792 resultados para Jones, Michael


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Selostus: Kohonneen hiilidioksidipitoisuuden, lämpötilan ja kuivuuden vaikutus nurmikasveihin

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Kirjallisuusarvostelu

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This report explores how recurrent neural networks can be exploited for learning high-dimensional mappings. Since recurrent networks are as powerful as Turing machines, an interesting question is how recurrent networks can be used to simplify the problem of learning from examples. The main problem with learning high-dimensional functions is the curse of dimensionality which roughly states that the number of examples needed to learn a function increases exponentially with input dimension. This thesis proposes a way of avoiding this problem by using a recurrent network to decompose a high-dimensional function into many lower dimensional functions connected in a feedback loop.

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We describe a method for modeling object classes (such as faces) using 2D example images and an algorithm for matching a model to a novel image. The object class models are "learned'' from example images that we call prototypes. In addition to the images, the pixelwise correspondences between a reference prototype and each of the other prototypes must also be provided. Thus a model consists of a linear combination of prototypical shapes and textures. A stochastic gradient descent algorithm is used to match a model to a novel image by minimizing the error between the model and the novel image. Example models are shown as well as example matches to novel images. The robustness of the matching algorithm is also evaluated. The technique can be used for a number of applications including the computation of correspondence between novel images of a certain known class, object recognition, image synthesis and image compression.

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We describe a technique for finding pixelwise correspondences between two images by using models of objects of the same class to guide the search. The object models are 'learned' from example images (also called prototypes) of an object class. The models consist of a linear combination ofsprototypes. The flow fields giving pixelwise correspondences between a base prototype and each of the other prototypes must be given. A novel image of an object of the same class is matched to a model by minimizing an error between the novel image and the current guess for the closest modelsimage. Currently, the algorithm applies to line drawings of objects. An extension to real grey level images is discussed.

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We had previously shown that regularization principles lead to approximation schemes, as Radial Basis Functions, which are equivalent to networks with one layer of hidden units, called Regularization Networks. In this paper we show that regularization networks encompass a much broader range of approximation schemes, including many of the popular general additive models, Breiman's hinge functions and some forms of Projection Pursuit Regression. In the probabilistic interpretation of regularization, the different classes of basis functions correspond to different classes of prior probabilities on the approximating function spaces, and therefore to different types of smoothness assumptions. In the final part of the paper, we also show a relation between activation functions of the Gaussian and sigmoidal type.

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The Ocean Sampling Day (OSD) is a simultaneous sampling campaign of the world's oceans which took place (for the first time) on the summer solstice (June 21st) in the year 2014. These cumulative samples, related in time, space and environmental parameters, provide insights into fundamental rules describing microbial diversity and function and contribute to the blue economy through the identification of novel, ocean-derived biotechnologies. We see OSD data as a reference data set for generations of experiments to follow in the coming decade. The present data set includes a description of each sample collected during the Ocean Sampling Day 2014 and provides contextual environmental data measured concurrently with the collection of water samples for genomic analyses.

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As a step toward understanding their functional role, the low frequency vibrational motions (<300 cm−1) that are coupled to optical excitation of the primary donor bacteriochlorophyll cofactors in the reaction center from Rhodobacter sphaeroides were investigated. The pattern of hydrogen-bonding interaction between these bacteriochlorophylls and the surrounding protein was altered in several ways by mutation of single amino acids. The spectrum of low frequency vibrational modes identified by femtosecond coherence spectroscopy varied strongly between the different reaction center complexes, including between different mutants where the pattern of hydrogen bonds was the same. It is argued that these variations are primarily due to changes in the nature of the individual modes, rather than to changes in the charge distribution in the electronic states involved in the optical excitation. Pronounced effects of point mutations on the low frequency vibrational modes active in a protein-cofactor system have not been reported previously. The changes in frequency observed indicate a strong involvement of the protein in these nuclear motions and demonstrate that the protein matrix can increase or decrease the fluctuations of the cofactor along specific directions.

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Anionic lipids play a variety of key roles in biomembrane function, including providing the immediate environment for the integral membrane proteins that catalyze photosynthetic and respiratory energy transduction. Little is known about the molecular basis of these lipid–protein interactions. In this study, x-ray crystallography has been used to examine the structural details of an interaction between cardiolipin and the photoreaction center, a key light-driven electron transfer protein complex found in the cytoplasmic membrane of photosynthetic bacteria. X-ray diffraction data collected over the resolution range 30.0–2.1 Å show that binding of the lipid to the protein involves a combination of ionic interactions between the protein and the lipid headgroup and van der Waals interactions between the lipid tails and the electroneutral intramembrane surface of the protein. In the headgroup region, ionic interactions involve polar groups of a number of residues, the protein backbone, and bound water molecules. The lipid tails sit along largely hydrophobic grooves in the irregular surface of the protein. In addition to providing new information on the immediate lipid environment of a key integral membrane protein, this study provides the first, to our knowledge, high-resolution x-ray crystal structure for cardiolipin. The possible significance of this interaction between an integral membrane protein and cardiolipin is considered.

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A pathway of electron transfer is described that operates in the wild-type reaction center (RC) of the photosynthetic bacterium Rhodobacter sphaeroides. The pathway does not involve the excited state of the special pair dimer of bacteriochlorophylls (P*), but instead is driven by the excited state of the monomeric bacteriochlorophyll (BA*) present in the active branch of pigments along which electron transfer occurs. Pump-probe experiments were performed at 77 K on membrane-bound RCs by using different excitation wavelengths, to investigate the formation of the charge separated state P+HA−. In experiments in which P or BA was selectively excited at 880 nm or 796 nm, respectively, the formation of P+HA− was associated with similar time constants of 1.5 ps and 1.7 ps. However, the spectral changes associated with the two time constants are very different. Global analysis of the transient spectra shows that a mixture of P+BA− and P* is formed in parallel from BA* on a subpicosecond time scale. In contrast, excitation of the inactive branch monomeric bacteriochlorophyll (BB) and the high exciton component of P (P+) resulted in electron transfer only after relaxation to P*. The multiple pathways for primary electron transfer in the bacterial RC are discussed with regard to the mechanism of charge separation in the RC of photosystem II from higher plants.

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Front Row: Josh Trexler, Andy Potts, Michael O'Connor, Jr., Raymond Papa, Dino Dovellos, head coach Jon Urbanchek.

Second Row: Chris Thompson, Joe Palmer, John Reich, G.J. Zann, Francisco Suriano Siu.

Third Row: Scott Werner, manager Ben Jones, Michael McWha, Chris Laskowski, Jay Zawacki, David Stephens

Back Row: asst. coach Eric Namesnik, Steven Williams, Tom Malchow, Derya Buyukuncu, Brett Wilmot, Owen von Richter, Scott Meyer, Guillermo Ramis, Jeff Flermoen.

Not Pictured: Carlos Castillo, volunteer asst. coach Tim Chaix, diving coach Dick Kimball, student trainer John Schoenwetter, Nathan Shepard.