Quantification of yield gaps in rain-fed rice, wheat, cotton and mustard in India

Rainfed farming / Crop yield / Simulation / Rice / Wheat / Cotton / Mustard / India

Dictionary Learning and Sparse Coding-based Denoising for High-Resolution Task Functional Connectivity MRI Analysis

We propose a novel denoising framework for task functional Magnetic Resonance Imaging (tfMRI) data to delineate the high-resolution spatial pattern of the brain functional connectivity via dictionary learning and sparse coding (DLSC). In order to address the limitations of the unsupervised DLSC-based fMRI studies, we utilize the prior knowledge of task paradigm in the learning step to train a data-driven dictionary and to model the sparse representation. We apply the proposed DLSC-based method to Human Connectome Project (HCP) motor tfMRI dataset. Studies on the functional connectivity of cerebrocerebellar circuits in somatomotor networks show that the DLSC-based denoising framework can significantly improve the prominent connectivity patterns, in comparison to the temporal non-local means (tNLM)-based denoising method as well as the case without denoising, which is consistent and neuroscientifically meaningful within motor area. The promising results show that the proposed method can provide an important foundation for the high-resolution functional connectivity analysis, and provide a better approach for fMRI preprocessing.Comment: 8 pages, 3 figures, MLMI201

Statistical Learning for Resting-State fMRI: Successes and Challenges

International audienceIn the absence of external stimuli, fluctuations in cerebral activity can be used to reveal intrinsic structures. Well-conditioned probabilistic models of this so-called resting-state activity are needed to support neuroscientific hypotheses. Exploring two specific descriptions of resting-state fMRI, namely spatial analysis and connectivity graphs, we discuss the progress brought by statistical learning techniques, but also the neuroscientific picture that they paint, and possible modeling pitfalls

eta_c production at the Large Hadron Collider

We have studied the production of the 1S_0 charmonium state, eta_c, at the Large Hadron Collider (LHC) in the framework of Non-Relativistic Quantum Chromodynamics (NRQCD) using heavy-quark symmetry. We find that NRQCD predicts a large production cross-section for this resonance at the LHC even after taking account the small branching ratio of eta_c into two photons. We show that it will be possible to test NRQCD through its predictions for eta_c, with the statistics that will be achieved at the early stage of the LHC, running at a center of mass energy of 7 TeV with an integrated luminosity of 100 pb^{-1}Comment: 8 pages, 2 figure

Automated information extraction from free-text EEG reports

In this study we have developed a supervised learning to automatically detect with high accuracy EEG reports that describe seizures and epileptiform discharges. We manually labeled 3,277 documents as describing one or more seizures vs no seizures, and as describing epileptiform discharges vs no epileptiform discharges. We then used Naïve Bayes to develop a system able to automatically classify EEG reports into these categories. Our system consisted of normalization techniques, extraction of key sentences, and automated feature selection using cross validation. As candidate features we used key words and special word patterns called elastic word sequences (EWS). Final feature selection was accomplished via sequential backward selection. We used cross validation to predict out of sample performance. Our automated feature selection procedure resulted in a classifier with 38 features for seizure detection, and 23 features for epileptiform discharge detection. The average [95% CI] area under the receiver operating curve was 99.05 [98.79, 99.32]% for detecting reports with seizures, and 96.15 [92.31, 100.00]% for detecting reports with epileptiform discharges. The methodology described herein greatly reduces the manual labor involved in identifying large cohorts of patients for retrospective neurophysiological studies of patients with epilepsy

Neural Network Model for Apparent Deterministic Chaos in Spontaneously Bursting Hippocampal Slices

A neural network model that exhibits stochastic population bursting is studied by simulation. First return maps of inter-burst intervals exhibit recurrent unstable periodic orbit (UPO)-like trajectories similar to those found in experiments on hippocampal slices. Applications of various control methods and surrogate analysis for UPO-detection also yield results similar to those of experiments. Our results question the interpretation of the experimental data as evidence for deterministic chaos and suggest caution in the use of UPO-based methods for detecting determinism in time-series data.Comment: 4 pages, 5 .eps figures (included), requires psfrag.sty (included

Predicting streamflow distributions and flow duration curves from landscape and climate

Characterizing the probability distribution of streamflows in catchments lacking in discharge measurements represents an attractive prospect with consequences for practical and scientific applications, in particular water resources management. In this paper, a physically-based analytic model of streamflow dynamics is combined with a set of water balance models and a geomorphological recession flow model in order to estimate streamflow probability distributions based on catchment-scale climatic and morphologic features. The models used are described and the novel parameterization approach is elaborated on. Starting from rainfall data, potential evapotranspiration and digital terrain maps, the method proved capable of capturing the statistics of observed streamflows reasonably well in 11 test catchments distributed throughout the United States, east of the rocky mountains. The method developed offers a unique approach for estimating probability distribution of streamflows where only climatic and geomorphologic features are known