Linear scaling electronic structure calculations and accurate sampling with noisy forces

Numerical simulations based on electronic structure calculations are finding ever growing applications in many areas of physics. A major limiting factor is however the cubic scaling of the algorithms used. Building on previous work [F. R. Krajewski and M. Parrinello, Phys.Rev. B71, 233105 (2005)] we introduce a novel statistical method for evaluating the inter-atomic forces which scales linearly with system size and is applicable also to metals. The method is based on exact decomposition of the fermionic determinant and on a mapping onto a field theoretical expression. We solve exactly the problem of sampling the Boltzmann distribution with noisy forces. This novel approach can be used in such diverse fields as quantum chromodynamics, quantum Monte Carlo or colloidal physics.Comment: 5 pages, 2 figure

Multiscaling analysis of high resolution space-time lidar-rainfall

In this study, we report results from scaling analysis of 2.5 m spatial and 1 s temporal resolution lidar-rainfall data. The high resolution spatial and temporal data from the same observing system allows us to investigate the variability of rainfall at very small scales ranging from few meters to ~1 km in space and few seconds to ~30 min in time. The results suggest multiscaling behaviour in the lidar-rainfall with the scaling regime extending down to the resolution of the data. The results also indicate the existence of a space-time transformation of the form <i>t</i>~<i>L<sup>z</sup></i> at very small scales, where <i>t</i> is the time lag, <i>L</i> is the spatial averaging scale and <i>z</i> is the dynamic scaling exponent

Extensible Structure-Informed Prediction of Formation Energy with Improved Accuracy and Usability employing Neural Networks

In the present paper, we introduce a new neural network-based tool for the prediction of formation energies of atomic structures based on elemental and structural features of Voronoi-tessellated materials. We provide a concise overview of the connection between the machine learning and the true material-property relationship, how to improve the generalization accuracy by reducing overfitting, and how new data can be incorporated into the model to tune it to a specific material system. The present work resulted in three final models optimized for (1) highest test accuracy on the Open Quantum Materials Database (OQMD), (2) performance in the discovery of new materials, and (3) performance at a low computational cost. On a test set of 21,800 compounds randomly selected from OQMD, they achieve a mean average error (MAE) of 28, 40, and 42 meV/atom, respectively. The second model provides better predictions on materials far from ones reported in OQMD, while the third reduces the computational cost by a factor of 8. We collect our results in a new open-source tool called SIPFENN (Structure-Informed Prediction of Formation Energy using Neural Networks). SIPFENN not only improves the accuracy beyond existing models but also ships in a ready-to-use form with pre-trained neural networks and a GUI interface. By virtue of this, it can be included in DFT calculations routines at nearly no cost

Comparative rainfall data analysis from two vertically pointing radars, an optical disdrometer, and a rain gauge

The authors present results of a comparative analysis of rainfall data from several ground-based instruments. The instruments include two vertically pointing Doppler radars, S-band and X-band, an optical disdrometer, and a tipping-bucket rain gauge. All instruments were collocated at the Iowa City Municipal Airport in Iowa City, Iowa, for a period of several months. The authors used the rainfall data derived from the four instruments to first study the temporal variability and scaling characteristics of rainfall and subsequently assess the instrumental effects on these derived properties. The results revealed obvious correspondence between the ground and remote sensors, which indicates the significance of the instrumental effect on the derived properties

Load Magnitude and Locomotion Strategy Alters Knee Mechanics in Recruit-Aged Women

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Influence of Combinatorial Histone Modifications on Antibody and Effector Protein Recognition

SummaryIncreasing evidence suggests that histone posttranslational modifications (PTMs) function in a combinatorial fashion to regulate the diverse activities associated with chromatin. Yet how these patterns of histone PTMs influence the adapter proteins known to bind them is poorly understood. In addition, how histone-specific antibodies are influenced by these same patterns of PTMs is largely unknown. Here we examine the binding properties of histone-specific antibodies and histone-interacting proteins using peptide arrays containing a library of combinatorially modified histone peptides. We find that modification-specific antibodies are more promiscuous in their PTM recognition than expected and are highly influenced by neighboring PTMs. Furthermore, we find that the binding of histone-interaction domains from BPTF, CHD1, and RAG2 to H3 lysine 4 trimethylation is also influenced by combinatorial PTMs. These results provide further support for the histone code hypothesis and raise specific concerns with the quality of the currently available modification-specific histone antibodies

Modelling of the crystallization front – particles interactions in ZnAl/(SiC)p composites

The presented work focuses on solid particle interactions with the moving crystallization front during a solidification of the metal matrix composite. The current analyses were made for silicon carbide particles and ZnAl alloy with different additions of aluminium. It was found, that the chemical composition of the metal matrix influences the behaviour of SiC particles. At the same time calculations of the forces acting on a single particle near the crystallization front were performed. For each alloy type the critical conditions that determine whether particle will be absorbed or pushed, were specified

Determination of an optimal response cut-off able to predict progression-free survival in patients with well-differentiated advanced pancreatic neuroendocrine tumours treated with sunitinib: an alternative to the current RECIST-defined response.

BACKGROUND: Sunitinib prolongs progression-free survival (PFS) in patients with advanced pancreatic neuroendocrine tumours (pNET). Response Evaluation Criteria in Solid Tumors (RECIST)-defined partial responses (PR; classically defined as ⩾30% size decrease from baseline) are infrequent. METHODS: Individual data of pNET patients from the phase II [NCT00056693] and pivotal phase III [NCT00428597] trials of sunitinib were analysed in this investigator-initiated, post hoc study. The primary objective was to determine the optimal RECIST (v.1.0) response cut-off value to identify patients who were progression-free at 11 months (median PFS in phase III trial); and the most informative time-point (highest area under the curve (AUC) by receiver operating characteristic (ROC) analysis and logistic regression) for prediction of benefit (PFS) from sunitinib. RESULTS: Data for 237 patients (85 placebo; 152 sunitinib (n=66.50 mg \u274-weeks on/2-weeks off\u27 schedule; n=86 \u2737.5 mg continuous daily dosing (CDD)\u27)) and 788 scans were analysed. The median PFS for sunitinib and placebo were 9.3 months (95% CI 7.6-12.2) and 5.4 months (95% CI 3.5-6.01), respectively (hazard ratio (HR) 0.43 (95% CI 0.29-0.62); P CONCLUSIONS: A 10% reduction within marker lesions identifies pNET patients benefiting from sunitinib treatment with implications for maintenance of dose intensity and future trial design