables were tested using ANOVA with Bonferroni post hoc comparisons. Hippocampal subfield volumes were first analysed using MANCOVA utilising Bonferroni correction by adopting a general linear model procedure, adjusting for age, gender, education, and APOE e4 genotype as covariates. Bonferroni pairwise comparisons were performed to inspect subfield volume differences between the groups. Multiple regression analyses were conducted in R version 2. 15. 2 using the lm function from the R stats package and Bonferroni correction for multiple comparisons. Patterns of subfield volume loss were tested in relation to the effects of age, gender, education, APOE e4 genotype, and neuropsychological test scores from MMSE and ADAS-1.
In this step, all subfield measures were tested as dependent variables by disease group (AD, MCI converters, stable MCI, and HC) as a whole. Age, gender, years of education, APOE e4 genotype, and neuropsychological scores from MMSE and ADAS-1 tests was treated as independent variables for identifying subfield specific effects. 10 fold cross validation was performed by fitting linear regression models to the data, excluding 1/10th of the data in each fold and using the fitted model for prediction on data that was excluded from the fold. Hippocampal subfields were subsequently analysed using Orthogonal Partial Least Squares (OPLS)  (Wiklund et al. 2008; Trygg and Wold 2002) , a supervised multivariate data analysis method included in the software package SIMCA (Umetrics AB, Umea, Sweden). Al1 14 variables (left and right subfields) were used for OPLS analysis.
1) in R. ROC curves provide a graphical means to interpret the quality of separation and are created by plotting the true positive rate (sensitivity) versus the false positive rate (1-specificity) for various thresholds. The discriminant value of the corresponding ROC curve can be obtained by calculating the area under the curve (AUC). AUC values range from 0. 5 (random discriminations no better than chance) to 1. 0 (perfect discrimination). The pROC (Receiver Operating Characteristic) package (version 1. 5. 4) (Robin et al. 2011 ) in R was used to perform area under the curve (AUC) statistical comparisons between the combined subfield and total hippocampal volume models in the AD vs. HC and MCI converter vs MCI non-converter models.
2004). Larger datasets are more likely to contain subjects with different types of atrophy which could explain the widespread pattern of subfield volume losses reported in the present study. A significant positive effect for both MMSE and ADAS-1 was found in relation to all hippocampal subfield volumes, indicating that subjects with lower MMSE scores and higher ADAS-1 scores had lower subfield volumes. This confirms the relationship between diffuse hippocampal volume loss and poorer neuropsychological test scores  (Scheltens et al. 1992; Liu et al. 2009). Relationship Between Age, Gender, Education, APOE e4 Genotype, and Hippocampal Subfields Previous studies investigating the influence of age on hippocampal subfields have found significant negative effects associated with CA1 and CA2-3 subfield volumes  (Mueller and Weiner 2009).
Consistent with this previous work, using a larger dataset we also found a significant negative effect of age but in relation to all subfield volumes. However, years of education was only significantly associated with the right CA1 and right fimbria. Gender specific differences in the pattern of subfield volume loss were found, with female subjects demonstrating lower bilateral CA2-3, CA4-DG, fimbria, presubiculum and subiculum volumes. Previous work with AD patients suggests that gender specific differences in the rate of hippocampal volume loss are not entirely clear. For example, a previous study has reported a higher prevalence and incidence of AD in females  (Barnes et al. 2005) , whereas sex hormone differences have been suggested as an explanation of any gender divergence  (Gouras et al.
Predicting MCI Conversion Building robust classification models on new and unseen data is of great importance for accurately predicting future MCI conversion to AD. MCI predictions were performed using AD vs. HC models in the combined ADNI and AddNeuroMed cohorts as classifiers and MCI subjects as our external validation test set. This approach has been applied previously and demonstrates how larger training sets can be used to assess MCI predictions that are more balanced in terms of sensitivity and specificity  (Westman et al. 2011a). Previous studies in the neuroimaging literature utilising advanced methods of high dimensional pattern classification  (Fan et al. 2008; Misra et al. 2009) , and whole brain structural MRI  (Karas et al. 2008; Davatzikos et al. 2010 ) have demonstrated the complexity of differential atrophy patterns observed in MCI-c and MCI-s subjects.
Acknowledgments This study was supported by  InnoMed , ( Innovative Medicines in Europe ) an  Integrated  Project funded by the  European Union  of the  Sixth Framework program  priority  FP6-2004-LIFESCIHEALTH-5 ,  Life Sciences, Genomics and Biotechnology for Health. Data collection and sharing for this project was funded by the  ADNI (National Institutes of Health  Grant  U01 AG024904 ). Data collection and sharing for this project was funded by the  Alzheimer's Disease Neuroimaging Initiative (ADNI)  ( National Institutes of Health  Grant  U01 AG024904 ) and  DOD ADNI (Department of Defense  award number  W81XWH-12-2-0012 ). ADNI is funded by the  National Institute on Aging , the  National Institute of Biomedical Imaging and Bioengineering , and through generous contributions from the following:  Alzheimer's Association; Alzheimer's Drug Discovery Foundation ;  BioClinica, Inc.
2-84. 1) 86. 9 (79. 2-92. 0) 0. 90 - ADNI b 80. 2 (76. 1-83. 8) 84. 4 (78. 5-88. 9) 76. 8 (70. 8-81. 8) 0. 884 - 80. 7 (76. 6-84. 3) 88. 2 (82. 7-92. 1) 74. 6 (68. 5-79. 8) 0. 881 - a AddNeuroMed dataset used as the test set and ADNI data as the training set b ADNI data used as the test set and AddNeuroMed dataset as the training set * AddNeuroMed and ADNI cohorts used as the combined cohort model, confidence intervals presented within parenthesis CV cross validation, AUC area under the curve.
We present here a more detailed and extended GS/GI template for the whole of the Last Glacial period. It is based on a synchronization of the NGRIP, GRIP, and GISP2 ice-core records that allows the parallel analysis of all three records on a common time scale. The boundaries of the GS and GI periods are defined based on a combination of stable-oxygen isotope ratios of the ice (d 18 O, reflecting mainly local temperature) and calcium ion concentrations (reflecting mainly atmospheric dust loading) measured in the ice. The data not only resolve the well-known sequence of DansgaardeOeschger events that were first defined and numbered in the ice-core records more than two decades ago, but also better resolve a number of short-lived climatic oscillations, some defined here for the first time. Using this revised scheme, we propose a consistent approach for discriminating and naming all the significant abrupt climatic events of the Last Glacial period that are represented in the Greenland ice records.
Over the last two decades, the INTIMATE project (INTegrating Ice-core, MArine, and TErrestrial records) has proposed a series of event-stratigraphic templates based on the Greenland ice cores. These reflect the sequence of interstadials and stadials recorded in Greenland profiles, and are designed to promote the consistent naming of events, and to provide a basis for robust correlation between different proxy climate records  (Bj€ orck et al. , 1998; Walker et al. , 1999; Lowe and Hoek, 2001; Lowe et al. , 2008; Blockley et al. , 2012). The event stratigraphies divide the characteristic sequence of Greenland climate changes in the Early Holocene and Lateglacial periods into numbered Greenland Interstadials (GI) and Greenland Stadials (GS). Initially based on the stable isotope record in the GRIP core, these have subsequently incorporated data from other ice cores, and have involved the use of different time scales (Table  1 ).
, 2013)  and consideration of the depositional context  (Griggs et al. , 2014). While we acknowledge that the use of time-stratigraphic markers to test assumed synchroneity of climate signals between archives or regions is neither new nor unique, it has formed a core element of the INTIMATE programme since its inception and plays a key role in the INTIMATE protocols that are now widely endorsed. Hence, for brevity, we refer to it as the INTIMATE approach. An excellent example of the successful application of this approach is the ice-core-based analysis of the climatic links between the North Atlantic DeO events and more gradual events observed in Antarctic temperature proxy records (Antarctic Isotope Maxima, AIM (EPICA community members, 2006)) during  [Ca 2þ ] scale; see text for data sources) from GRIP (red), GISP2 (green), and NGRIP (blue) on the GICC05modelext time scale.
The dots just below the upper NGRIP depth axis show the position of the match points used to transfer the GICC05modelext time scale from NGRIP to the GRIP (red dots) and GISP2 (green dots) records. The proposed extension of the INTIMATE event stratigraphy scheme is shown with interstadials illustrated by grey shading (light grey indicates cold sub-events). In the Eemian interglacial, NGRIP data are extended by NEEM d 18 O data offset by 2‰  (NEEM community members, 2013). See main text for details on the numbering of stadial and interstadial events. Note the small time overlap between the three panels introduced to ease interpretation. Holocene GS-1 GS-1 GI-1 a b c1 c2 c3 d e GS-2. 1 (GS-2. 1a) (GS-2. 1b) (GS-2. 1c) GI 2. 1 GS 2. 2 GI 2. 2 GS-3 GI-3 GS-4 GI-4 GS- the Last Glacial period. Based on the fact that the atmosphere is relatively well-mixed on a decadal time scale,  Blunier et al.
Data This work is based on records from the NGRIP, GRIP, and GISP2 ice cores, all now available on a common time scale. For the Holocene, the GICC05 time scale has been constructed by combining data from different depth intervals in the GRIP, NGRIP and DYE-3 ice cores  (Vinther et al. , 2006) , while the GICC05 glacial section is based on annual layer counting in NGRIP down to 60 ka b2k  (Rasmussen et al. , 2006; Andersen et al. , 2006b; Svensson et al. , 2008)  extended by an ice-flow model below this  (Wolff et al. , 2010). The combined GICC05modelext time scale has been transferred to the GRIP and GISP2 ice-core records using reference horizons principally of volcanic origin  (Rasmussen et al. , 2008; Seierstad et al. , 2014). The match-point locations are shown in Fig. 1  as red (GRIP-NGRIP) and green (GISP2-NGRIP) dots just below the upper depth axis.
In the previous INTIMATE event stratigraphies, events were defined by visual inspection of d 18 O data initially re-sampled to c. 50-year resolution  (Bj€ orck et al. , 1998; Walker et al. , 1999) , and subsequently to 20-year resolution  (Lowe et al. , 2008; Blockley et al. , 2012). The temporal resolution is determined by the choice of averaging period which reflects different considerations, as follows: The lowest meaningful averaging period is determined by the resolution of the d 18 O data. The sampling resolution is typically 5 cm for the GRIP and NGRIP cores  (Johnsen et al. , 1997; North Greenland Ice Core Project members, 2004 ) and up to 20 cm in glacial sections of the GISP2 ice core  (Grootes and Stuiver, 1997; Stuiver and Grootes, 2000). In temporal terms, this corresponds to a range from approximately annual resolution in GRIP and NGRIP at the onset of the Holocene, to about 20 years for GISP2 sections from the early glacial.
Our assumption therefore, is that both d 18 O and [Ca 2þ ] in Greenland begin to change simultaneously, at least within the 20year resolution of the applied data-set. The resampling of d 18 O and [Ca 2þ ] data onto the GICC05modelext time scale has been performed in the same manner as described by  Seierstad et al. (2014) , and is only briefly described here. For GRIP, d 18 O data from  Johnsen et al. (1997)  and [Ca 2þ ] data from  Fuhrer et al. (1993)  are used; for GISP2, d 18 O data are from  Grootes and Stuiver (1997)  and  Stuiver and Grootes (2000) , and the [Ca 2þ ] data are from  Mayewski et al. (1997). For NGRIP, the d 18 O data are described in North Greenland Ice Core Project members (2004), while the [Ca 2þ ] data are from  Bigler (2004). Resampling of NGRIP data was conducted in depth space but is otherwise trivial as the GICC05modelext time scale is based on NGRIP for the entire glacial period.
To resample GRIP and GISP2 data, NGRIP depths corresponding to 20-year intervals were converted to GRIP and GISP2 depths by linear interpolation using stratigraphic matching  (Seierstad et al. , 2014). The original GRIP or GISP2 data were then resampled in depth space. The alternative of re-sampling in age space between the GICC05modelext ages of the match points of  Seierstad et al. (2014)  gives a different result because this approach implicitly assumes constant annual layer thicknesses between the match points. This is clearly an invalid assumption given the large variations in accumulation rates across stadial-interstadial boundaries and the fact that most match points are located in interstadials (due to stadial ice being more alkaline than interstadial ice, which tends to mute the volcanic signals used for synchronization).
Defining stadials and interstadials Any modification of the original interstadial numbering of  Johnsen et al. (1992)  and  Dansgaard et al. (1993)  must respect the underlying concept of numbering the main d 18 O excursions that typically involve d 18 O shifts of at least a few per mille. However, the relatively coarse stratigraphic resolution of the original isotopic dataset, based on a single record only, meant that a number of events of either short duration or smaller amplitude were not initially recognised although they may represent variability of the same nature. Starting from the top of the record presented in Fig. 1 , the first example appears around 30. 7 ka b2k. Concealed within the short-term variability of the individual d 18 O records, d 18 O values elevated about 2‰ above the stadial base-line persist in all three d 18 O records for more than two centuries.
The revised INTIMATE event stratigraphy Stadial and interstadial events have been defined by combining the results of visual inspection by several investigators of the 20-yr dataset shown in Fig. 1 , following the criteria outlined above. The onsets of interstadials are often the easier to define while the gradual transitions into stadials make it difficult to determine the precise point of termination of some interstadials. The aim has been to define the event onsets at the first clear mark of a transition, e. g. at the first data point of the steep part that clearly deviates from the base-line level preceding the transition. The subdivision of interstadials based on the isotopic signal alone is often difficult due to the relatively high short-term variability and inter-core differences and, in these cases, more emphasis was placed on the less noisy [Ca 2þ ] record.
Acknowledgements This study is a contribution to the  INTIMATE  project, supported by  INQUA  and funded as the  EU COST Action   ES0907. JBP acknowledges the support of the  Joint Institute for the Study of the Atmosphere and Ocean (JISAO) ; JISAO Contribution No. 2284. MW acknowledges the support of  Villum Fonden. IKS acknowledges the  RESOLuTION  project, part of the  EuroCLIMATE ESF-EUROCORES programme , supported by the  Danish Council for Independent Research, Natural Sciences (FNU). We thank the many individuals and organisations involved in NGRIP, GRIP, and  GISP2  logistics, drill development, drilling, as well as ice-core processing and analysis. NGRIP is directed and organized by the  Ice and Climate Research Group, Niels Bohr Institute, University of Copenhagen. It was supported by funding agencies  in Denmark (Forskningsrådet for Natur og Univers) ,  Belgium  ( Fonds National de la Recherche Scientifique ),  France (Institut Polaire Française and Institut National des Science l'Univers/CNRS) ,  Germany (AWI) ,  Iceland (Rannís), Japan (Ministry of Education, Culture, Sports, Science and Technology), Sweden (Polarforskningssek retariatet) ,  Switzerland (Der Schweizerische Nationalfonds)  and the  United States (NSF, Office of Polar Programs ).
Section 2 gives a short state of the art and presents the main artificial neural network techniques for image recognition. Section 3 details the IRF-NN architecture, weight initialization for gray-level and color images and the algorithms of the network. Section 4 discusses our concept of image receptive fields (IRFs) induced by the organization of the weights. It first gives a general interpretation of the network in terms of random and sparse sampling of a continuous scale-space representation of the input image. Then it provides some detailed information about the networks functioning and its configuration. Section 5 illustrates some IRF-NN properties with various image datasets. It shows that the internal representation forms an efficient encoding of the images. A single linear classifier can then be used to perform classification or recognition of large sets of images.
The use of the network is extremely flexible. Only a set of examples of responses is needed. These remarks apply in theory to all SLFN approaches and ELM networks. Practically, the linear regression algorithms are limited by the numerical implementation, especially for huge data matrices. It is interesting to observe that the IRF-NN works efficiently with both large internal vectors and a great amount of training images. Using the previous notation, and considering a color or graylevel image I k , the network output ŝ ∈ B m is determined by ŝ(I k ) = ŝ(Φ k ) = W. h(Φ k ) = M ∑ i=1 w i h i (Φ k ) (11) The I k. In matrix notation, H = [ h(x 1 ) T • • • h(x N ) T ] T ∈ R N×M repre- sents the activation of the M neurons for the N images according to  (8) , and S = [s 1 • • • s N ] T ∈ R N×m represents the desired responses for the N images.
The complementary study in progress aims to determine the optimal choice q opt which ensures not only extrema, but also the best distribution of the h i j. Good results for a learning set can be obtained simply by imposing a predeterminated proportion of H in the unsaturated range defined by [h min , h max ], for example a proportion of 15 or 20%. This method remains empirical and needs futher study. A stronger objective is the determination of q independently of the image set, allowing the use of any image. Two preliminary observations support this approach: performance is weakly sensitive to the value of q, and optimal values empirically identified on various datasets are not too different. The bar graph in figure  3  illustrates the proportion of neural activation components for a few images selected in various datasets.