2,360 research outputs found
Intermolecular correlations are necessary to explain diffuse scattering from protein crystals
Conformational changes drive protein function, including catalysis,
allostery, and signaling. X-ray diffuse scattering from protein crystals has
frequently been cited as a probe of these correlated motions, with significant
potential to advance our understanding of biological dynamics. However, recent
work challenged this prevailing view, suggesting instead that diffuse
scattering primarily originates from rigid body motions and could therefore be
applied to improve structure determination. To investigate the nature of the
disorder giving rise to diffuse scattering, and thus the potential applications
of this signal, a diverse repertoire of disorder models was assessed for its
ability to reproduce the diffuse signal reconstructed from three protein
crystals. This comparison revealed that multiple models of intramolecular
conformational dynamics, including ensemble models inferred from the Bragg
data, could not explain the signal. Models of rigid body or short-range
liquid-like motions, in which dynamics are confined to the biological unit,
showed modest agreement with the diffuse maps, but were unable to reproduce
experimental features indicative of long-range correlations. Extending a model
of liquid-like motions to include disorder across neighboring proteins in the
crystal significantly improved agreement with all three systems and highlighted
the contribution of intermolecular correlations to the observed signal. These
findings anticipate a need to account for intermolecular disorder in order to
advance the interpretation of diffuse scattering to either extract biological
motions or aid structural inference.Comment: 12 pages, 5 figures (not including Supplementary Information
Coupling different methods for overcoming the class imbalance problem
Many classification problems must deal with imbalanced datasets where one class \u2013 the majority class \u2013 outnumbers the other classes. Standard classification methods do not provide accurate predictions in this setting since classification is generally biased towards the majority class. The minority classes are oftentimes the ones of interest (e.g., when they are associated with pathological conditions in patients), so methods for handling imbalanced datasets are critical.
Using several different datasets, this paper evaluates the performance of state-of-the-art classification methods for handling the imbalance problem in both binary and multi-class datasets. Different strategies are considered, including the one-class and dimension reduction approaches, as well as their fusions. Moreover, some ensembles of classifiers are tested, in addition to stand-alone classifiers, to assess the effectiveness of ensembles in the presence of imbalance. Finally, a novel ensemble of ensembles is designed specifically to tackle the problem of class imbalance: the proposed ensemble does not need to be tuned separately for each dataset and outperforms all the other tested approaches.
To validate our classifiers we resort to the KEEL-dataset repository, whose data partitions (training/test) are publicly available and have already been used in the open literature: as a consequence, it is possible to report a fair comparison among different approaches in the literature.
Our best approach (MATLAB code and datasets not easily accessible elsewhere) will be available at https://www.dei.unipd.it/node/2357
ECOM: a fast and accurate solver for toroidal axisymmetric MHD equilibria
We present ECOM (Equilibrium solver via COnformal Mapping), a fast and
accurate fixed boundary solver for toroidally axisymmetric magnetohydrodynamic
equilibria with or without a toroidal flow. ECOM combines conformal mapping and
Fourier and integral equation methods on the unit disk to achieve exponential
convergence for the poloidal flux function as well as its first and second
partial derivatives. As a consequence of its high order accuracy, for dense
grids and tokamak-like elongations ECOM computes key quantities such as the
safety factor and the magnetic shear with higher accuracy than the finite
element based code CHEASE [H. L\"utjens \textit{et al.}, Computer physics
communications 97, 219 (1996)] at equal run time. ECOM has been developed to
provide equilibrium quantities and details of the flux contour geometry as
inputs to stability, wave propagation and transport codes.Comment: 25 pages, 9 figure
Non-Uniform Time Sampling for Multiple-Frequency Harmonic Balance Computations
A time-domain harmonic balance method for the analysis of almost-periodic (multi-harmonics) flows is presented. This method relies on Fourier analysis to derive an efficient alternative to classical time marching schemes for such flows. It has recently received significant attention, especially in the turbomachinery field where the flow spectrum is essentially a combination of the blade passing frequencies. Up to now, harmonic balance methods have used a uniform time sampling of the period of interest, but in the case of several frequencies, non-necessarily multiple of each other, harmonic balance methods can face stability issues due to a bad condition number of the Fourier operator. Two algorithms are derived to find a non-uniform time sampling in order to minimize this condition number. Their behavior is studied on a wide range of frequencies, and a model problem of a 1D flow with pulsating outlet pressure, which enables to prove their efficiency. Finally, the flow in a multi-stage axial compressor is analyzed with different frequency sets. It demonstrates the stability and robustness of the present non-uniform harmonic balance method regardless of the frequency set
On the class overlap problem in imbalanced data classification.
Class imbalance is an active research area in the machine learning community. However, existing and recent literature showed that class overlap had a higher negative impact on the performance of learning algorithms. This paper provides detailed critical discussion and objective evaluation of class overlap in the context of imbalanced data and its impact on classification accuracy. First, we present a thorough experimental comparison of class overlap and class imbalance. Unlike previous work, our experiment was carried out on the full scale of class overlap and an extreme range of class imbalance degrees. Second, we provide an in-depth critical technical review of existing approaches to handle imbalanced datasets. Existing solutions from selective literature are critically reviewed and categorised as class distribution-based and class overlap-based methods. Emerging techniques and the latest development in this area are also discussed in detail. Experimental results in this paper are consistent with existing literature and show clearly that the performance of the learning algorithm deteriorates across varying degrees of class overlap whereas class imbalance does not always have an effect. The review emphasises the need for further research towards handling class overlap in imbalanced datasets to effectively improve learning algorithms’ performance
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