Analytical models and system topologies for remote multispectral data acquisition and classification

Simple analytical models are presented of the radiometric and statistical processes that are involved in multispectral data acquisition and classification. Also presented are basic system topologies which combine remote sensing with data classification. These models and topologies offer a preliminary but systematic step towards the use of computer simulations to analyze remote multispectral data acquisition and classification systems

Tunable far infrared studies of molecular parameters in support of stratospheric measurements

Lab studies were made in support of far infrared spectroscopy of the stratosphere using the Tunable Far InfraRed (TuFIR) method of ultrahigh resolution spectroscopy and, more recently, spectroscopic and retrieval calculations performed in support of satellite-based atmospheric measurement programs: the Global Ozone Monitoring Experiment (GOME), and the SCanning Imaging Absorption spectroMeter for Atmospheric CHartographY (SCIAMACHY)

Statistical strategies for constructing health risk models with multiple pollutants and their interactions: possible choices and comparisons

Abstract Background As public awareness of consequences of environmental exposures has grown, estimating the adverse health effects due to simultaneous exposure to multiple pollutants is an important topic to explore. The challenges of evaluating the health impacts of environmental factors in a multipollutant model include, but are not limited to: identification of the most critical components of the pollutant mixture, examination of potential interaction effects, and attribution of health effects to individual pollutants in the presence of multicollinearity. Methods In this paper, we reviewed five methods available in the statistical literature that are potentially helpful for constructing multipollutant models. We conducted a simulation study and presented two data examples to assess the performance of these methods on feature selection, effect estimation and interaction identification using both cross-sectional and time-series designs. We also proposed and evaluated a two-step strategy employing an initial screening by a tree-based method followed by further dimension reduction/variable selection by the aforementioned five approaches at the second step. Results Among the five methods, least absolute shrinkage and selection operator regression performs well in general for identifying important exposures, but will yield biased estimates and slightly larger model dimension given many correlated candidate exposures and modest sample size. Bayesian model averaging, and supervised principal component analysis are also useful in variable selection when there is a moderately strong exposure-response association. Substantial improvements on reducing model dimension and identifying important variables have been observed for all the five statistical methods using the two-step modeling strategy when the number of candidate variables is large. Conclusions There is no uniform dominance of one method across all simulation scenarios and all criteria. The performances differ according to the nature of the response variable, the sample size, the number of pollutants involved, and the strength of exposure-response association/interaction. However, the two-step modeling strategy proposed here is potentially applicable under a multipollutant framework with many covariates by taking advantage of both the screening feature of an initial tree-based method and dimension reduction/variable selection property of the subsequent method. The choice of the method should also depend on the goal of the study: risk prediction, effect estimation or screening for important predictors and their interactions.http://deepblue.lib.umich.edu/bitstream/2027.42/112386/1/12940_2013_Article_691.pd

Identifying the neurophysiological effects of memory-enhancing amygdala stimulation using interpretable machine learning

BACKGROUND: Direct electrical stimulation of the amygdala can enhance declarative memory for specific events. An unanswered question is what underlying neurophysiological changes are induced by amygdala stimulation. OBJECTIVE: To leverage interpretable machine learning to identify the neurophysiological processes underlying amygdala-mediated memory, and to develop more efficient neuromodulation technologies. METHOD: Patients with treatment-resistant epilepsy and depth electrodes placed in the hippocampus and amygdala performed a recognition memory task for neutral images of objects. During the encoding phase, 160 images were shown to patients. Half of the images were followed by brief low-amplitude amygdala stimulation. For local field potentials (LFPs) recorded from key medial temporal lobe structures, feature vectors were calculated by taking the average spectral power in canonical frequency bands, before and after stimulation, to train a logistic regression classification model with elastic net regularization to differentiate brain states. RESULTS: Classifying the neural states at the time of encoding based on images subsequently remembered versus not-remembered showed that theta and slow-gamma power in the hippocampus were the most important features predicting subsequent memory performance. Classifying the post-image neural states at the time of encoding based on stimulated versus unstimulated trials showed that amygdala stimulation led to increased gamma power in the hippocampus. CONCLUSION: Amygdala stimulation induced pro-memory states in the hippocampus to enhance subsequent memory performance. Interpretable machine learning provides an effective tool for investigating the neurophysiological effects of brain stimulation

Mapping of functionalized regions on carbon nanotubes by scanning tunneling microscopy

Scanning tunneling microscopy (STM) gives us the opportunity to map the surface of functionalized carbon nanotubes in an energy resolved manner and with atomic precision. But this potential is largely untapped, mainly due to sample stability issues which inhibit reliable measurements. Here we present a simple and straightforward solution that makes away with this difficulty, by incorporating the functionalized multiwalled carbon nanotubes (MWCNT) into a few layer graphene - nanotube composite. This enabled us to measure energy resolved tunneling conductance maps on the nanotubes, which shed light on the level of doping, charge transfer between tube and functional groups and the dependence of defect creation or functionalization on crystallographic orientation.Comment: Keywords: functionalization, carbon nanotubes, few layer graphene, STM, CITS, ST

Seeing two faces together: preference formation in humans and rhesus macaques

Humans, great apes and old world monkeys show selective attention to faces depending on conspecificity, familiarity, and social status supporting the view that primates share similar face processing mechanisms. Although many studies have been done on face scanning strategy in monkeys and humans, the mechanisms influencing viewing preference have received little attention. To determine how face categories influence viewing preference in humans and rhesus macaques (Macaca mulatta), we performed two eye-tracking experiments using a visual preference task whereby pairs of faces from different species were presented simultaneously. The results indicated that viewing time was significantly influenced by the pairing of the face categories. Humans showed a strong bias towards an own-race face in an Asian–Caucasian condition. Rhesus macaques directed more attention towards non-human primate faces when they were paired with human faces, regardless of the species. When rhesus faces were paired with faces from Barbary macaques (Macaca sylvanus) or chimpanzees (Pan troglodytes), the novel species’ faces attracted more attention. These results indicate that monkeys’ viewing preferences, as assessed by a visual preference task, are modulated by several factors, species and dominance being the most influential

Evidence for MBM_B and MCM_C phases in the morphotropic phase boundary region of (1−x)[Pb(Mg1/3Nb2/3)O3]−xPbTiO3(1-x)[Pb(Mg_{1/3}Nb_{2/3})O_3]-xPbTiO_3 : A Rietveld study

We present here the results of the room temperature dielectric constant measurements and Rietveld analysis of the powder x-ray diffraction data on $(1-x)[Pb(Mg_{1/3}Nb_{2/3})O_3]-xPbTiO_3$(PMN-$x$PT) in the composition range $0.20 \leq x \leq 0.45$ to show that the morphotropic phase boundary (MPB) region contains two monoclinic phases with space groups Cm (or $M_B$ type) and Pm (or $M_C$ type) stable in the composition ranges $0.27 \leq x \leq 0.30$ and $0.31 \leq x \leq 0.34$, respectively. The structure of PMN-$x$PT in the composition ranges $0 \leq x \leq$ 0.26, and $0.35 \leq x \leq1$ is found to be rhombohedral (R3m) and tetragonal (P4mm), respectively. These results are compared with the predictions of Vanderbilt & Cohen's theory.Comment: 20 pages, 11 pdf figure

Study of nucleon resonances with electromagnetic interactions

Recent developments in using electromagnetic meson production reactions to study the structure of nucleon resonances are reviewed. Possible future works are discussed.Comment: 25 pages, 19 figure

A new class of Cu/ZnO catalysts derived from zincian georgeite precursors prepared by co-precipitation

Zincian georgeite, an amorphous copper-zinc hydroxycarbonate, has been prepared by co-precipitation using acetate salts and ammonium carbonate. Incorporation of zinc into the georgeite phase and mild ageing conditions inhibits crystallisation into zincian malachite or aurichalcite. This zincian georgeite precursor was used to prepare a Cu/ZnO catalyst, which exhibits a superior performance to a zincian malachite derived catalyst for methanol synthesis and the low temperature water-gas shift (LTS) reaction. Furthermore, the enhanced LTS activity and stability in comparison to that of a commercial Cu/ZnO/Al2O3 catalyst, indicates that the addition of alumina as a stabiliser may not be required for the zincian georgeite derived Cu/ZnO catalyst. The enhanced performance is partly attributed to the exclusion of alkali metals from the synthesis procedure, which are known to act as catalyst poisons. The effect of residual sodium on the microstructural properties of the catalyst precursor was investigated further with preparations using sodium carbonate