update 20161214

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 mysite/settings/local.py
 
-papers/name_first.py
-papers/name_last.py
-papers/unique_first.py
-papers/unique_last.py
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 # Distribution / packaging

arxiv/update_log/2016-12-05-05-08-45.txt

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+[u'Voxelwise nonlinear regression toolbox for neuroimage analysis: Application to aging and neurodegenerative disease modeling', ['Santi Puch', 'Asier Aduriz', 'Adria Casamitjana', 'Veronica Vilaplana', 'Paula Petrone', 'Gregory Operto', 'Raffaele Cacciaglia', 'Stavros Skouras', 'Carles Falcon', 'Jose Luis Molinuevo', 'Juan Domingo Gispert'], u'2016-12-02', u"This paper describes a new neuroimaging analysis toolbox that allows for the modeling of nonlinear effects at the voxel level, overcoming limitations of methods based on linear models like the GLM. We illustrate its features using a relevant example in which distinct nonlinear trajectories of Alzheimer's disease related brain atrophy patterns were found across the full biological spectrum of the disease.", u'http://arxiv.org/abs/1612.00667v1', ['Neurons and Cognition'], []]
+[u'Bayesian Population Receptive Field Modelling', ['Peter Zeidman', 'Edward Harry Silson', 'Dietrich Samuel Schwarzkopf', 'Chris Ian Baker', 'Will Penny'], u'2016-12-02', u'We introduce a probabilistic (Bayesian) framework and associated software toolbox for mapping population receptive fields (pRFs) based on fMRI data. This generic approach is intended to work with stimuli of any dimension and is demonstrated and validated in the context of 2D retinotopic mapping. The framework enables the experimenter to specify generative (encoding) models of fMRI timeseries, in which experimental manipulations enter a pRF model of neural activity, which in turns drives a nonlinear model of neurovascular coupling and Blood Oxygenation Level Dependent (BOLD) response. The neuronal and haemodynamic parameters are estimated together on a voxel-by-voxel or region-of-interest basis using a Bayesian estimation algorithm (variational Laplace). This offers several novel contributions to receptive field modelling. The variance / covariance of parameters are estimated, enabling receptive fields to be plotted while properly representing uncertainty about pRF size and location. Variability in the haemodynamic response across the brain is accounted for. Furthermore, the framework introduces formal hypothesis testing to pRF analysis, enabling competing models to be evaluated based on their model evidence (approximated by the variational free energy), which represents the optimal tradeoff between accuracy and complexity. Using simulations and empirical data, we found that parameters typically used to represent pRF size and neuronal scaling are strongly correlated, which should be taken into account when making inferences. We used the framework to compare the evidence for six variants of pRF model using 7T functional MRI data and we found a circular Difference of Gaussians (DoG) model to be the best explanation for our data overall. We hope this framework will prove useful for mapping stimulus spaces with any number of dimensions onto the anatomy of the brain.', u'http://arxiv.org/abs/1612.00644v1', ['Neurons and Cognition'], []]
+[u'Using Brain Connectivity Measure of EEG Synchrostates for Discriminating Typical and Autism Spectrum Disorder', ['Wasifa Jamal', 'Saptarshi Das', 'Koushik Maharatna', 'Doga Kuyucu', 'Federico Sicca', 'Lucia Billeci', 'Fabio Apicella', 'Filippo Muratori'], u'2016-11-29', u'In this paper we utilized the concept of stable phase synchronization topography - synchrostates - over the scalp derived from EEG recording for formulating brain connectivity network in Autism Spectrum Disorder (ASD) and typically-growing children. A synchronization index is adapted for forming the edges of the connectivity graph capturing the stability of each of the synchrostates. Such network is formed for 11 ASD and 12 control group children. Comparative analyses of these networks using graph theoretic measures show that children with autism have a different modularity of such networks from typical children. This result could pave the way to a new modality for possible identification of ASD from non-invasively recorded EEG data.', u'http://arxiv.org/abs/1611.09891v1', ['Neurons and Cognition'], []]
+[u'Existence of Millisecond-order Stable States in Time-Varying Phase Synchronization Measure in EEG Signals', ['Wasifa Jamal', 'Saptarshi Das', 'Koushik Maharatna'], u'2016-11-29', u'In this paper, we have developed a new measure of understanding the temporal evolution of phase synchronization for EEG signals using cross-electrode information. From this measure it is found that there exists a small number of well-defined phase-synchronized states, each of which is stable for few milliseconds during the execution of a face perception task. We termed these quasi-stable states as synchrostates. We used k-means clustering algorithms to estimate the optimal number of synchrostates from 100 trials of EEG signals over 128 channels. Our results show that these synchrostates exist consistently in all the different trials. It is also found that from the onset of the stimulus, switching between these synchrostates results in well-behaved temporal sequence with repeatability which may be indicative of the dynamics of the cognitive process underlying that task. Therefore these synchrostates and their temporal switching sequences may be used as a new measure of the stability of phase synchrony and information exchange between different regions of a human brain.', u'http://arxiv.org/abs/1611.09888v1', ['Neurons and Cognition'], []]
+[u'EEG-assisted Modulation of Sound Sources in the Auditory Scene', ['Marzieh Haghighi', 'Mohammad Moghadamfalahi', 'Murat Akcakaya', 'Deniz Erdogmus'], u'2016-11-26', u'Noninvasive EEG (electroencephalography) based auditory attention detection could be useful for improved hearing aids in the future. This work is a preliminary attempt to investigate the feasibility of online classification of auditory attention using a noninvasive EEG-based brain interface. Proposed online system modulates the upcoming sound sources through gain adaptation which combines decisions from a classifier trained on offline calibration data. For decision making, features are extracted based on cross correlation of EEG and speech envelope at specific time lags that were shown to be useful to discriminate attention in the competing speakers scenario. Attention detection performance of the model and its application to online source modulation is reported in the form of AUCs. On average, for attended speaker classification in training session and application of learned model on online session, the presented approach yields 88% and 82% AUC values respectively, using 20 seconds of data for each decision. Keywords: auditory BCI, cocktail party problem, auditory attention classification', u'http://arxiv.org/abs/1612.00703v1', ['Neurons and Cognition'], []]
+[u'Primordial Sex Facilitates the Emergence of Evolution', ['Sam Sinai', 'Jason Olejarz', 'Iulia A. Neagu', 'Martin A. Nowak'], u'2016-12-02', u'Compartments are ubiquitous throughout biology, yet their importance stretches back to the origin of cells. In the context of origin of life, we assume that a protocell, a compartment enclosing functional components, requires $N$ components to be evolvable. We take interest in the timescale in which a minimal evolvable protocell is produced. We show that when protocells fuse and share information, the time to produce an evolvable protocell scales algebraically in $N$, in contrast to an exponential scaling in the absence of fusion. We discuss the implications of this result for origins of life, as well as other biological processes.', u'http://arxiv.org/abs/1612.00825v1', ['Populations and Evolution'], []]
+[u'A SICA compartmental model in epidemiology with application to HIV/AIDS in Cape Verde', ['Cristiana J. Silva', 'Delfim F. M. Torres'], u'2016-12-02', u'We propose a new mathematical model for the transmission dynamics of the human immunodeficiency virus (HIV). Global stability of the unique endemic equilibrium is proved. Then, based on data provided by the "Progress Report on the AIDS response in Cape Verde 2015", we calibrate our model to the cumulative cases of infection by HIV and AIDS from 1987 to 2014 and we show that our model predicts well such reality. Finally, a sensitivity analysis is done for the case study in Cape Verde. We conclude that the goal of the United Nations to end the AIDS epidemic by 2030 is a nontrivial task.', u'http://arxiv.org/abs/1612.00732v1', ['Populations and Evolution'], []]
+[u'Chaos in the Y-chromosome evolution?', ['Matheus P. Lobo', 'Edison T. Franco', 'Nilo M. Sotomayor', 'Felipe R. Costa'], u'2016-12-01', u'The Y-chromosome degeneration is still an intriguing mechanism and comprises the very origin of sex. We present a coupled version of the well known logistic map and the logistic equation describing the evolution of XY chromosomes. Although chaos was found in X, Y chromosomes do not evolve chaotically. A mathematical constraint is shown as the responsible for this behaviour. In addition, analytical solutions are presented for the differential equations herein.', u'http://arxiv.org/abs/1612.00463v1', ['Populations and Evolution'], []]
+[u'Survival Prediction with Limited Features: a Top Performing Approach from the DREAM ALS Stratification Prize4Life Challenge', ['Christoph Kurz'], u'2016-12-02', u'Survival prediction with small sets of features is a highly relevant topic for decision-making in clinical practice. I describe a method for predicting survival of amyotrophic lateral sclerosis (ALS) patients that was developed as a submission to the DREAM ALS Stratification Prize4Life Challenge held in summer 2015 to find the most accurate prediction of ALS progression and survival. ALS is a neurodegenerative disease with very heterogeneous survival times. Based on patient data from two national registries, solvers were asked to predict survival for three different time intervals, which was then evaluated on undisclosed information from additional data. I describe methods used to generate new features from existing ones from longitudinal data, selecting the most predictive features, and developing the best survival model. I show that easily obtainable engineered features can significantly improve prediction and could be incorporated into clinical practice. Furthermore, my prediction model confirms previous reports suggesting that past disease progression measured by the ALSFRS (ALS functional rating scale score), time since disease onset, onset site, and age are strong predictors for survival. Regarding prediction accuracy, this approach ranked second.', u'http://arxiv.org/abs/1612.00664v1', ['Quantitative Methods'], []]
+[u'A Noise-Filtering Approach for Cancer Drug Sensitivity Prediction', ['Turki Turki', 'Zhi Wei'], u'2016-12-02', u"Accurately predicting drug responses to cancer is an important problem hindering oncologists' efforts to find the most effective drugs to treat cancer, which is a core goal in precision medicine. The scientific community has focused on improving this prediction based on genomic, epigenomic, and proteomic datasets measured in human cancer cell lines. Real-world cancer cell lines contain noise, which degrades the performance of machine learning algorithms. This problem is rarely addressed in the existing approaches. In this paper, we present a noise-filtering approach that integrates techniques from numerical linear algebra and information retrieval targeted at filtering out noisy cancer cell lines. By filtering out noisy cancer cell lines, we can train machine learning algorithms on better quality cancer cell lines. We evaluate the performance of our approach and compare it with an existing approach using the Area Under the ROC Curve (AUC) on clinical trial data. The experimental results show that our proposed approach is stable and also yields the highest AUC at a statistically significant level.", u'http://arxiv.org/abs/1612.00525v1', ['Genomics'], []]