1,111 research outputs found
Metadynamics with Discriminants: a Tool for Understanding Chemistry
We introduce an extension of a recently published method\cite{Mendels2018} to
obtain low-dimensional collective variables for studying multiple states free
energy processes in chemical reactions. The only information needed is a
collection of simple statistics of the equilibrium properties of the reactants
and product states. No information on the reaction mechanism has to be given.
The method allows studying a large variety of chemical reactivity problems
including multiple reaction pathways, isomerization, stereo- and
regiospecificity. We applied the method to two fundamental organic chemical
reactions. First we study the \ce{S_N2} nucleophilic substitution reaction of a
\ce{Cl} in \ce{CH_2 Cl_2} leading to an understanding of the kinetic origin of
the chirality inversion in such processes. Subsequently, we tackle the problem
of regioselectivity in the hydrobromination of propene revealing that the
nature of empirical observations such as the Markovinikov's rules lies in the
chemical kinetics rather than the thermodynamic stability of the products
Stabilizing Training of Generative Adversarial Networks through Regularization
Deep generative models based on Generative Adversarial Networks (GANs) have
demonstrated impressive sample quality but in order to work they require a
careful choice of architecture, parameter initialization, and selection of
hyper-parameters. This fragility is in part due to a dimensional mismatch or
non-overlapping support between the model distribution and the data
distribution, causing their density ratio and the associated f-divergence to be
undefined. We overcome this fundamental limitation and propose a new
regularization approach with low computational cost that yields a stable GAN
training procedure. We demonstrate the effectiveness of this regularizer across
several architectures trained on common benchmark image generation tasks. Our
regularization turns GAN models into reliable building blocks for deep
learning
Implicitization of curves and (hyper)surfaces using predicted support
We reduce implicitization of rational planar parametric curves and (hyper)surfaces to linear algebra, by interpolating the coefficients of the implicit equation.
For predicting the implicit support, we focus on methods that exploit input and output structure in the sense of sparse (or toric) elimination theory, namely by computing the Newton polytope of the implicit polynomial, via sparse resultant theory.
Our algorithm works even in the presence of base points but, in this case, the implicit equation shall be obtained as a factor of the produced polynomial.
We implement our methods on Maple, and some on Matlab as well, and study their numerical stability and efficiency on several classes of curves and surfaces.
We apply our approach to approximate implicitization,
and quantify the accuracy of the approximate output,
which turns out to be satisfactory on all tested examples; we also relate our measures to Hausdorff distance.
In building a square or rectangular matrix, an important issue is (over)sampling the given curve or surface: we conclude that unitary complexes offer the best tradeoff between speed and accuracy when numerical methods are employed, namely SVD, whereas for exact kernel computation random integers is the method of choice.
We compare our prototype to existing software and find that it is rather competitive
Automated identification of Fos expression
The concentration of Fos, a protein encoded by the immediate-early gene c-fos, provides a measure of synaptic activity that may not parallel the electrical activity of neurons. Such a measure is important for the difficult problem of identifying dynamic properties of neuronal circuitries activated by a variety of stimuli and behaviours. We employ two-stage statistical pattern recognition to identify cellular nuclei that express Fos in two-dimensional sections of rat forebrain after administration of antipsychotic drugs. In stage one, we distinguish dark-stained candidate nuclei from image background by a thresholding algorithm and record size and shape measurements of these objects. In stage two, we compare performance of linear and quadratic discriminants, nearest-neighbour and artificial neural network classifiers that employ functions of these measurements to label candidate objects as either Fos nuclei, two touching Fos nuclei or irrelevant background material. New images of neighbouring brain tissue serve as test sets to assess generalizability of the best derived classification rule, as determined by lowest cross-validation misclassification rate. Three experts, two internal and one external, compare manual and automated results for accuracy assessment. Analyses of a subset of images on two separate occasions provide quantitative measures of inter- and intra-expert consistency. We conclude that our automated procedure yields results that compare favourably with those of the experts and thus has potential to remove much of the tedium, subjectivity and irreproducibility of current Fos identification methods in digital microscopy
Norwegian Sea Herring Stock Discrimination phase I (NORDISI)
There is growing concern among fishermen about the migration of North Sea herring into the Norwegian Sea. The Pelagic Freezer-trawler Association therefore commissioned IMARES to develop a technique to monitor possible catches of North Sea herring in the Norwegian Sea. This technique will use morphometric (shape) differences in herring to distinguish between Norwegian Sea spawning herring and North Sea herring. The results show that the model is able to distinguish Norwegian spring spawning herring from North Sea autumn or winter spawning herring. Overall we can conclude that even though we still have to overcome some methodological problems we are confident that this research constitutes a first step towards developing a technique to monitor catches of herring from the Norwegian Sea for Norwegian Spring spawning or other herring
Discriminant analysis of solar bright points and faculae I. Classification method and center-to-limb distribution
While photospheric magnetic elements appear mainly as Bright Points (BPs) at
the disk center and as faculae near the limb, high-resolution images reveal the
coexistence of BPs and faculae over a range of heliocentric angles. This is not
explained by a "hot wall" effect through vertical flux tubes, and suggests that
the transition from BPs to faculae needs to be quantitatively investigated. To
achieve this, we made the first recorded attempt to discriminate BPs and
faculae, using a statistical classification approach based on Linear
Discriminant Analysis(LDA). This paper gives a detailed description of our
method, and shows its application on high-resolution images of active regions
to retrieve a center-to-limb distribution of BPs and faculae. Bright "magnetic"
features were detected at various disk positions by a segmentation algorithm
using simultaneous G-band and continuum information. By using a selected sample
of those features to represent BPs and faculae, suitable photometric parameters
were identified in order to carry out LDA. We thus obtained a Center-to-Limb
Variation (CLV) of the relative number of BPs and faculae, revealing the
predominance of faculae at all disk positions except close to disk center (mu >
0.9). Although the present dataset suffers from limited statistics, our results
are consistent with other observations of BPs and faculae at various disk
positions. The retrieved CLV indicates that at high resolution, faculae are an
essential constituent of active regions all across the solar disk. We speculate
that the faculae near disk center as well as the BPs away from disk center are
associated with inclined fields
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