Using FTIR spectra and pattern recognition for discrimination of tea varieties
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Void formation is an important aspect of irradiation response of metals. In situ transmission electron microscopy observation for void evolution during irradiation is an effective technique for studying void evolution. However, the amount of data collected during in situ studies drastically overwhelm the current capability for manual data analyses. Here, we used a data-driven approach where a convolutional neural network combined with greedy matching to detect and track nanovoid evolutions and migrations. This approach was able to discover the surprising phenomena of void size fluctuation and shrinkage during irradiation of Cu with pre-existing nanovoids. Phase–field simulations revealed the fundamental mechanism behind this in situ observed phenomenon of void size fluctuation.
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Antibodies (Abs) specific for CD4-induced envelope (Env) epitopes within constant region 1 and 2 (C1/C2) were induced in the RV144 vaccine trial, where antibody-dependent cellular cytotoxicity (ADCC) correlated with reduced risk of HIV-1 infection. We combined X-ray crystallography and fluorescence resonance energy transfer-fluorescence correlation spectroscopy to describe the molecular basis for epitopes of seven RV144 Abs and compared them to A32 and C11, C1/C2 Abs induced in HIV infection. Our data indicate that most vaccine Abs recognize the 7-stranded β-sandwich of gp120, a unique hybrid epitope bridging A32 and C11 binding sites. Although primarily directed at the 7-stranded β-sandwich, some accommodate the gp120 N terminus in C11-bound 8-stranded conformation and therefore recognize a broader range of CD4-triggered Env conformations. Our data also suggest that Abs of RV144 and RV305, the RV144 follow-up study, although likely initially induced by the ALVAC-HIV prime encoding full-length gp120, matured through boosting with truncated AIDSVAX gp120 variants.
MicroBooNE (the Micro Booster Neutrino Experiment) is a liquid argon time-projection chamber experiment designed for short-baseline neutrino physics, currently running at Fermilab. It aims to address the anomalous excess of low-energy events observed by the MiniBooNE experiment. In this note we present a fully automated event selection algorithm to identify charged-current electron neutrino event candidates with no pions and at least one proton in the final state (ν e CC0π-Np). The efficiency of the current selection algorithm is (46.5±0.3) %. We also show some cuts on kinematic and geometric variables which reject background events. These cuts have been validated by analyzing two event samples orthogonal to our signal. Future improvements have been identified which will improve the reconstruction efficiency, especially at low energy.
The topics to be covered in this presentation include: --Applications of triplet finding --Problems with pairing --Tiny Triplet Finder --An example of track
Computer programs for design of cloud character recognition system using TIROS photographic data
Sequential approach to digital picture processing
Fortran computer program for computing linear approximation of surface area for any given portion of digitized contour map
Statistical system using statistical decision theory for data interpretation
The ratio transformation technique is used to determine effective features as function of time in remote multiple sensing of crops and soils. The selection of quantizer parameters for a two-class recognition problem under the criteria of minimizing the probability of errors is also discussed.
Optimum channel selection among 12 channels of multispectral scanner imagery identified six as providing the best information about 11 vegetation classes and two nonvegetation classes at the Manitou Experimental Forest. Intensive preprocessing of the scanner signals was required to eliminate a serious scan angle effect. Final processing of the normalized data provided acceptable recognition results of generalized plant community types. Serious errors occurred with attempts to classify specific community types within upland grassland areas. The consideration of the convex mixtures concept (effects of amounts of live plant cover, exposed soil, and plant litter cover on apparent scene radiances) significantly improved the classification of some of the grassland classes.
The table look-up approach is based on prestoring in fast, random-access, core memory the desired answer (e.g., crop-type) for all combinations of multispectral scanner outputs from selected channels. Specifically, each set of measurements from a given point on the ground is interpreted as that address in core memory where the answer can be retrieved. Substituting the simple retrieval operation for the length y computations required by the conventional approach offers two advantages: (1) the processing time is reduced by more than an order of magnitude; (2) the multispectral scanner data can be processed by computers having minimal sophistication, complexity, and cost. These two advantages may make it possible to use an onboard computer to perform the classification function in flight.
The author has identified the following significant results. Uncontrolled photo mosaics of ERTS-1 imagery using MSS band 5 and 7 at a scale of 1:1,000,000 were used to make a preliminary surficial geology map in northcentral Alaska. Seven distinct geologic units were recognized, defined, and mapped directly on a photo mosaic. Results are closely correlated with published surficial geology maps. Eight MSS images were examined to test utility of ERTS data in studies of coastal processes and stream hydrology, and in the identification and interpretation of geomorphic features throughout Alaska. The feasibility of using ERTS-1 data to map structural lineaments is well illustrated on a mosaic of 8, band 5 MSS images. Along the northern edge of the Brooks Range one lineament can be followed the entire width of the mosaic, a distance of 225 miles. Two nearly parallel lineaments can be seen running along the northern and southern edges of the Schwatka Mountains. About 135 miles south of these two lineaments another series located in the Chitanana River region can be followed for 45 miles. These lineaments appear to be faults, and it is interesting to note that the Yukon River parallels these and appears to be structurally controlled.
The applied research discussed in this report determines and compares the correct classification percentage of the nonparametric sign test, Wilcoxon's signed rank test, and K-class classifier with the performance of the Bayes classifier. The performance is determined for data which have Gaussian, Laplacian and Rayleigh probability density functions. The correct classification percentage is shown graphically for differences in modes and/or means of the probability density functions for four, eight and sixteen samples. The K-class classifier performed very well with respect to the other classifiers used. Since the K-class classifier is a nonparametric technique, it usually performed better than the Bayes classifier which assumes the data to be Gaussian even though it may not be. The K-class classifier has the advantage over the Bayes in that it works well with non-Gaussian data without having to determine the probability density function of the data. It should be noted that the data in this experiment was always unimodal.
An algorithm is presented for construction of a separation function in the case of two non-intersecting sets in a Hilbert space. An example illustrating the algorithm usage is given and the results are discussed.
The properties of a linear discriminant function are discussed for the case of arbitrary distributions with equal covariance matrices. Using two examples, a comparison is made showing how the difference of the means relates to the covariance matrices.