Motivational context for response inhibition influences proactive involvement of attention

Motoric inhibition is ingrained in human cognition and implicated in pervasive neurological diseases and disorders. The present electroencephalographic (EEG) study investigated proactive motivational adjustments in attention during response inhibition. We compared go-trial data from a stop-signal task, in which infrequently presented stop-signals required response cancellation without extrinsic incentives ("standard-stop"), to data where a monetary reward was posted on some stop-signals ("rewarded-stop"). A novel EEG analysis was used to directly model the covariation between response time and the attention-related N1 component. A positive relationship between response time and N1 amplitudes was found in the standard-stop context, but not in the rewarded-stop context. Simultaneously, average go-trial N1 amplitudes were larger in the rewarded-stop context. This suggests that down-regulation of go-signal-directed attention is dynamically adjusted in the standard-stop trials, but is overridden by a more generalized increase in attention in reward-motivated trials. Further, a diffusion process model indicated that behavior between contexts was the result of partially opposing evidence accumulation processes. Together these analyses suggest that response inhibition relies on dynamic and flexible proactive adjustments of low-level processes and that contextual changes can alter their interplay. This could prove to have ramifications for clinical disorders involving deficient response inhibition and impulsivity

Patterns of Cognitive Decline in Alzheimer's Dementia: An Application of Hierarchical Bayesian Multiphase Models

As the Alzheimer's disease process progresses in time measurements of cognitive functioning exhibit nonlinearity. Multiphase models were used to quantify this nonlinearity for thirty-six well characterized individuals(~12 observations per individual over ~15 years in the study) by partitioning each into a healthy aging phase and a diseased phase. This enabled us to detail both the magnitude and timing that Alzheimer's disease alters different aspects of cognitive function. Estimation of these models was done using Bayesian methods. Eight different outcomes representing three areas of memory functioning(visual, verbal, working) were used to define a pattern of cognitive decline. The earliest phase change was found to be visual memory(~6 years before diagnosis) and was followed by changes in verbal and working memory beginning roughly four years later

Wildfires identification: Semantic segmentation using support vector machine classifier

summary:This paper deals with wildfire identification in the Alaska regions as a semantic segmentation task using support vector machine classifiers. Instead of colour information represented by means of BGR channels, we proceed with a normalized reflectance over 152 days so that such time series is assigned to each pixel. We compare models associated with $\mathcal{l}1$-loss and $\mathcal{l}2$-loss functions and stopping criteria based on a projected gradient and duality gap in the presented benchmarks

Deep Learning Approach for Mapping Arctic Vegetation using Multi-Sensor Remote Sensing Fusion

Land cover datasets are essential for modeling Arctic ecosystem structure and function and for understanding land–atmosphere interactions at high spatial resolutions. However, most Arctic land cover products are generated at a coarse resolution, and finding quality satellite remote sensing datasets to produce such maps is difficult due to cloud cover, polar darkness, and poor availability of high-resolution imagery. A multi-sensor remote sensing-based deep learning approach was developed for generating high-resolution (5 m) vegetation maps for the western Alaskan Arctic on the Seward Peninsula, Alaska. The datasets from hyperspectral, multispectral, synthetic aperture radar (SAR) platforms, and terrain datasets were fused together using unsupervised and supervised classification techniques over a 343 km2 region to generate high-resolution (5 m) vegetation type maps. A unsupervised technique was developed to classify high-dimensional remote sensing datasets into cohesive clusters and a quantitative technique to add supervision to the unlabeled clusters was employed, producing a fully labeled vegetation map. Deep neural networks (DNNs) were developed using multi-sensor remote sensing datasets to map vegetation distributions using the original labels and the labels produced by the unsupervised method for training [1]. Fourteen different combinations of remote sensing imagery were analyzed to explore the optimization of multi-sensor remote sensing fusion. To validate the resulting DNN-based vegetation maps, field vegetation observations were conducted at 30 plots during the summer of 2016 and developed vegetation maps were evaluated against them for accuracy. Our analysis showed that the DNN models based on hyperspectral EO-1 Hyperion, integrated with the other remote sensing data, provided the most accurate mapping of vegetation types, increasing the average validation score from 0.56 to 0.70 based on field observation-based vegetation. REFERENCES: 1. Langford, Z. L., Kumar, J., and Hoffman, F. M., "Convolutional Neural Network Approach for Mapping Arctic Vegetation Using Multi-Sensor Remote Sensing Fusion," 2017 IEEE International Conference on Data Mining Workshops (ICDMW), New Orleans, LA, 2017, pp. 322-331. doi: 10.1109/ICDMW.2017.4

Vertical Takeoff and Landing Vehicle with Increased Cruise Efficiency

Systems, methods, and devices are provided that combine an advance vehicle configuration, such as an advanced aircraft configuration, with the infusion of electric propulsion, thereby enabling a four times increase in range and endurance while maintaining a full vertical takeoff and landing ("VTOL") and hover capability for the vehicle. Embodiments may provide vehicles with both VTOL and cruise efficient capabilities without the use of ground infrastructure. An embodiment vehicle may comprise a wing configured to tilt through a range of motion, a first series of electric motors coupled to the wing and each configured to drive an associated wing propeller, a tail configured to tilt through the range of motion, a second series of electric motors coupled to the tail and each configured to drive an associated tail propeller, and an electric propulsion system connected to the first series of electric motors and the second series of electric motors

MUSiC : a model-unspecific search for new physics in proton-proton collisions at root s=13TeV

Results of the Model Unspecific Search in CMS (MUSiC), using proton-proton collision data recorded at the LHC at a centre-of-mass energy of 13 TeV, corresponding to an integrated luminosity of 35.9 fb(-1), are presented. The MUSiC analysis searches for anomalies that could be signatures of physics beyond the standard model. The analysis is based on the comparison of observed data with the standard model prediction, as determined from simulation, in several hundred final states and multiple kinematic distributions. Events containing at least one electron or muon are classified based on their final state topology, and an automated search algorithm surveys the observed data for deviations from the prediction. The sensitivity of the search is validated using multiple methods. No significant deviations from the predictions have been observed. For a wide range of final state topologies, agreement is found between the data and the standard model simulation. This analysis complements dedicated search analyses by significantly expanding the range of final states covered using a model independent approach with the largest data set to date to probe phase space regions beyond the reach of previous general searches.Peer reviewe

Measurement of prompt open-charm production cross sections in proton-proton collisions at root s=13 TeV

The production cross sections for prompt open-charm mesons in proton-proton collisions at a center-of-mass energy of 13TeV are reported. The measurement is performed using a data sample collected by the CMS experiment corresponding to an integrated luminosity of 29 nb(-1). The differential production cross sections of the D*(+/-), D-+/-, and D-0 ((D) over bar (0)) mesons are presented in ranges of transverse momentum and pseudorapidity 4 < p(T) < 100 GeV and vertical bar eta vertical bar < 2.1, respectively. The results are compared to several theoretical calculations and to previous measurements.Peer reviewe

Observation of tW production in the single-lepton channel in pp collisions at root s=13 TeV

A measurement of the cross section of the associated production of a single top quark and a W boson in final states with a muon or electron and jets in proton-proton collisions at root s = 13 TeV is presented. The data correspond to an integrated luminosity of 36 fb(-1) collected with the CMS detector at the CERN LHC in 2016. A boosted decision tree is used to separate the tW signal from the dominant t (t) over bar background, whilst the subleading W+jets and multijet backgrounds are constrained using data-based estimates. This result is the first observation of the tW process in final states containing a muon or electron and jets, with a significance exceeding 5 standard deviations. The cross section is determined to be 89 +/- 4 (stat) +/- 12 (syst) pb, consistent with the standard model.Peer reviewe